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ftopal
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huggingface-models-embeddings

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train · 241k rows

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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b5_6 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1429 - Mean Iou: 0.6443 - Mean Accuracy: 0.6853 - Overall Accuracy: 0.9669 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3782 - Accuracy Undropoff: 0.9925 - Iou Unlabeled: nan - Iou Dropoff: 0.3223 - Iou Undropoff: 0.9664 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.159 \| 5.0 \| 10 \| 1.0040 \| 0.2283 \| 0.5676 \| 0.6267 \| nan \| 0.5031 \| 0.6321 \| 0.0 \| 0.0644 \| 0.6203 \| \| 0.8345 \| 10.0 \| 20 \| 0.7480 \| 0.3236 \| 0.5320 \| 0.9158 \| nan \| 0.1134 \| 0.9506 \| 0.0 \| 0.0555 \| 0.9154 \| \| 0.5406 \| 15.0 \| 30 \| 0.5477 \| 0.3223 \| 0.5049 \| 0.9513 \| nan \| 0.0179 \| 0.9918 \| 0.0 \| 0.0157 \| 0.9513 \| \| 0.3695 \| 20.0 \| 40 \| 0.4590 \| 0.3215 \| 0.5036 \| 0.9519 \| nan \| 0.0146 \| 0.9926 \| 0.0 \| 0.0125 \| 0.9519 \| \| 0.3053 \| 25.0 \| 50 \| 0.3790 \| 0.3196 \| 0.5001 \| 0.9565 \| nan \| 0.0023 \| 0.9979 \| 0.0 \| 0.0022 \| 0.9565 \| \| 0.2436 \| 30.0 \| 60 \| 0.3303 \| 0.4812 \| 0.5020 \| 0.9568 \| nan \| 0.0059 \| 0.9981 \| nan \| 0.0056 \| 0.9568 \| \| 0.2148 \| 35.0 \| 70 \| 0.2739 \| 0.4794 \| 0.5002 \| 0.9580 \| nan \| 0.0008 \| 0.9996 \| nan \| 0.0008 \| 0.9580 \| \| 0.1983 \| 40.0 \| 80 \| 0.2348 \| 0.5079 \| 0.5284 \| 0.9595 \| nan \| 0.0582 \| 0.9986 \| nan \| 0.0564 \| 0.9594 \| \| 0.1784 \| 45.0 \| 90 \| 0.2178 \| 0.6064 \| 0.6440 \| 0.9631 \| nan \| 0.2960 \| 0.9920 \| nan \| 0.2501 \| 0.9626 \| \| 0.1631 \| 50.0 \| 100 \| 0.1943 \| 0.6223 \| 0.6811 \| 0.9607 \| nan \| 0.3760 \| 0.9861 \| nan \| 0.2846 \| 0.9601 \| \| 0.1468 \| 55.0 \| 110 \| 0.1759 \| 0.6206 \| 0.6731 \| 0.9617 \| nan \| 0.3583 \| 0.9879 \| nan \| 0.2801 \| 0.9611 \| \| 0.1353 \| 60.0 \| 120 \| 0.1657 \| 0.6014 \| 0.6335 \| 0.9639 \| nan \| 0.2731 \| 0.9939 \| nan \| 0.2393 \| 0.9635 \| \| 0.1474 \| 65.0 \| 130 \| 0.1590 \| 0.5943 \| 0.6228 \| 0.9641 \| nan \| 0.2505 \| 0.9951 \| nan \| 0.2249 \| 0.9637 \| \| 0.1172 \| 70.0 \| 140 \| 0.1562 \| 0.6272 \| 0.6662 \| 0.9653 \| nan \| 0.3400 \| 0.9924 \| nan \| 0.2896 \| 0.9648 \| \| 0.1169 \| 75.0 \| 150 \| 0.1538 \| 0.6302 \| 0.6696 \| 0.9656 \| nan \| 0.3467 \| 0.9925 \| nan \| 0.2954 \| 0.9651 \| \| 0.1263 \| 80.0 \| 160 \| 0.1540 \| 0.6372 \| 0.6784 \| 0.9661 \| nan \| 0.3645 \| 0.9922 \| nan \| 0.3089 \| 0.9656 \| \| 0.1028 \| 85.0 \| 170 \| 0.1512 \| 0.6462 \| 0.6948 \| 0.9659 \| nan \| 0.3992 \| 0.9904 \| nan \| 0.3271 \| 0.9653 \| \| 0.1163 \| 90.0 \| 180 \| 0.1493 \| 0.6469 \| 0.6932 \| 0.9663 \| nan \| 0.3953 \| 0.9911 \| nan \| 0.3280 \| 0.9658 \| \| 0.0998 \| 95.0 \| 190 \| 0.1481 \| 0.6457 \| 0.6894 \| 0.9666 \| nan \| 0.3869 \| 0.9918 \| nan \| 0.3253 \| 0.9661 \| \| 0.0997 \| 100.0 \| 200 \| 0.1465 \| 0.6454 \| 0.6893 \| 0.9665 \| nan \| 0.3869 \| 0.9917 \| nan \| 0.3247 \| 0.9660 \| \| 0.0998 \| 105.0 \| 210 \| 0.1473 \| 0.6488 \| 0.6937 \| 0.9668 \| nan \| 0.3958 \| 0.9916 \| nan \| 0.3313 \| 0.9662 \| \| 0.1003 \| 110.0 \| 220 \| 0.1437 \| 0.6401 \| 0.6774 \| 0.9671 \| nan \| 0.3614 \| 0.9934 \| nan \| 0.3136 \| 0.9666 \| \| 0.0932 \| 115.0 \| 230 \| 0.1434 \| 0.6469 \| 0.6898 \| 0.9669 \| nan \| 0.3876 \| 0.9920 \| nan \| 0.3275 \| 0.9664 \| \| 0.0942 \| 120.0 \| 240 \| 0.1429 \| 0.6443 \| 0.6853 \| 0.9669 \| nan \| 0.3782 \| 0.9925 \| nan \| 0.3223 \| 0.9664 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "dropoff-utcustom-train-SF-RGBD-b5_6", "results": []}]}	image-segmentation	sam1120/dropoff-utcustom-train-SF-RGBD-b5_6	[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	2024-02-12T13:25:25+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us	dropoff-utcustom-train-SF-RGBD-b5\_6 ==================================== This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: * Loss: 0.1429 * Mean Iou: 0.6443 * Mean Accuracy: 0.6853 * Overall Accuracy: 0.9669 * Accuracy Unlabeled: nan * Accuracy Dropoff: 0.3782 * Accuracy Undropoff: 0.9925 * Iou Unlabeled: nan * Iou Dropoff: 0.3223 * Iou Undropoff: 0.9664 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.05 * num\_epochs: 120 ### Training results ### Framework versions * Transformers 4.30.2 * Pytorch 2.0.1+cu117 * Datasets 2.13.1 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ 48, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ -0.10591059178113937, 0.036406707018613815, -0.002041781088337302, 0.11596930027008057, 0.1714659482240677, 0.028318241238594055, 0.11641968786716461, 0.11560021340847015, -0.10877452790737152, 0.030912652611732483, 0.10430896282196045, 0.14163099229335785, 0.01629962958395481, 0.09532219916582108, -0.020085278898477554, -0.30744001269340515, -0.02539004385471344, 0.03143095597624779, -0.08681149780750275, 0.12519687414169312, 0.06513086706399918, -0.16188034415245056, 0.09207220375537872, -0.0032083075493574142, -0.22071543335914612, 0.01589730754494667, -0.004114775452762842, -0.03122878633439541, 0.16029687225818634, 0.02357633225619793, 0.11608259379863739, 0.010143724270164967, 0.11489138752222061, -0.20352774858474731, 0.017524294555187225, 0.05539601296186447, -0.0043795364908874035, 0.06756952404975891, 0.06578890234231949, 0.0016288068145513535, 0.15191982686519623, -0.1059940904378891, 0.06725753843784332, 0.0020551886409521103, -0.14449739456176758, -0.21125079691410065, -0.07391216605901718, 0.022725099697709084, 0.07813199609518051, 0.09479884803295135, -0.004944729618728161, 0.11241678893566132, -0.09166383743286133, 0.1124383807182312, 0.26984864473342896, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGBD-b5_7 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1296 - Mean Iou: 0.6242 - Mean Accuracy: 0.6623 - Overall Accuracy: 0.9652 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.3319 - Accuracy Undropoff: 0.9926 - Iou Unlabeled: nan - Iou Dropoff: 0.2838 - Iou Undropoff: 0.9647 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 0.9278 \| 5.0 \| 10 \| 0.8454 \| 0.3197 \| 0.5545 \| 0.8788 \| nan \| 0.2009 \| 0.9082 \| 0.0 \| 0.0807 \| 0.8785 \| \| 0.5551 \| 10.0 \| 20 \| 0.4668 \| 0.3221 \| 0.5042 \| 0.9540 \| nan \| 0.0135 \| 0.9948 \| 0.0 \| 0.0122 \| 0.9540 \| \| 0.3667 \| 15.0 \| 30 \| 0.3354 \| 0.3218 \| 0.5035 \| 0.9570 \| nan \| 0.0088 \| 0.9982 \| 0.0 \| 0.0085 \| 0.9570 \| \| 0.2402 \| 20.0 \| 40 \| 0.2678 \| 0.5985 \| 0.6492 \| 0.9587 \| nan \| 0.3116 \| 0.9868 \| nan \| 0.2388 \| 0.9582 \| \| 0.1562 \| 25.0 \| 50 \| 0.2101 \| 0.6240 \| 0.6719 \| 0.9631 \| nan \| 0.3544 \| 0.9895 \| nan \| 0.2854 \| 0.9625 \| \| 0.1159 \| 30.0 \| 60 \| 0.1704 \| 0.6262 \| 0.6641 \| 0.9654 \| nan \| 0.3353 \| 0.9928 \| nan \| 0.2875 \| 0.9650 \| \| 0.0869 \| 35.0 \| 70 \| 0.1443 \| 0.6380 \| 0.6817 \| 0.9657 \| nan \| 0.3720 \| 0.9915 \| nan \| 0.3108 \| 0.9652 \| \| 0.079 \| 40.0 \| 80 \| 0.1350 \| 0.6072 \| 0.6360 \| 0.9654 \| nan \| 0.2766 \| 0.9953 \| nan \| 0.2494 \| 0.9650 \| \| 0.0647 \| 45.0 \| 90 \| 0.1370 \| 0.5800 \| 0.6031 \| 0.9643 \| nan \| 0.2090 \| 0.9971 \| nan \| 0.1959 \| 0.9640 \| \| 0.0587 \| 50.0 \| 100 \| 0.1336 \| 0.6276 \| 0.6796 \| 0.9628 \| nan \| 0.3707 \| 0.9885 \| nan \| 0.2929 \| 0.9622 \| \| 0.0575 \| 55.0 \| 110 \| 0.1313 \| 0.6189 \| 0.6531 \| 0.9654 \| nan \| 0.3126 \| 0.9937 \| nan \| 0.2729 \| 0.9649 \| \| 0.0527 \| 60.0 \| 120 \| 0.1298 \| 0.6252 \| 0.6655 \| 0.9648 \| nan \| 0.3391 \| 0.9920 \| nan \| 0.2860 \| 0.9643 \| \| 0.0491 \| 65.0 \| 130 \| 0.1313 \| 0.6110 \| 0.6492 \| 0.9635 \| nan \| 0.3063 \| 0.9920 \| nan \| 0.2589 \| 0.9631 \| \| 0.0441 \| 70.0 \| 140 \| 0.1295 \| 0.6103 \| 0.6429 \| 0.9648 \| nan \| 0.2919 \| 0.9939 \| nan \| 0.2562 \| 0.9643 \| \| 0.0426 \| 75.0 \| 150 \| 0.1233 \| 0.6271 \| 0.6633 \| 0.9659 \| nan \| 0.3333 \| 0.9933 \| nan \| 0.2887 \| 0.9654 \| \| 0.0477 \| 80.0 \| 160 \| 0.1286 \| 0.6255 \| 0.6629 \| 0.9655 \| nan \| 0.3328 \| 0.9929 \| nan \| 0.2861 \| 0.9650 \| \| 0.039 \| 85.0 \| 170 \| 0.1265 \| 0.6380 \| 0.6824 \| 0.9656 \| nan \| 0.3735 \| 0.9913 \| nan \| 0.3109 \| 0.9650 \| \| 0.0378 \| 90.0 \| 180 \| 0.1309 \| 0.6185 \| 0.6543 \| 0.9650 \| nan \| 0.3154 \| 0.9932 \| nan \| 0.2725 \| 0.9645 \| \| 0.0362 \| 95.0 \| 190 \| 0.1266 \| 0.6311 \| 0.6715 \| 0.9655 \| nan \| 0.3508 \| 0.9922 \| nan \| 0.2973 \| 0.9650 \| \| 0.0394 \| 100.0 \| 200 \| 0.1307 \| 0.6274 \| 0.6635 \| 0.9659 \| nan \| 0.3337 \| 0.9934 \| nan \| 0.2894 \| 0.9655 \| \| 0.0362 \| 105.0 \| 210 \| 0.1271 \| 0.6366 \| 0.6789 \| 0.9658 \| nan \| 0.3661 \| 0.9918 \| nan \| 0.3080 \| 0.9653 \| \| 0.0361 \| 110.0 \| 220 \| 0.1274 \| 0.6317 \| 0.6736 \| 0.9653 \| nan \| 0.3554 \| 0.9918 \| nan \| 0.2987 \| 0.9648 \| \| 0.0353 \| 115.0 \| 230 \| 0.1290 \| 0.6216 \| 0.6579 \| 0.9652 \| nan \| 0.3228 \| 0.9931 \| nan \| 0.2784 \| 0.9647 \| \| 0.0344 \| 120.0 \| 240 \| 0.1296 \| 0.6242 \| 0.6623 \| 0.9652 \| nan \| 0.3319 \| 0.9926 \| nan \| 0.2838 \| 0.9647 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "dropoff-utcustom-train-SF-RGBD-b5_7", "results": []}]}	image-segmentation	sam1120/dropoff-utcustom-train-SF-RGBD-b5_7	[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	2024-02-12T13:25:26+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us	dropoff-utcustom-train-SF-RGBD-b5\_7 ==================================== This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: * Loss: 0.1296 * Mean Iou: 0.6242 * Mean Accuracy: 0.6623 * Overall Accuracy: 0.9652 * Accuracy Unlabeled: nan * Accuracy Dropoff: 0.3319 * Accuracy Undropoff: 0.9926 * Iou Unlabeled: nan * Iou Dropoff: 0.2838 * Iou Undropoff: 0.9647 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.05 * num\_epochs: 120 ### Training results ### Framework versions * Transformers 4.30.2 * Pytorch 2.0.1+cu117 * Datasets 2.13.1 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ 48, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ -0.10649233311414719, 0.03541902080178261, -0.002008110284805298, 0.11587068438529968, 0.17249611020088196, 0.02851674146950245, 0.11544682830572128, 0.11457392573356628, -0.10972921550273895, 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-base-generator-rank2 This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2155 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 8.206 \| 1.0 \| 179 \| 3.8146 \| \| 3.5779 \| 2.0 \| 358 \| 3.2736 \| \| 3.3568 \| 3.0 \| 537 \| 3.2155 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/electra-base-generator", "model-index": [{"name": "electra-base-generator-rank2", "results": []}]}	null	alitolga/electra-base-generator-rank2	[ "safetensors", "generated_from_trainer", "base_model:google/electra-base-generator", "license:apache-2.0", "region:us" ]	2024-02-12T13:25:46+00:00	[]	[]	TAGS #safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us	electra-base-generator-rank2 ============================ This model is a fine-tuned version of google/electra-base-generator on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.2155 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu118 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 40, 98, 4, 33 ]	[ "passage: TAGS\n#safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.0986127182841301, 0.018834460526704788, -0.0008125067106448114, 0.10979532450437546, 0.18232838809490204, 0.014075469225645065, 0.14945824444293976, 0.05513402447104454, -0.11618620902299881, 0.027743428945541382, 0.11155061423778534, 0.12338999658823013, -0.0060639819130301476, 0.12559986114501953, -0.0439465306699276, -0.19480036199092865, 0.025722401216626167, 0.0023020326625555754, -0.06931312382221222, 0.12410648912191391, 0.10072977840900421, -0.1471579670906067, 0.0789017304778099, -0.0045234463177621365, -0.218098446726799, 0.03776150941848755, 0.037302516400814056, -0.06900284439325333, 0.1463816612958908, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 1.17.0 - Tokenizers 0.14.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-squad", "results": []}]}	question-answering	Nattipon/bert-finetuned-squad	[ "transformers", "pytorch", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "base_model:bert-base-cased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-12T13:26:25+00:00	[]	[]	TAGS #transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us	# bert-finetuned-squad This model is a fine-tuned version of bert-base-cased on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 1.17.0 - Tokenizers 0.14.1	[ "# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.34.0\n- Pytorch 2.1.0+cu121\n- Datasets 1.17.0\n- Tokenizers 0.14.1" ]	[ "TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us \n", "# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.34.0\n- Pytorch 2.1.0+cu121\n- Datasets 1.17.0\n- Tokenizers 0.14.1" ]	[ 61, 34, 6, 12, 8, 3, 90, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #bert #question-answering #generated_from_trainer #dataset-squad #base_model-bert-base-cased #license-apache-2.0 #endpoints_compatible #region-us \n# bert-finetuned-squad\n\nThis model is a fine-tuned version of bert-base-cased on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1### Training results### Framework versions\n\n- Transformers 4.34.0\n- Pytorch 2.1.0+cu121\n- Datasets 1.17.0\n- Tokenizers 0.14.1" ]	[ -0.08765111863613129, 0.05973196029663086, -0.0011512723285704851, 0.07199656218290329, 0.17598719894886017, 0.014386482536792755, 0.10761704295873642, 0.10356049239635468, -0.12827421724796295, 0.03680035099387169, 0.061929669231176376, 0.05283472687005997, 0.025398096069693565, 0.09513931721448898, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-classification	Camillahannesbo/Camillas_bert_model	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T13:26:44+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 46, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06817419826984406, 0.1699906885623932, -0.003845146857202053, 0.018365124240517616, 0.11478200554847717, 0.00763329304754734, 0.07986336201429367, 0.10738246887922287, -0.0269484706223011, 0.1267213374376297, 0.03862300142645836, 0.1017010435461998, 0.11044707149267197, 0.18616852164268494, 0.002953584771603346, -0.2117370218038559, 0.062315817922353745, -0.11355884373188019, 0.01421935111284256, 0.12174045294523239, 0.14285145699977875, -0.10472407191991806, 0.07340893894433975, -0.03533155843615532, -0.019184017553925514, -0.029508300125598907, -0.06138347089290619, -0.062117863446474075, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-classification	MikkelONielsen/bert_classification	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T13:27:01+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 46, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06817419826984406, 0.1699906885623932, -0.003845146857202053, 0.018365124240517616, 0.11478200554847717, 0.00763329304754734, 0.07986336201429367, 0.10738246887922287, -0.0269484706223011, 0.1267213374376297, 0.03862300142645836, 0.1017010435461998, 0.11044707149267197, 0.18616852164268494, 0.002953584771603346, -0.2117370218038559, 0.062315817922353745, -0.11355884373188019, 0.01421935111284256, 0.12174045294523239, 0.14285145699977875, -0.10472407191991806, 0.07340893894433975, -0.03533155843615532, -0.019184017553925514, -0.029508300125598907, -0.06138347089290619, -0.062117863446474075, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-classification	HamidBekam/bert_classification_v1	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T13:27:41+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 46, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06817419826984406, 0.1699906885623932, -0.003845146857202053, 0.018365124240517616, 0.11478200554847717, 0.00763329304754734, 0.07986336201429367, 0.10738246887922287, -0.0269484706223011, 0.1267213374376297, 0.03862300142645836, 0.1017010435461998, 0.11044707149267197, 0.18616852164268494, 0.002953584771603346, -0.2117370218038559, 0.062315817922353745, -0.11355884373188019, 0.01421935111284256, 0.12174045294523239, 0.14285145699977875, -0.10472407191991806, 0.07340893894433975, -0.03533155843615532, -0.019184017553925514, -0.029508300125598907, -0.06138347089290619, -0.062117863446474075, 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null	null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: microsoft/phi-1_5 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: garage-bAInd/Open-Platypus type: alpaca dataset_prepared_path: val_set_size: 0.05 output_dir: ./phi-sft-out sequence_len: 2048 sample_packing: true pad_to_sequence_len: true adapter: qlora lora_model_dir: lora_r: 64 lora_alpha: 32 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 1 micro_batch_size: 2 num_epochs: 4 optimizer: adamw_torch adam_beta2: 0.95 adam_epsilon: 0.00001 max_grad_norm: 1.0 lr_scheduler: cosine learning_rate: 0.000003 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: True early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 100 evals_per_epoch: 4 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.1 fsdp: fsdp_config: resize_token_embeddings_to_32x: true special_tokens: pad_token: "<\|endoftext\|>" ``` </details><br> # phi-sft-out This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2548 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0668 \| 0.0 \| 1 \| 1.2826 \| \| 0.9408 \| 0.25 \| 580 \| 1.2613 \| \| 1.2121 \| 0.5 \| 1160 \| 1.2559 \| \| 0.9644 \| 0.75 \| 1740 \| 1.2562 \| \| 0.9582 \| 1.0 \| 2320 \| 1.2556 \| \| 1.0009 \| 1.23 \| 2900 \| 1.2559 \| \| 0.7816 \| 1.48 \| 3480 \| 1.2556 \| \| 0.9843 \| 1.73 \| 4060 \| 1.2552 \| \| 0.8877 \| 1.98 \| 4640 \| 1.2559 \| \| 0.8498 \| 2.21 \| 5220 \| 1.2554 \| \| 0.9163 \| 2.46 \| 5800 \| 1.2550 \| \| 1.0539 \| 2.71 \| 6380 \| 1.2545 \| \| 0.9533 \| 2.96 \| 6960 \| 1.2547 \| \| 0.6969 \| 3.19 \| 7540 \| 1.2547 \| \| 0.6204 \| 3.44 \| 8120 \| 1.2547 \| \| 0.891 \| 3.69 \| 8700 \| 1.2548 \| ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.17.0 - Tokenizers 0.15.0	{"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-1_5", "model-index": [{"name": "phi-sft-out", "results": []}]}	null	Deadwalker0/phitune	[ "peft", "tensorboard", "safetensors", "phi", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-1_5", "license:mit", "4-bit", "region:us" ]	2024-02-12T13:30:14+00:00	[]	[]	TAGS #peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #4-bit #region-us	<img src="URL alt="Built with Axolotl" width="200" height="32"/> See axolotl config axolotl version: '0.4.0' phi-sft-out =========== This model is a fine-tuned version of microsoft/phi-1\_5 on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.2548 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 3e-06 * train\_batch\_size: 2 * eval\_batch\_size: 2 * seed: 42 * optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_steps: 100 * num\_epochs: 4 ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.38.0.dev0 * Pytorch 2.0.1+cu118 * Datasets 2.17.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 4", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #4-bit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 4", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.0" ]	[ 51, 117, 4, 44 ]	[ "passage: TAGS\n#peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #4-bit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 4### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.17.0\n* Tokenizers 0.15.0" ]	[ -0.13421018421649933, 0.05786968395113945, -0.001539409626275301, 0.1157117709517479, 0.12771134078502655, 0.016527919098734856, 0.12740403413772583, 0.0944160521030426, -0.07903594523668289, 0.09833302348852158, 0.11483386904001236, 0.13475942611694336, 0.04958562180399895, 0.1875339150428772, -0.045759327709674835, -0.2119860053062439, 0.01288656983524561, -0.013863325119018555, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-classification	Annikaijak/bert_classification	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T13:31:04+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 46, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06817419826984406, 0.1699906885623932, -0.003845146857202053, 0.018365124240517616, 0.11478200554847717, 0.00763329304754734, 0.07986336201429367, 0.10738246887922287, -0.0269484706223011, 0.1267213374376297, 0.03862300142645836, 0.1017010435461998, 0.11044707149267197, 0.18616852164268494, 0.002953584771603346, -0.2117370218038559, 0.062315817922353745, -0.11355884373188019, 0.01421935111284256, 0.12174045294523239, 0.14285145699977875, -0.10472407191991806, 0.07340893894433975, -0.03533155843615532, -0.019184017553925514, -0.029508300125598907, -0.06138347089290619, -0.062117863446474075, 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null	null	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "214.81 +/- 68.51", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	hweemiin/ppo-LunarLander-v2	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-12T13:31:13+00:00	[]	[]	TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code	[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 39, 41, 17 ]	[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 0.03942384943366051, 0.04900386184453964, -0.005304091144353151, 0.026427261531352997, 0.107408307492733, -0.026511888951063156, 0.11188238859176636, 0.0814051404595375, 0.10722193866968155, 0.04762078449130058, 0.08338645845651627, 0.06030960753560066, 0.05080918222665787, 0.2571701407432556, 0.04754156619310379, -0.22987541556358337, 0.036159250885248184, -0.04869936779141426, 0.12395193427801132, 0.07178173214197159, -0.0038484656251966953, -0.06485428661108017, 0.020415637642145157, -0.013290755450725555, 0.05367108806967735, 0.04282612353563309, -0.01716216839849949, -0.08207534998655319, 0.07169748842716217, -0.06345846503973007, 0.06986866891384125, 0.07677983492612839, 0.13218913972377777, -0.17832116782665253, 0.029566360637545586, 0.02571309357881546, 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null	null	ml-agents	# ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: hugo-massonnat/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀	{"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]}	reinforcement-learning	hugo-massonnat/ppo-Huggy	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	2024-02-12T13:35:03+00:00	[]	[]	TAGS #ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us	# ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the Unity ML-Agents Library. ## Usage (with ML-Agents) The Documentation: URL We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your browser: URL - A longer tutorial to understand how works ML-Agents: URL ### Resume the training ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to URL 2. Step 1: Find your model_id: hugo-massonnat/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play	[ "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A longer tutorial to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: hugo-massonnat/ppo-Huggy\n 3. Step 2: Select your .nn /.onnx file\n 4. Click on Watch the agent play" ]	[ "TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n", "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A longer tutorial to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: hugo-massonnat/ppo-Huggy\n 3. Step 2: Select your .nn /.onnx file\n 4. Click on Watch the agent play" ]	[ 44, 202 ]	[ "passage: TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A longer tutorial to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: hugo-massonnat/ppo-Huggy\n 3. Step 2: Select your .nn /.onnx file\n 4. 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-base-generator-rank4 This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2603 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 8.3543 \| 1.0 \| 179 \| 3.9048 \| \| 3.7115 \| 2.0 \| 358 \| 3.3385 \| \| 3.4042 \| 3.0 \| 537 \| 3.2603 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/electra-base-generator", "model-index": [{"name": "electra-base-generator-rank4", "results": []}]}	null	alitolga/electra-base-generator-rank4	[ "safetensors", "generated_from_trainer", "base_model:google/electra-base-generator", "license:apache-2.0", "region:us" ]	2024-02-12T13:35:29+00:00	[]	[]	TAGS #safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us	electra-base-generator-rank4 ============================ This model is a fine-tuned version of google/electra-base-generator on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.2603 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu118 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 40, 98, 4, 33 ]	[ "passage: TAGS\n#safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.0986127182841301, 0.018834460526704788, -0.0008125067106448114, 0.10979532450437546, 0.18232838809490204, 0.014075469225645065, 0.14945824444293976, 0.05513402447104454, -0.11618620902299881, 0.027743428945541382, 0.11155061423778534, 0.12338999658823013, -0.0060639819130301476, 0.12559986114501953, -0.0439465306699276, -0.19480036199092865, 0.025722401216626167, 0.0023020326625555754, -0.06931312382221222, 0.12410648912191391, 0.10072977840900421, -0.1471579670906067, 0.0789017304778099, -0.0045234463177621365, -0.218098446726799, 0.03776150941848755, 0.037302516400814056, -0.06900284439325333, 0.1463816612958908, 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null	null	null	# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction	{"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-PixelCopter_64", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "33.00 +/- 41.93", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	ramsi-k/Reinforce-PixelCopter_64	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	2024-02-12T13:36:09+00:00	[]	[]	TAGS #Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us	# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL	[ "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ "TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ 41, 58 ]	[ "passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ 0.0073175891302526, -0.2259262204170227, -0.0017347558168694377, 0.05054566636681557, 0.0658537745475769, -0.055378563702106476, 0.1412602812051773, 0.05916554853320122, -0.04990595206618309, 0.059261854737997055, 0.14166708290576935, 0.03996060788631439, 0.022112762555480003, 0.1513713151216507, 0.09764605015516281, -0.2469022423028946, 0.07438477873802185, 0.01641594059765339, 0.008152224123477936, 0.09583204984664917, 0.060265738517045975, -0.1405058205127716, 0.037032704800367355, -0.01332044042646885, -0.13650871813297272, 0.0010478810872882605, -0.021802188828587532, -0.03625129908323288, 0.15681709349155426, 0.006844013463705778, 0.09602472931146622, -0.001560068572871387, 0.06475798785686493, -0.12438877671957016, 0.05466329678893089, 0.06455880403518677, -0.06293967366218567, 0.058029334992170334, -0.057374246418476105, 0.11959903687238693, 0.04641333222389221, -0.01578129455447197, 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null	null	null	Quantized GGUF of my meme-merge [Moza-7B-v1.0](https://huggingface.co/kidyu/Moza-7B-v1.0/)	{"tags": ["mergekit", "merge"], "base_model": "kidyu/Moza-7B-v1.0", "inference": false, "quantized_by": "kidyu"}	null	kidyu/Moza-7B-v1.0-GGUF	[ "gguf", "mergekit", "merge", "base_model:kidyu/Moza-7B-v1.0", "region:us" ]	2024-02-12T13:37:03+00:00	[]	[]	TAGS #gguf #mergekit #merge #base_model-kidyu/Moza-7B-v1.0 #region-us	Quantized GGUF of my meme-merge Moza-7B-v1.0	[]	[ "TAGS\n#gguf #mergekit #merge #base_model-kidyu/Moza-7B-v1.0 #region-us \n" ]	[ 31 ]	[ "passage: TAGS\n#gguf #mergekit #merge #base_model-kidyu/Moza-7B-v1.0 #region-us \n" ]	[ -0.02254718914628029, -0.16754774749279022, -0.005084953270852566, -0.07240639626979828, 0.0888434648513794, 0.06922636181116104, 0.11460037529468536, 0.07850335538387299, 0.13837116956710815, -0.011878183111548424, 0.04441649094223976, 0.03570666164159775, 0.043223071843385696, 0.2019771784543991, -0.01813402585685253, 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-base-generator-rank8 This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2562 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 8.2296 \| 1.0 \| 179 \| 3.8171 \| \| 3.6406 \| 2.0 \| 358 \| 3.3218 \| \| 3.395 \| 3.0 \| 537 \| 3.2562 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/electra-base-generator", "model-index": [{"name": "electra-base-generator-rank8", "results": []}]}	null	alitolga/electra-base-generator-rank8	[ "safetensors", "generated_from_trainer", "base_model:google/electra-base-generator", "license:apache-2.0", "region:us" ]	2024-02-12T13:41:17+00:00	[]	[]	TAGS #safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us	electra-base-generator-rank8 ============================ This model is a fine-tuned version of google/electra-base-generator on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.2562 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu118 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 40, 98, 4, 33 ]	[ "passage: TAGS\n#safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.0986127182841301, 0.018834460526704788, -0.0008125067106448114, 0.10979532450437546, 0.18232838809490204, 0.014075469225645065, 0.14945824444293976, 0.05513402447104454, -0.11618620902299881, 0.027743428945541382, 0.11155061423778534, 0.12338999658823013, -0.0060639819130301476, 0.12559986114501953, -0.0439465306699276, -0.19480036199092865, 0.025722401216626167, 0.0023020326625555754, -0.06931312382221222, 0.12410648912191391, 0.10072977840900421, -0.1471579670906067, 0.0789017304778099, -0.0045234463177621365, -0.218098446726799, 0.03776150941848755, 0.037302516400814056, -0.06900284439325333, 0.1463816612958908, 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null	null	transformers	# Model Card for Spam Detection Model This model card outlines a spam detection model trained on the SetFit/enron_spam and Deysi/spam-detection-dataset from Hugging Face. The model aims to classify emails or text messages into spam or not spam (ham) with high accuracy, leveraging the BERT architecture for natural language processing tasks. ## Model Details ### Model Description This spam detection model was developed to identify and filter out unwanted or harmful emails and messages automatically. It was fine-tuned on two significant datasets featuring real-world spam examples, demonstrating a high level of accuracy in distinguishing between spam and ham. - Developed by: AI and cybersecurity researchers. - Model type: BERT for Sequence Classification. - Language(s) (NLP): English. - License: Unknown. - Finetuned from model: `bert-base-uncased`. ## Uses ### Direct Use The model is intended for direct use in email filtering systems, cybersecurity applications, and any platform needing to identify spam content within text data. ### Out-of-Scope Use The model is not designed for identifying phishing attempts, detecting malware within attachments, or other security threats beyond the scope of text-based spam content. It may not perform well on texts significantly different from those found in the training datasets, such as messages in languages other than English or texts from domains vastly different from emails. ## Bias, Risks, and Limitations The model's performance is highly dependent on the nature and diversity of the training data. There might be biases in the datasets that could affect the model's predictions, particularly for edge cases or underrepresented categories of spam. Users should be aware of these limitations and consider additional layers of security and content moderation according to their specific needs. ## How to Get Started with the Model To get started with the model, load the pretrained model and tokenizer from the specified directory and use them to preprocess your text data. The model can then be applied to classify texts as spam or not spam. ## Training Details ### Training Data The model was trained on the SetFit/enron_spam and Deysi/spam-detection-dataset, which include a variety of spam and ham examples collected from real-world email data. ### Training Procedure The model was fine-tuned for 3 epochs, achieving a final training loss of 0.0239 and an accuracy of 99.55% on the evaluation set. Training was conducted using a batch size of 8, with a learning rate of 2e-5. ## Evaluation ### Testing Data, Factors & Metrics The evaluation was performed on a test split from the datasets, focusing on the accuracy metric to assess the model's performance. ### Results The model achieved an evaluation accuracy of 99.55% with an evaluation loss of 0.0448, indicating excellent performance in distinguishing between spam and ham messages. ## Environmental Impact Given the high accuracy and low loss, this model presents a robust solution for spam detection tasks. However, users are encouraged to assess the model's applicability to their specific use cases, considering potential biases and the model's limitations.	{"license": "unknown", "datasets": ["SetFit/enron_spam", "Deysi/spam-detection-dataset"], "metrics": ["accuracy"]}	text-classification	cybert79/spamai	[ "transformers", "safetensors", "bert", "text-classification", "dataset:SetFit/enron_spam", "dataset:Deysi/spam-detection-dataset", "license:unknown", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T13:42:50+00:00	[]	[]	TAGS #transformers #safetensors #bert #text-classification #dataset-SetFit/enron_spam #dataset-Deysi/spam-detection-dataset #license-unknown #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Spam Detection Model This model card outlines a spam detection model trained on the SetFit/enron_spam and Deysi/spam-detection-dataset from Hugging Face. The model aims to classify emails or text messages into spam or not spam (ham) with high accuracy, leveraging the BERT architecture for natural language processing tasks. ## Model Details ### Model Description This spam detection model was developed to identify and filter out unwanted or harmful emails and messages automatically. It was fine-tuned on two significant datasets featuring real-world spam examples, demonstrating a high level of accuracy in distinguishing between spam and ham. - Developed by: AI and cybersecurity researchers. - Model type: BERT for Sequence Classification. - Language(s) (NLP): English. - License: Unknown. - Finetuned from model: 'bert-base-uncased'. ## Uses ### Direct Use The model is intended for direct use in email filtering systems, cybersecurity applications, and any platform needing to identify spam content within text data. ### Out-of-Scope Use The model is not designed for identifying phishing attempts, detecting malware within attachments, or other security threats beyond the scope of text-based spam content. It may not perform well on texts significantly different from those found in the training datasets, such as messages in languages other than English or texts from domains vastly different from emails. ## Bias, Risks, and Limitations The model's performance is highly dependent on the nature and diversity of the training data. There might be biases in the datasets that could affect the model's predictions, particularly for edge cases or underrepresented categories of spam. Users should be aware of these limitations and consider additional layers of security and content moderation according to their specific needs. ## How to Get Started with the Model To get started with the model, load the pretrained model and tokenizer from the specified directory and use them to preprocess your text data. The model can then be applied to classify texts as spam or not spam. ## Training Details ### Training Data The model was trained on the SetFit/enron_spam and Deysi/spam-detection-dataset, which include a variety of spam and ham examples collected from real-world email data. ### Training Procedure The model was fine-tuned for 3 epochs, achieving a final training loss of 0.0239 and an accuracy of 99.55% on the evaluation set. Training was conducted using a batch size of 8, with a learning rate of 2e-5. ## Evaluation ### Testing Data, Factors & Metrics The evaluation was performed on a test split from the datasets, focusing on the accuracy metric to assess the model's performance. ### Results The model achieved an evaluation accuracy of 99.55% with an evaluation loss of 0.0448, indicating excellent performance in distinguishing between spam and ham messages. ## Environmental Impact Given the high accuracy and low loss, this model presents a robust solution for spam detection tasks. However, users are encouraged to assess the model's applicability to their specific use cases, considering potential biases and the model's limitations.	[ "# Model Card for Spam Detection Model\n\nThis model card outlines a spam detection model trained on the SetFit/enron_spam and Deysi/spam-detection-dataset from Hugging Face. The model aims to classify emails or text messages into spam or not spam (ham) with high accuracy, leveraging the BERT architecture for natural language processing tasks.", "## Model Details", "### Model Description\n\nThis spam detection model was developed to identify and filter out unwanted or harmful emails and messages automatically. It was fine-tuned on two significant datasets featuring real-world spam examples, demonstrating a high level of accuracy in distinguishing between spam and ham.\n\n- Developed by: AI and cybersecurity researchers.\n- Model type: BERT for Sequence Classification.\n- Language(s) (NLP): English.\n- License: Unknown.\n- Finetuned from model: 'bert-base-uncased'.", "## Uses", "### Direct Use\n\nThe model is intended for direct use in email filtering systems, cybersecurity applications, and any platform needing to identify spam content within text data.", "### Out-of-Scope Use\n\nThe model is not designed for identifying phishing attempts, detecting malware within attachments, or other security threats beyond the scope of text-based spam content. It may not perform well on texts significantly different from those found in the training datasets, such as messages in languages other than English or texts from domains vastly different from emails.", "## Bias, Risks, and Limitations\n\nThe model's performance is highly dependent on the nature and diversity of the training data. There might be biases in the datasets that could affect the model's predictions, particularly for edge cases or underrepresented categories of spam. Users should be aware of these limitations and consider additional layers of security and content moderation according to their specific needs.", "## How to Get Started with the Model\n\nTo get started with the model, load the pretrained model and tokenizer from the specified directory and use them to preprocess your text data. The model can then be applied to classify texts as spam or not spam.", "## Training Details", "### Training Data\n\nThe model was trained on the SetFit/enron_spam and Deysi/spam-detection-dataset, which include a variety of spam and ham examples collected from real-world email data.", "### Training Procedure\n\nThe model was fine-tuned for 3 epochs, achieving a final training loss of 0.0239 and an accuracy of 99.55% on the evaluation set. Training was conducted using a batch size of 8, with a learning rate of 2e-5.", "## Evaluation", "### Testing Data, Factors & Metrics\n\nThe evaluation was performed on a test split from the datasets, focusing on the accuracy metric to assess the model's performance.", "### Results\n\nThe model achieved an evaluation accuracy of 99.55% with an evaluation loss of 0.0448, indicating excellent performance in distinguishing between spam and ham messages.", "## Environmental Impact\n\nGiven the high accuracy and low loss, this model presents a robust solution for spam detection tasks. However, users are encouraged to assess the model's applicability to their specific use cases, considering potential biases and the model's limitations." ]	[ "TAGS\n#transformers #safetensors #bert #text-classification #dataset-SetFit/enron_spam #dataset-Deysi/spam-detection-dataset #license-unknown #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Spam Detection Model\n\nThis model card outlines a spam detection model trained on the SetFit/enron_spam and Deysi/spam-detection-dataset from Hugging Face. The model aims to classify emails or text messages into spam or not spam (ham) with high accuracy, leveraging the BERT architecture for natural language processing tasks.", "## Model Details", "### Model Description\n\nThis spam detection model was developed to identify and filter out unwanted or harmful emails and messages automatically. It was fine-tuned on two significant datasets featuring real-world spam examples, demonstrating a high level of accuracy in distinguishing between spam and ham.\n\n- Developed by: AI and cybersecurity researchers.\n- Model type: BERT for Sequence Classification.\n- Language(s) (NLP): English.\n- License: Unknown.\n- Finetuned from model: 'bert-base-uncased'.", "## Uses", "### Direct Use\n\nThe model is intended for direct use in email filtering systems, cybersecurity applications, and any platform needing to identify spam content within text data.", "### Out-of-Scope Use\n\nThe model is not designed for identifying phishing attempts, detecting malware within attachments, or other security threats beyond the scope of text-based spam content. It may not perform well on texts significantly different from those found in the training datasets, such as messages in languages other than English or texts from domains vastly different from emails.", "## Bias, Risks, and Limitations\n\nThe model's performance is highly dependent on the nature and diversity of the training data. There might be biases in the datasets that could affect the model's predictions, particularly for edge cases or underrepresented categories of spam. Users should be aware of these limitations and consider additional layers of security and content moderation according to their specific needs.", "## How to Get Started with the Model\n\nTo get started with the model, load the pretrained model and tokenizer from the specified directory and use them to preprocess your text data. The model can then be applied to classify texts as spam or not spam.", "## Training Details", "### Training Data\n\nThe model was trained on the SetFit/enron_spam and Deysi/spam-detection-dataset, which include a variety of spam and ham examples collected from real-world email data.", "### Training Procedure\n\nThe model was fine-tuned for 3 epochs, achieving a final training loss of 0.0239 and an accuracy of 99.55% on the evaluation set. Training was conducted using a batch size of 8, with a learning rate of 2e-5.", "## Evaluation", "### Testing Data, Factors & Metrics\n\nThe evaluation was performed on a test split from the datasets, focusing on the accuracy metric to assess the model's performance.", "### Results\n\nThe model achieved an evaluation accuracy of 99.55% with an evaluation loss of 0.0448, indicating excellent performance in distinguishing between spam and ham messages.", "## Environmental Impact\n\nGiven the high accuracy and low loss, this model presents a robust solution for spam detection tasks. However, users are encouraged to assess the model's applicability to their specific use cases, considering potential biases and the model's limitations." ]	[ 72, 88, 3, 126, 3, 34, 88, 90, 59, 3, 51, 65, 3, 44, 37, 61 ]	[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #dataset-SetFit/enron_spam #dataset-Deysi/spam-detection-dataset #license-unknown #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Spam Detection Model\n\nThis model card outlines a spam detection model trained on the SetFit/enron_spam and Deysi/spam-detection-dataset from Hugging Face. The model aims to classify emails or text messages into spam or not spam (ham) with high accuracy, leveraging the BERT architecture for natural language processing tasks.## Model Details### Model Description\n\nThis spam detection model was developed to identify and filter out unwanted or harmful emails and messages automatically. It was fine-tuned on two significant datasets featuring real-world spam examples, demonstrating a high level of accuracy in distinguishing between spam and ham.\n\n- Developed by: AI and cybersecurity researchers.\n- Model type: BERT for Sequence Classification.\n- Language(s) (NLP): English.\n- License: Unknown.\n- Finetuned from model: 'bert-base-uncased'.## Uses### Direct Use\n\nThe model is intended for direct use in email filtering systems, cybersecurity applications, and any platform needing to identify spam content within text data.### Out-of-Scope Use\n\nThe model is not designed for identifying phishing attempts, detecting malware within attachments, or other security threats beyond the scope of text-based spam content. It may not perform well on texts significantly different from those found in the training datasets, such as messages in languages other than English or texts from domains vastly different from emails.## Bias, Risks, and Limitations\n\nThe model's performance is highly dependent on the nature and diversity of the training data. There might be biases in the datasets that could affect the model's predictions, particularly for edge cases or underrepresented categories of spam. Users should be aware of these limitations and consider additional layers of security and content moderation according to their specific needs." ]	[ -0.013309000991284847, 0.08167235553264618, -0.002852531149983406, 0.046919774264097214, 0.07813996076583862, -0.03344402834773064, 0.0982201024889946, -0.0478372760117054, -0.005766246933490038, 0.08970015496015549, -0.04609671235084534, -0.0869993194937706, 0.04980992153286934, 0.11423540860414505, -0.007050801999866962, -0.25134482979774475, 0.08684098720550537, -0.18629911541938782, 0.0657498836517334, 0.07653478533029556, 0.09888561069965363, -0.1368360072374344, 0.07310072332620621, -0.02347397431731224, -0.0012952798279002309, -0.01624436117708683, -0.029785895720124245, -0.020103713497519493, 0.05804416164755821, 0.03244710713624954, 0.18082410097122192, -0.058864761143922806, 0.130669504404068, -0.13836555182933807, 0.014114396646618843, 0.07590523362159729, 0.03200022503733635, 0.05315412953495979, 0.07608737051486969, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	feature-extraction	tommymarto/LernnaviBERT_baseline_students_answers_mlp	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T13:49:23+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 39, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.052746038883924484, 0.20255789160728455, -0.0045078229159116745, 0.0248473659157753, 0.10497838258743286, 0.00675728265196085, 0.06521498411893845, 0.11486967653036118, -0.0023755673319101334, 0.12028469145298004, 0.027631845325231552, 0.08119397610425949, 0.12110675126314163, 0.15393014252185822, 0.005160121712833643, -0.24253977835178375, 0.05344875901937485, -0.09366832673549652, 0.004077504388988018, 0.11452110856771469, 0.1343945860862732, -0.10780399292707443, 0.08976872265338898, -0.00683097867295146, -0.01712046191096306, -0.015751034021377563, -0.07134060561656952, -0.06668227165937424, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilhubert-finetuned-gtzan This model is a fine-tuned version of [arshsin/distilhubert-finetuned-gtzan](https://huggingface.co/arshsin/distilhubert-finetuned-gtzan) on the GTZAN dataset. It achieves the following results on the evaluation set: - Loss: 1.6457 - Accuracy: 0.84 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0001 \| 0.99 \| 56 \| 1.4113 \| 0.84 \| \| 0.0001 \| 2.0 \| 113 \| 1.4248 \| 0.84 \| \| 0.0001 \| 2.99 \| 169 \| 1.4818 \| 0.83 \| \| 0.0001 \| 4.0 \| 226 \| 1.5228 \| 0.83 \| \| 0.0001 \| 4.99 \| 282 \| 1.5067 \| 0.84 \| \| 0.0032 \| 6.0 \| 339 \| 1.5205 \| 0.84 \| \| 0.0 \| 6.99 \| 395 \| 1.5488 \| 0.84 \| \| 0.0 \| 8.0 \| 452 \| 1.5890 \| 0.84 \| \| 0.0 \| 8.99 \| 508 \| 1.6020 \| 0.83 \| \| 0.0117 \| 10.0 \| 565 \| 1.5945 \| 0.84 \| \| 0.0 \| 10.99 \| 621 \| 1.6145 \| 0.84 \| \| 0.0 \| 12.0 \| 678 \| 1.6370 \| 0.83 \| \| 0.0 \| 12.99 \| 734 \| 1.6396 \| 0.84 \| \| 0.0 \| 14.0 \| 791 \| 1.6458 \| 0.83 \| \| 0.0 \| 14.87 \| 840 \| 1.6457 \| 0.84 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.2 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["marsyas/gtzan"], "metrics": ["accuracy"], "base_model": "arshsin/distilhubert-finetuned-gtzan", "model-index": [{"name": "distilhubert-finetuned-gtzan", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "GTZAN", "type": "marsyas/gtzan", "config": "all", "split": "train", "args": "all"}, "metrics": [{"type": "accuracy", "value": 0.84, "name": "Accuracy"}]}]}]}	audio-classification	arshsin/distilhubert-finetuned-gtzan	[ "transformers", "tensorboard", "safetensors", "hubert", "audio-classification", "generated_from_trainer", "dataset:marsyas/gtzan", "base_model:arshsin/distilhubert-finetuned-gtzan", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2024-02-12T13:52:16+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #base_model-arshsin/distilhubert-finetuned-gtzan #license-apache-2.0 #model-index #endpoints_compatible #region-us	distilhubert-finetuned-gtzan ============================ This model is a fine-tuned version of arshsin/distilhubert-finetuned-gtzan on the GTZAN dataset. It achieves the following results on the evaluation set: * Loss: 1.6457 * Accuracy: 0.84 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-06 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 15 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.2 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #base_model-arshsin/distilhubert-finetuned-gtzan #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 84, 159, 4, 30 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #hubert #audio-classification #generated_from_trainer #dataset-marsyas/gtzan #base_model-arshsin/distilhubert-finetuned-gtzan #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.2\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.11074284464120865, 0.14068494737148285, -0.003839588025584817, 0.0664944052696228, 0.09032809734344482, 0.024336880072951317, 0.11126602441072464, 0.13209812343120575, -0.04865409806370735, 0.12843415141105652, 0.10309731960296631, 0.0964481383562088, 0.05282476544380188, 0.13579882681369781, -0.019456584006547928, -0.29692938923835754, 0.029158972203731537, -0.011313341557979584, -0.1254175454378128, 0.10371482372283936, 0.07572777569293976, -0.10205145925283432, 0.052409350872039795, 0.009300141595304012, -0.09577406942844391, -0.01697024516761303, -0.028144031763076782, -0.05912663787603378, 0.07716846466064453, 0.025127572938799858, 0.059356044977903366, 0.03834094479680061, 0.07589273154735565, -0.26158130168914795, 0.011914835311472416, 0.05180254951119423, 0.015766562893986702, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	feature-extraction	tommymarto/LernnaviBERT_baseline_students_answers_384_lstm_seq_len_40	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T13:52:35+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 39, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.052746038883924484, 0.20255789160728455, -0.0045078229159116745, 0.0248473659157753, 0.10497838258743286, 0.00675728265196085, 0.06521498411893845, 0.11486967653036118, -0.0023755673319101334, 0.12028469145298004, 0.027631845325231552, 0.08119397610425949, 0.12110675126314163, 0.15393014252185822, 0.005160121712833643, -0.24253977835178375, 0.05344875901937485, -0.09366832673549652, 0.004077504388988018, 0.11452110856771469, 0.1343945860862732, -0.10780399292707443, 0.08976872265338898, -0.00683097867295146, -0.01712046191096306, -0.015751034021377563, -0.07134060561656952, -0.06668227165937424, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # furina_seed42_eng_amh_hau_roman This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0233 - Spearman Corr: 0.7621 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Spearman Corr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\| \| No log \| 0.58 \| 200 \| 0.0306 \| 0.6454 \| \| No log \| 1.15 \| 400 \| 0.0353 \| 0.6854 \| \| No log \| 1.73 \| 600 \| 0.0298 \| 0.7055 \| \| 0.0458 \| 2.3 \| 800 \| 0.0307 \| 0.7105 \| \| 0.0458 \| 2.88 \| 1000 \| 0.0263 \| 0.7299 \| \| 0.0458 \| 3.45 \| 1200 \| 0.0273 \| 0.7357 \| \| 0.0222 \| 4.03 \| 1400 \| 0.0255 \| 0.7374 \| \| 0.0222 \| 4.6 \| 1600 \| 0.0268 \| 0.7398 \| \| 0.0222 \| 5.18 \| 1800 \| 0.0316 \| 0.7371 \| \| 0.0222 \| 5.76 \| 2000 \| 0.0245 \| 0.7445 \| \| 0.0155 \| 6.33 \| 2200 \| 0.0264 \| 0.7484 \| \| 0.0155 \| 6.91 \| 2400 \| 0.0311 \| 0.7549 \| \| 0.0155 \| 7.48 \| 2600 \| 0.0223 \| 0.7585 \| \| 0.0112 \| 8.06 \| 2800 \| 0.0257 \| 0.7483 \| \| 0.0112 \| 8.63 \| 3000 \| 0.0240 \| 0.7507 \| \| 0.0112 \| 9.21 \| 3200 \| 0.0275 \| 0.7609 \| \| 0.0112 \| 9.78 \| 3400 \| 0.0265 \| 0.7565 \| \| 0.0086 \| 10.36 \| 3600 \| 0.0250 \| 0.7534 \| \| 0.0086 \| 10.94 \| 3800 \| 0.0285 \| 0.7577 \| \| 0.0086 \| 11.51 \| 4000 \| 0.0225 \| 0.7625 \| \| 0.007 \| 12.09 \| 4200 \| 0.0233 \| 0.7621 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"tags": ["generated_from_trainer"], "base_model": "yihongLiu/furina", "model-index": [{"name": "furina_seed42_eng_amh_hau_roman", "results": []}]}	text-classification	Shijia/furina_seed42_eng_amh_hau_roman	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:yihongLiu/furina", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T13:53:48+00:00	[]	[]	TAGS #transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us	furina\_seed42\_eng\_amh\_hau\_roman ==================================== This model is a fine-tuned version of yihongLiu/furina on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0233 * Spearman Corr: 0.7621 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 32 * eval\_batch\_size: 128 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 30 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 60, 141, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.14007613062858582, 0.12448705732822418, 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null	null	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "280.09 +/- 14.77", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	Zaphare/ppo-LunarLander-v2	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-12T13:55:36+00:00	[]	[]	TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code	[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 39, 41, 17 ]	[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 0.03942384943366051, 0.04900386184453964, -0.005304091144353151, 0.026427261531352997, 0.107408307492733, -0.026511888951063156, 0.11188238859176636, 0.0814051404595375, 0.10722193866968155, 0.04762078449130058, 0.08338645845651627, 0.06030960753560066, 0.05080918222665787, 0.2571701407432556, 0.04754156619310379, -0.22987541556358337, 0.036159250885248184, -0.04869936779141426, 0.12395193427801132, 0.07178173214197159, -0.0038484656251966953, -0.06485428661108017, 0.020415637642145157, -0.013290755450725555, 0.05367108806967735, 0.04282612353563309, -0.01716216839849949, -0.08207534998655319, 0.07169748842716217, -0.06345846503973007, 0.06986866891384125, 0.07677983492612839, 0.13218913972377777, -0.17832116782665253, 0.029566360637545586, 0.02571309357881546, 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null	null	pruna-engine	<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching. - *How does the model quality change?* The quality of the model output might slightly vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. ## Setup You can run the smashed model with these steps: 0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`. 1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install. ```bash pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/ ``` 3. Download the model files using one of these three options. - Option 1 - Use command line interface (CLI): ```bash mkdir prompthero-openjourney-turbo-tiny-green-smashed huggingface-cli download PrunaAI/prompthero-openjourney-turbo-tiny-green-smashed --local-dir prompthero-openjourney-turbo-tiny-green-smashed --local-dir-use-symlinks False ``` - Option 2 - Use Python: ```python import subprocess repo_name = "prompthero-openjourney-turbo-tiny-green-smashed" subprocess.run(["mkdir", repo_name]) subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"]) ``` - Option 3 - Download them manually on the HuggingFace model page. 3. Load & run the model. ```python from pruna_engine.PrunaModel import PrunaModel model_path = "prompthero-openjourney-turbo-tiny-green-smashed/model" # Specify the downloaded model path. smashed_model = PrunaModel.load_model(model_path) # Load the model. smashed_model(prompt='Beautiful fruits in trees', height=512, width=512)[0][0] # Run the model where x is the expected input of. ``` ## Configurations The configuration info are in `config.json`. ## Credits & License We follow the same license as the original model. Please check the license of the original model prompthero/openjourney before using this model which provided the base model. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).	{"license": "apache-2.0", "library_name": "pruna-engine", "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"}	null	PrunaAI/prompthero-openjourney-turbo-tiny-green-smashed	[ "pruna-engine", "license:apache-2.0", "region:us" ]	2024-02-12T13:56:52+00:00	[]	[]	TAGS #pruna-engine #license-apache-2.0 #region-us	<div style="width: auto; margin-left: auto; margin-right: auto"> <a href="URL target="_blank" rel="noopener noreferrer"> <img src="https://i.URL alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> ![Twitter](URL ![GitHub](URL ![LinkedIn](URL ![Discord](URL # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next here. - Request access to easily compress your own AI models here. - Read the documentations to know more here - Join Pruna AI community on Discord here to share feedback/suggestions or get help. ## Results !image info Frequently Asked Questions - How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching. - How does the model quality change? The quality of the model output might slightly vary compared to the base model. - How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed. - What is the naming convention for Pruna Huggingface models? We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here. - What are "first" metrics? Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. ## Setup You can run the smashed model with these steps: 0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'. 1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install. 3. Download the model files using one of these three options. - Option 1 - Use command line interface (CLI): - Option 2 - Use Python: - Option 3 - Download them manually on the HuggingFace model page. 3. Load & run the model. ## Configurations The configuration info are in 'URL'. ## Credits & License We follow the same license as the original model. Please check the license of the original model prompthero/openjourney before using this model which provided the base model. ## Want to compress other models? - Contact us and tell us which model to compress next here. - Request access to easily compress your own AI models here.	[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.\n- How does the model quality change? The quality of the model output might slightly vary compared to the base model.\n- How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed.\n- What is the naming convention for Pruna Huggingface models? We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here.\n- What are \"first\" metrics? Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'.\n1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install.\n \n3. Download the model files using one of these three options. \n - Option 1 - Use command line interface (CLI):\n \n - Option 2 - Use Python:\n \n - Option 3 - Download them manually on the HuggingFace model page.\n3. Load & run the model.", "## Configurations\n\nThe configuration info are in 'URL'.", "## Credits & License\n\nWe follow the same license as the original model. Please check the license of the original model prompthero/openjourney before using this model which provided the base model.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]	[ "TAGS\n#pruna-engine #license-apache-2.0 #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.\n- How does the model quality change? The quality of the model output might slightly vary compared to the base model.\n- How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed.\n- What is the naming convention for Pruna Huggingface models? We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here.\n- What are \"first\" metrics? Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'.\n1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install.\n \n3. Download the model files using one of these three options. \n - Option 1 - Use command line interface (CLI):\n \n - Option 2 - Use Python:\n \n - Option 3 - Download them manually on the HuggingFace model page.\n3. Load & run the model.", "## Configurations\n\nThe configuration info are in 'URL'.", "## Credits & License\n\nWe follow the same license as the original model. Please check the license of the original model prompthero/openjourney before using this model which provided the base model.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]	[ 19, 92, 402, 155, 13, 39, 36 ]	[ "passage: TAGS\n#pruna-engine #license-apache-2.0 #region-us \n# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help." ]	[ -0.03573288768529892, 0.13100412487983704, -0.001540288794785738, 0.022444402799010277, 0.10067341476678848, 0.055228229612112045, 0.07351797074079514, 0.10892018675804138, 0.048860158771276474, 0.008610251359641552, 0.1138949766755104, 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-base-generator-rank16 This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2684 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 8.2873 \| 1.0 \| 179 \| 3.8892 \| \| 3.6837 \| 2.0 \| 358 \| 3.3411 \| \| 3.4127 \| 3.0 \| 537 \| 3.2684 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/electra-base-generator", "model-index": [{"name": "electra-base-generator-rank16", "results": []}]}	null	alitolga/electra-base-generator-rank16	[ "safetensors", "generated_from_trainer", "base_model:google/electra-base-generator", "license:apache-2.0", "region:us" ]	2024-02-12T13:59:43+00:00	[]	[]	TAGS #safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us	electra-base-generator-rank16 ============================= This model is a fine-tuned version of google/electra-base-generator on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.2684 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu118 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 40, 98, 4, 33 ]	[ "passage: TAGS\n#safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.0986127182841301, 0.018834460526704788, -0.0008125067106448114, 0.10979532450437546, 0.18232838809490204, 0.014075469225645065, 0.14945824444293976, 0.05513402447104454, -0.11618620902299881, 0.027743428945541382, 0.11155061423778534, 0.12338999658823013, -0.0060639819130301476, 0.12559986114501953, -0.0439465306699276, -0.19480036199092865, 0.025722401216626167, 0.0023020326625555754, -0.06931312382221222, 0.12410648912191391, 0.10072977840900421, -0.1471579670906067, 0.0789017304778099, -0.0045234463177621365, -0.218098446726799, 0.03776150941848755, 0.037302516400814056, -0.06900284439325333, 0.1463816612958908, 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-base-generator-rank32 This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2706 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 8.2965 \| 1.0 \| 179 \| 3.8731 \| \| 3.6721 \| 2.0 \| 358 \| 3.3368 \| \| 3.4122 \| 3.0 \| 537 \| 3.2706 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/electra-base-generator", "model-index": [{"name": "electra-base-generator-rank32", "results": []}]}	null	alitolga/electra-base-generator-rank32	[ "safetensors", "generated_from_trainer", "base_model:google/electra-base-generator", "license:apache-2.0", "region:us" ]	2024-02-12T14:02:32+00:00	[]	[]	TAGS #safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us	electra-base-generator-rank32 ============================= This model is a fine-tuned version of google/electra-base-generator on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.2706 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu118 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 40, 98, 4, 33 ]	[ "passage: TAGS\n#safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.0986127182841301, 0.018834460526704788, -0.0008125067106448114, 0.10979532450437546, 0.18232838809490204, 0.014075469225645065, 0.14945824444293976, 0.05513402447104454, -0.11618620902299881, 0.027743428945541382, 0.11155061423778534, 0.12338999658823013, -0.0060639819130301476, 0.12559986114501953, -0.0439465306699276, -0.19480036199092865, 0.025722401216626167, 0.0023020326625555754, -0.06931312382221222, 0.12410648912191391, 0.10072977840900421, -0.1471579670906067, 0.0789017304778099, -0.0045234463177621365, -0.218098446726799, 0.03776150941848755, 0.037302516400814056, -0.06900284439325333, 0.1463816612958908, 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null	null	transformers	## Exllama v2 Quantizations of HerculeanSea-7b-128k Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/Test157t/HerculeanSea-7b-128k \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/HerculeanSea-7b-128k-exl2/tree/8_0) \| 8.0 \| 8.0 \| 8.4 GB \| 9.8 GB \| 11.8 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/HerculeanSea-7b-128k-exl2/tree/6_5) \| 6.5 \| 8.0 \| 7.2 GB \| 8.6 GB \| 10.6 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/bartowski/HerculeanSea-7b-128k-exl2/tree/5_0) \| 5.0 \| 6.0 \| 6.0 GB \| 7.4 GB \| 9.4 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/bartowski/HerculeanSea-7b-128k-exl2/tree/4_25) \| 4.25 \| 6.0 \| 5.3 GB \| 6.7 GB \| 8.7 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/HerculeanSea-7b-128k-exl2/tree/3_5) \| 3.5 \| 6.0 \| 4.7 GB \| 6.1 GB \| 8.1 GB \| Lower quality, only use if you have to. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/HerculeanSea-7b-128k-exl2 HerculeanSea-7b-128k-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `HerculeanSea-7b-128k-exl2`: ```shell mkdir HerculeanSea-7b-128k-exl2 huggingface-cli download bartowski/HerculeanSea-7b-128k-exl2 --local-dir HerculeanSea-7b-128k-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir HerculeanSea-7b-128k-exl2-6_5 huggingface-cli download bartowski/HerculeanSea-7b-128k-exl2 --revision 6_5 --local-dir HerculeanSea-7b-128k-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir HerculeanSea-7b-128k-exl2-6.5 huggingface-cli download bartowski/HerculeanSea-7b-128k-exl2 --revision 6_5 --local-dir HerculeanSea-7b-128k-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski	{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Test157t/Pasta-Sea-7b-128k", "Locutusque/Hercules-2.0-Mistral-7B"], "quantized_by": "bartowski", "pipeline_tag": "text-generation"}	text-generation	bartowski/HerculeanSea-7b-128k-exl2	[ "transformers", "mergekit", "merge", "text-generation", "base_model:Test157t/Pasta-Sea-7b-128k", "base_model:Locutusque/Hercules-2.0-Mistral-7B", "endpoints_compatible", "region:us" ]	2024-02-12T14:05:20+00:00	[]	[]	TAGS #transformers #mergekit #merge #text-generation #base_model-Test157t/Pasta-Sea-7b-128k #base_model-Locutusque/Hercules-2.0-Mistral-7B #endpoints_compatible #region-us	Exllama v2 Quantizations of HerculeanSea-7b-128k ------------------------------------------------ Using <a href="URL ExLlamaV2 v0.0.13 for quantization. The "main" branch only contains the URL, download one of the other branches for the model (see below) Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions. Original model: URL Download instructions --------------------- With git: With huggingface hub (credit to TheBloke for instructions): To download the 'main' (only useful if you only care about URL) branch to a folder called 'HerculeanSea-7b-128k-exl2': To download from a different branch, add the '--revision' parameter: Linux: Windows (which apparently doesn't like \_ in folders sometimes?): Want to support my work? Visit my ko-fi page here: URL	[]	[ "TAGS\n#transformers #mergekit #merge #text-generation #base_model-Test157t/Pasta-Sea-7b-128k #base_model-Locutusque/Hercules-2.0-Mistral-7B #endpoints_compatible #region-us \n" ]	[ 67 ]	[ "passage: TAGS\n#transformers #mergekit #merge #text-generation #base_model-Test157t/Pasta-Sea-7b-128k #base_model-Locutusque/Hercules-2.0-Mistral-7B #endpoints_compatible #region-us \n" ]	[ -0.07367008179426193, -0.07330811023712158, -0.0036582325119525194, -0.03361327946186066, 0.07760003209114075, 0.058416515588760376, 0.1304343044757843, 0.1389797329902649, 0.11707784235477448, 0.017698314040899277, 0.08666084706783295, 0.052097614854574203, 0.03020465560257435, 0.12913961708545685, -0.06377653777599335, -0.2227928787469864, 0.09140966832637787, 0.07941398024559021, -0.20375613868236542, 0.08476302027702332, 0.07086564600467682, -0.036869116127491, 0.11035989224910736, -0.07499758154153824, -0.07739373296499252, 0.1153327226638794, 0.005068836733698845, 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # model_IMDB_peft This model is a fine-tuned version of [finiteautomata/bertweet-base-sentiment-analysis](https://huggingface.co/finiteautomata/bertweet-base-sentiment-analysis) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2807 - Accuracy: 0.8894 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.3222 \| 1.0 \| 1563 \| 0.3062 \| 0.8706 \| \| 0.3122 \| 2.0 \| 3126 \| 0.2992 \| 0.8770 \| \| 0.296 \| 3.0 \| 4689 \| 0.2916 \| 0.8825 \| \| 0.286 \| 4.0 \| 6252 \| 0.2864 \| 0.8847 \| \| 0.2723 \| 5.0 \| 7815 \| 0.2843 \| 0.8858 \| \| 0.2776 \| 6.0 \| 9378 \| 0.2802 \| 0.8877 \| \| 0.2688 \| 7.0 \| 10941 \| 0.2806 \| 0.888 \| \| 0.2641 \| 8.0 \| 12504 \| 0.2795 \| 0.889 \| \| 0.267 \| 9.0 \| 14067 \| 0.2807 \| 0.8896 \| \| 0.2726 \| 10.0 \| 15630 \| 0.2807 \| 0.8894 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu117 - Datasets 2.17.0 - Tokenizers 0.14.0	{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "finiteautomata/bertweet-base-sentiment-analysis", "model-index": [{"name": "model_IMDB_peft", "results": []}]}	null	Kudod/model_IMDB_peft	[ "generated_from_trainer", "base_model:finiteautomata/bertweet-base-sentiment-analysis", "region:us" ]	2024-02-12T14:05:46+00:00	[]	[]	TAGS #generated_from_trainer #base_model-finiteautomata/bertweet-base-sentiment-analysis #region-us	model\_IMDB\_peft ================= This model is a fine-tuned version of finiteautomata/bertweet-base-sentiment-analysis on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.2807 * Accuracy: 0.8894 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.34.0 * Pytorch 2.0.1+cu117 * Datasets 2.17.0 * Tokenizers 0.14.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.17.0\n* Tokenizers 0.14.0" ]	[ "TAGS\n#generated_from_trainer #base_model-finiteautomata/bertweet-base-sentiment-analysis #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.17.0\n* Tokenizers 0.14.0" ]	[ 33, 98, 4, 33 ]	[ "passage: TAGS\n#generated_from_trainer #base_model-finiteautomata/bertweet-base-sentiment-analysis #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.17.0\n* Tokenizers 0.14.0" ]	[ -0.06830080598592758, -0.03805186599493027, -0.00012877993867732584, 0.11464529484510422, 0.19074855744838715, 0.07356631010770798, 0.09693362563848495, 0.06533555686473846, -0.11521736532449722, 0.006774642039090395, 0.08862153440713882, 0.08712673932313919, -0.03863169252872467, 0.0720040500164032, -0.04662231728434563, -0.2703838050365448, -0.02375933900475502, 0.02332456037402153, -0.08463194966316223, 0.13977819681167603, 0.09769681841135025, -0.12376193702220917, 0.08850055187940598, -0.01689188741147518, -0.3271397054195404, 0.0417984277009964, 0.03767223656177521, -0.04875792935490608, 0.12742003798484802, -0.03936922922730446, 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null	null	peft	## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0	{"library_name": "peft"}	null	NBA55/llama2-7B-without-diversity-epoch-10-new	[ "peft", "region:us" ]	2024-02-12T14:09:02+00:00	[]	[]	TAGS #peft #region-us	## Training procedure The following 'bitsandbytes' quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0	[ "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n\n- PEFT 0.4.0" ]	[ "TAGS\n#peft #region-us \n", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n\n- PEFT 0.4.0" ]	[ 9, 154, 11 ]	[ "passage: TAGS\n#peft #region-us \n## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16### Framework versions\n\n\n- PEFT 0.4.0" ]	[ -0.06979092955589294, 0.021877916529774666, -0.0024098597932606936, 0.1378258466720581, 0.10862318426370621, 0.07474296540021896, 0.10942144691944122, 0.12339992076158524, 0.0572756864130497, 0.09055759757757187, 0.09443594515323639, 0.05080708488821983, 0.07617583870887756, 0.15425844490528107, -0.02472618594765663, -0.030937515199184418, 0.05461447685956955, 0.0024960634764283895, 0.003683591727167368, 0.07837489992380142, 0.05202331766486168, -0.036243125796318054, 0.037176959216594696, -0.09029994904994965, -0.1683429628610611, -0.0007763148751109838, 0.010803398676216602, 0.03168312832713127, 0.04643981158733368, 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null	null	peft	# r-zephyr-7b-beta-qlora The 'r' means replicate. This model is a model replicated by using https://github.com/huggingface/alignment-handbook. This model is a fine-tuned version on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set: - Loss: 0.5232 - Rewards/chosen: -0.9374 - Rewards/rejected: -1.7181 - Rewards/accuracies: 0.7734 - Rewards/margins: 0.7807 - Logps/rejected: -420.1122 - Logps/chosen: -341.2448 - Logits/rejected: 0.6190 - Logits/chosen: 0.6345 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.5917 \| 0.21 \| 100 \| 0.5950 \| -0.3904 \| -0.7775 \| 0.7109 \| 0.3872 \| -326.0618 \| -286.5451 \| -1.9790 \| -1.9769 \| \| 0.5281 \| 0.42 \| 200 \| 0.5492 \| -0.8657 \| -1.6137 \| 0.7617 \| 0.7479 \| -409.6739 \| -334.0814 \| -0.2289 \| -0.2367 \| \| 0.5321 \| 0.63 \| 300 \| 0.5321 \| -0.7444 \| -1.4427 \| 0.7734 \| 0.6983 \| -392.5731 \| -321.9463 \| 0.3829 \| 0.3741 \| \| 0.5149 \| 0.84 \| 400 \| 0.5233 \| -0.9570 \| -1.7432 \| 0.7617 \| 0.7862 \| -422.6298 \| -343.2071 \| 0.6479 \| 0.6688 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2	{"license": "apache-2.0", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "r-zephyr-7b-beta-qlora", "results": []}]}	null	amu/r-zephyr-7b-beta-qlora	[ "peft", "pytorch", "mistral", "alignment-handbook", "generated_from_trainer", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	2024-02-12T14:11:12+00:00	[]	[]	TAGS #peft #pytorch #mistral #alignment-handbook #generated_from_trainer #dataset-HuggingFaceH4/ultrafeedback_binarized #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us	r-zephyr-7b-beta-qlora ====================== The 'r' means replicate. This model is a model replicated by using URL This model is a fine-tuned version on the HuggingFaceH4/ultrafeedback\_binarized dataset. It achieves the following results on the evaluation set: * Loss: 0.5232 * Rewards/chosen: -0.9374 * Rewards/rejected: -1.7181 * Rewards/accuracies: 0.7734 * Rewards/margins: 0.7807 * Logps/rejected: -420.1122 * Logps/chosen: -341.2448 * Logits/rejected: 0.6190 * Logits/chosen: 0.6345 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-06 * train\_batch\_size: 4 * eval\_batch\_size: 8 * seed: 42 * distributed\_type: multi-GPU * num\_devices: 8 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 128 * total\_eval\_batch\_size: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 1 ### Training results ### Framework versions * PEFT 0.7.1 * Transformers 4.36.2 * Pytorch 2.1.2+cu121 * Datasets 2.14.6 * Tokenizers 0.15.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]	[ "TAGS\n#peft #pytorch #mistral #alignment-handbook #generated_from_trainer #dataset-HuggingFaceH4/ultrafeedback_binarized #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]	[ 71, 179, 4, 39 ]	[ "passage: TAGS\n#peft #pytorch #mistral #alignment-handbook #generated_from_trainer #dataset-HuggingFaceH4/ultrafeedback_binarized #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]	[ -0.09634587913751602, 0.12166961282491684, -0.0041625360026955605, 0.0905427560210228, 0.11117053031921387, 0.07131046801805496, 0.11264579743146896, 0.13616390526294708, -0.05254879966378212, 0.12403064221143723, 0.09426853060722351, 0.04196103289723396, 0.09421705454587936, 0.16592203080654144, 0.00437567662447691, -0.2700546383857727, 0.00816798210144043, -0.034653276205062866, -0.0986253023147583, 0.10762805491685867, 0.06922373175621033, 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null	null	pruna-engine	<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching. - *How does the model quality change?* The quality of the model output might slightly vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. ## Setup You can run the smashed model with these steps: 0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`. 1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install. ```bash pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/ ``` 3. Download the model files using one of these three options. - Option 1 - Use command line interface (CLI): ```bash mkdir stabilityai-sdxl-turbo-turbo-tiny-green-smashed huggingface-cli download PrunaAI/stabilityai-sdxl-turbo-turbo-tiny-green-smashed --local-dir stabilityai-sdxl-turbo-turbo-tiny-green-smashed --local-dir-use-symlinks False ``` - Option 2 - Use Python: ```python import subprocess repo_name = "stabilityai-sdxl-turbo-turbo-tiny-green-smashed" subprocess.run(["mkdir", repo_name]) subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"]) ``` - Option 3 - Download them manually on the HuggingFace model page. 3. Load & run the model. ```python from pruna_engine.PrunaModel import PrunaModel model_path = "stabilityai-sdxl-turbo-turbo-tiny-green-smashed/model" # Specify the downloaded model path. smashed_model = PrunaModel.load_model(model_path) # Load the model. smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input of. ``` ## Configurations The configuration info are in `config.json`. ## Credits & License We follow the same license as the original model. Please check the license of the original model stabilityai/sdxl-turbo before using this model which provided the base model. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).	{"license": "apache-2.0", "library_name": "pruna-engine", "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"}	null	PrunaAI/stabilityai-sdxl-turbo-turbo-tiny-green-smashed	[ "pruna-engine", "license:apache-2.0", "region:us" ]	2024-02-12T14:13:59+00:00	[]	[]	TAGS #pruna-engine #license-apache-2.0 #region-us	<div style="width: auto; margin-left: auto; margin-right: auto"> <a href="URL target="_blank" rel="noopener noreferrer"> <img src="https://i.URL alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> ![Twitter](URL ![GitHub](URL ![LinkedIn](URL ![Discord](URL # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next here. - Request access to easily compress your own AI models here. - Read the documentations to know more here - Join Pruna AI community on Discord here to share feedback/suggestions or get help. ## Results !image info Frequently Asked Questions - How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching. - How does the model quality change? The quality of the model output might slightly vary compared to the base model. - How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed. - What is the naming convention for Pruna Huggingface models? We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here. - What are "first" metrics? Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. ## Setup You can run the smashed model with these steps: 0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'. 1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install. 3. Download the model files using one of these three options. - Option 1 - Use command line interface (CLI): - Option 2 - Use Python: - Option 3 - Download them manually on the HuggingFace model page. 3. Load & run the model. ## Configurations The configuration info are in 'URL'. ## Credits & License We follow the same license as the original model. Please check the license of the original model stabilityai/sdxl-turbo before using this model which provided the base model. ## Want to compress other models? - Contact us and tell us which model to compress next here. - Request access to easily compress your own AI models here.	[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.\n- How does the model quality change? The quality of the model output might slightly vary compared to the base model.\n- How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed.\n- What is the naming convention for Pruna Huggingface models? We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here.\n- What are \"first\" metrics? Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'.\n1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install.\n \n3. Download the model files using one of these three options. \n - Option 1 - Use command line interface (CLI):\n \n - Option 2 - Use Python:\n \n - Option 3 - Download them manually on the HuggingFace model page.\n3. Load & run the model.", "## Configurations\n\nThe configuration info are in 'URL'.", "## Credits & License\n\nWe follow the same license as the original model. Please check the license of the original model stabilityai/sdxl-turbo before using this model which provided the base model.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]	[ "TAGS\n#pruna-engine #license-apache-2.0 #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.\n- How does the model quality change? The quality of the model output might slightly vary compared to the base model.\n- How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed.\n- What is the naming convention for Pruna Huggingface models? We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here.\n- What are \"first\" metrics? Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'.\n1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install.\n \n3. Download the model files using one of these three options. \n - Option 1 - Use command line interface (CLI):\n \n - Option 2 - Use Python:\n \n - Option 3 - Download them manually on the HuggingFace model page.\n3. Load & run the model.", "## Configurations\n\nThe configuration info are in 'URL'.", "## Credits & License\n\nWe follow the same license as the original model. Please check the license of the original model stabilityai/sdxl-turbo before using this model which provided the base model.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]	[ 19, 92, 402, 155, 13, 43, 36 ]	[ "passage: TAGS\n#pruna-engine #license-apache-2.0 #region-us \n# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help." ]	[ -0.03573288768529892, 0.13100412487983704, -0.001540288794785738, 0.022444402799010277, 0.10067341476678848, 0.055228229612112045, 0.07351797074079514, 0.10892018675804138, 0.048860158771276474, 0.008610251359641552, 0.1138949766755104, 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null	null	pruna-engine	<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching. - *How does the model quality change?* The quality of the model output might slightly vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. ## Setup You can run the smashed model with these steps: 0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`. 1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install. ```bash pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/ ``` 3. Download the model files using one of these three options. - Option 1 - Use command line interface (CLI): ```bash mkdir segmind-Segmind-Vega-turbo-tiny-green-smashed huggingface-cli download PrunaAI/segmind-Segmind-Vega-turbo-tiny-green-smashed --local-dir segmind-Segmind-Vega-turbo-tiny-green-smashed --local-dir-use-symlinks False ``` - Option 2 - Use Python: ```python import subprocess repo_name = "segmind-Segmind-Vega-turbo-tiny-green-smashed" subprocess.run(["mkdir", repo_name]) subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"]) ``` - Option 3 - Download them manually on the HuggingFace model page. 3. Load & run the model. ```python from pruna_engine.PrunaModel import PrunaModel model_path = "segmind-Segmind-Vega-turbo-tiny-green-smashed/model" # Specify the downloaded model path. smashed_model = PrunaModel.load_model(model_path) # Load the model. smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input of. ``` ## Configurations The configuration info are in `config.json`. ## Credits & License We follow the same license as the original model. Please check the license of the original model segmind/Segmind-Vega before using this model which provided the base model. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).	{"license": "apache-2.0", "library_name": "pruna-engine", "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"}	null	PrunaAI/segmind-Segmind-Vega-turbo-tiny-green-smashed	[ "pruna-engine", "license:apache-2.0", "region:us" ]	2024-02-12T14:15:08+00:00	[]	[]	TAGS #pruna-engine #license-apache-2.0 #region-us	<div style="width: auto; margin-left: auto; margin-right: auto"> <a href="URL target="_blank" rel="noopener noreferrer"> <img src="https://i.URL alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> ![Twitter](URL ![GitHub](URL ![LinkedIn](URL ![Discord](URL # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next here. - Request access to easily compress your own AI models here. - Read the documentations to know more here - Join Pruna AI community on Discord here to share feedback/suggestions or get help. ## Results !image info Frequently Asked Questions - How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching. - How does the model quality change? The quality of the model output might slightly vary compared to the base model. - How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed. - What is the naming convention for Pruna Huggingface models? We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here. - What are "first" metrics? Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. ## Setup You can run the smashed model with these steps: 0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'. 1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install. 3. Download the model files using one of these three options. - Option 1 - Use command line interface (CLI): - Option 2 - Use Python: - Option 3 - Download them manually on the HuggingFace model page. 3. Load & run the model. ## Configurations The configuration info are in 'URL'. ## Credits & License We follow the same license as the original model. Please check the license of the original model segmind/Segmind-Vega before using this model which provided the base model. ## Want to compress other models? - Contact us and tell us which model to compress next here. - Request access to easily compress your own AI models here.	[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.\n- How does the model quality change? The quality of the model output might slightly vary compared to the base model.\n- How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed.\n- What is the naming convention for Pruna Huggingface models? We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here.\n- What are \"first\" metrics? Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'.\n1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install.\n \n3. Download the model files using one of these three options. \n - Option 1 - Use command line interface (CLI):\n \n - Option 2 - Use Python:\n \n - Option 3 - Download them manually on the HuggingFace model page.\n3. Load & run the model.", "## Configurations\n\nThe configuration info are in 'URL'.", "## Credits & License\n\nWe follow the same license as the original model. Please check the license of the original model segmind/Segmind-Vega before using this model which provided the base model.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]	[ "TAGS\n#pruna-engine #license-apache-2.0 #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.\n- How does the model quality change? The quality of the model output might slightly vary compared to the base model.\n- How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed.\n- What is the naming convention for Pruna Huggingface models? We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here.\n- What are \"first\" metrics? Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'.\n1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install.\n \n3. Download the model files using one of these three options. \n - Option 1 - Use command line interface (CLI):\n \n - Option 2 - Use Python:\n \n - Option 3 - Download them manually on the HuggingFace model page.\n3. Load & run the model.", "## Configurations\n\nThe configuration info are in 'URL'.", "## Credits & License\n\nWe follow the same license as the original model. Please check the license of the original model segmind/Segmind-Vega before using this model which provided the base model.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]	[ 19, 92, 402, 155, 13, 42, 36 ]	[ "passage: TAGS\n#pruna-engine #license-apache-2.0 #region-us \n# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help." ]	[ -0.03573288768529892, 0.13100412487983704, -0.001540288794785738, 0.022444402799010277, 0.10067341476678848, 0.055228229612112045, 0.07351797074079514, 0.10892018675804138, 0.048860158771276474, 0.008610251359641552, 0.1138949766755104, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # furina_seed42_eng_kin_amh_roman This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0284 - Spearman Corr: 0.7771 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Spearman Corr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\| \| No log \| 0.65 \| 200 \| 0.0373 \| 0.5747 \| \| No log \| 1.3 \| 400 \| 0.0297 \| 0.6851 \| \| No log \| 1.95 \| 600 \| 0.0311 \| 0.7236 \| \| 0.0545 \| 2.61 \| 800 \| 0.0305 \| 0.7322 \| \| 0.0545 \| 3.26 \| 1000 \| 0.0281 \| 0.7496 \| \| 0.0545 \| 3.91 \| 1200 \| 0.0278 \| 0.7582 \| \| 0.0208 \| 4.56 \| 1400 \| 0.0278 \| 0.7528 \| \| 0.0208 \| 5.21 \| 1600 \| 0.0238 \| 0.7556 \| \| 0.0208 \| 5.86 \| 1800 \| 0.0235 \| 0.7631 \| \| 0.0143 \| 6.51 \| 2000 \| 0.0245 \| 0.7634 \| \| 0.0143 \| 7.17 \| 2200 \| 0.0243 \| 0.7619 \| \| 0.0143 \| 7.82 \| 2400 \| 0.0242 \| 0.7651 \| \| 0.0102 \| 8.47 \| 2600 \| 0.0257 \| 0.7645 \| \| 0.0102 \| 9.12 \| 2800 \| 0.0271 \| 0.7713 \| \| 0.0102 \| 9.77 \| 3000 \| 0.0255 \| 0.7661 \| \| 0.0079 \| 10.42 \| 3200 \| 0.0218 \| 0.7720 \| \| 0.0079 \| 11.07 \| 3400 \| 0.0250 \| 0.7658 \| \| 0.0079 \| 11.73 \| 3600 \| 0.0266 \| 0.7628 \| \| 0.0064 \| 12.38 \| 3800 \| 0.0267 \| 0.7657 \| \| 0.0064 \| 13.03 \| 4000 \| 0.0261 \| 0.7680 \| \| 0.0064 \| 13.68 \| 4200 \| 0.0232 \| 0.7720 \| \| 0.0055 \| 14.33 \| 4400 \| 0.0256 \| 0.7737 \| \| 0.0055 \| 14.98 \| 4600 \| 0.0237 \| 0.7736 \| \| 0.0055 \| 15.64 \| 4800 \| 0.0284 \| 0.7771 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"tags": ["generated_from_trainer"], "base_model": "yihongLiu/furina", "model-index": [{"name": "furina_seed42_eng_kin_amh_roman", "results": []}]}	text-classification	Shijia/furina_seed42_eng_kin_amh_roman	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:yihongLiu/furina", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T14:18:30+00:00	[]	[]	TAGS #transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us	furina\_seed42\_eng\_kin\_amh\_roman ==================================== This model is a fine-tuned version of yihongLiu/furina on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0284 * Spearman Corr: 0.7771 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 32 * eval\_batch\_size: 128 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 30 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 60, 141, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.14007613062858582, 0.12448705732822418, 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null	null	transformers	# Uploaded model - Developed by: jaykchen - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/tinyllama-bnb-4bit"}	text-generation	jaykchen/tiny	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/tinyllama-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T14:19:02+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #en #base_model-unsloth/tinyllama-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# Uploaded model - Developed by: jaykchen - License: apache-2.0 - Finetuned from model : unsloth/tinyllama-bnb-4bit This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>	[ "# Uploaded model\n\n- Developed by: jaykchen\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #en #base_model-unsloth/tinyllama-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: jaykchen\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]	[ 81, 77 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #en #base_model-unsloth/tinyllama-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: jaykchen\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]	[ -0.032661546021699905, 0.04324871301651001, -0.0026716997381299734, 0.13264767825603485, 0.08342783153057098, 0.023126505315303802, 0.131966233253479, 0.11868315935134888, -0.07078026980161667, -0.03598950058221817, 0.10409598797559738, 0.11625891923904419, -0.0009124791249632835, 0.03467448428273201, -0.010704591870307922, -0.13671527802944183, 0.06969711184501648, -0.052030596882104874, -0.059581734240055084, 0.04869536682963371, 0.06488703191280365, -0.006688247434794903, 0.09261702001094818, -0.08134733140468597, -0.04171440005302429, 0.014656092040240765, 0.0032548061572015285, -0.02605384960770607, 0.025466572493314743, 0.06708849221467972, 0.0008883287664502859, 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null	null	diffusers	<!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # SDXL LoRA DreamBooth - doroshroman/finetuned_sd_xl <Gallery /> ## Model description These are doroshroman/finetuned_sd_xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of guy raise money for army to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](doroshroman/finetuned_sd_xl/tree/main) them in the Files & versions tab. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]	{"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of guy raise money for army", "widget": []}	text-to-image	doroshroman/finetuned_sd_xl	[ "diffusers", "tensorboard", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "has_space", "region:us" ]	2024-02-12T14:23:09+00:00	[]	[]	TAGS #diffusers #tensorboard #text-to-image #stable-diffusion-xl #stable-diffusion-xl-diffusers #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us	# SDXL LoRA DreamBooth - doroshroman/finetuned_sd_xl <Gallery /> ## Model description These are doroshroman/finetuned_sd_xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using DreamBooth. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of guy raise money for army to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab. ## Intended uses & limitations #### How to use #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]	[ "# SDXL LoRA DreamBooth - doroshroman/finetuned_sd_xl\n\n<Gallery />", "## Model description\n\nThese are doroshroman/finetuned_sd_xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of guy raise money for army to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]	[ "TAGS\n#diffusers #tensorboard #text-to-image #stable-diffusion-xl #stable-diffusion-xl-diffusers #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n", "# SDXL LoRA DreamBooth - doroshroman/finetuned_sd_xl\n\n<Gallery />", "## Model description\n\nThese are doroshroman/finetuned_sd_xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of guy raise money for army to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]	[ 86, 26, 91, 22, 28, 9, 5, 24, 16 ]	[ "passage: TAGS\n#diffusers #tensorboard #text-to-image #stable-diffusion-xl #stable-diffusion-xl-diffusers #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n# SDXL LoRA DreamBooth - doroshroman/finetuned_sd_xl\n\n<Gallery />## Model description\n\nThese are doroshroman/finetuned_sd_xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use a photo of guy raise money for army to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.## Intended uses & limitations#### How to use#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]## Training details\n\n[TODO: describe the data used to train the model]" ]	[ -0.039166659116744995, 0.18985046446323395, -0.0031269246246665716, 0.039503421634435654, 0.12361370772123337, -0.03181440010666847, 0.1610679030418396, 0.10718491673469543, 0.0013916492462158203, 0.11499498039484024, 0.00807119719684124, 0.008034673519432545, 0.1338765174150467, 0.19034409523010254, 0.008032826706767082, -0.23123900592327118, -0.0034897630102932453, 0.005464902613312006, 0.05732732638716698, 0.05806443840265274, 0.055773090571165085, -0.10954467952251434, 0.053180672228336334, -0.021307973191142082, -0.07509152591228485, 0.06774281710386276, -0.03402178734540939, 0.02727632038295269, 0.008912887424230576, 0.0413302518427372, 0.07709506154060364, 0.061213232576847076, 0.09761745482683182, -0.1808445304632187, 0.011047791689634323, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	Guilherme34/Jennifer-uwu-version	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T14:23:32+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	null	# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="xncy/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	{"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	xncy/q-FrozenLake-v1-4x4-noSlippery	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	2024-02-12T14:24:08+00:00	[]	[]	TAGS #FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us	# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage	[ "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ "TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ 40, 39 ]	[ "passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ 0.04578453302383423, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b5_1 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.6279 - Mean Iou: 0.4054 - Mean Accuracy: 0.7471 - Overall Accuracy: 0.8860 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.5956 - Accuracy Undropoff: 0.8986 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.3318 - Iou Undropoff: 0.8843 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.0071 \| 5.0 \| 10 \| 1.0206 \| 0.1745 \| 0.2748 \| 0.5034 \| nan \| 0.0255 \| 0.5241 \| 0.0 \| 0.0147 \| 0.5087 \| \| 0.9688 \| 10.0 \| 20 \| 0.9873 \| 0.2140 \| 0.3486 \| 0.5771 \| nan \| 0.0992 \| 0.5979 \| 0.0 \| 0.0582 \| 0.5838 \| \| 0.9406 \| 15.0 \| 30 \| 0.9313 \| 0.2613 \| 0.4446 \| 0.6655 \| nan \| 0.2038 \| 0.6855 \| 0.0 \| 0.1135 \| 0.6705 \| \| 0.9278 \| 20.0 \| 40 \| 0.8851 \| 0.2930 \| 0.5149 \| 0.7111 \| nan \| 0.3009 \| 0.7289 \| 0.0 \| 0.1648 \| 0.7142 \| \| 0.8956 \| 25.0 \| 50 \| 0.8563 \| 0.3118 \| 0.5642 \| 0.7358 \| nan \| 0.3770 \| 0.7514 \| 0.0 \| 0.1985 \| 0.7370 \| \| 0.8674 \| 30.0 \| 60 \| 0.8260 \| 0.3303 \| 0.6086 \| 0.7664 \| nan \| 0.4366 \| 0.7807 \| 0.0 \| 0.2246 \| 0.7664 \| \| 0.8438 \| 35.0 \| 70 \| 0.8149 \| 0.3347 \| 0.6355 \| 0.7671 \| nan \| 0.4921 \| 0.7790 \| 0.0 \| 0.2381 \| 0.7660 \| \| 0.8309 \| 40.0 \| 80 \| 0.7881 \| 0.3459 \| 0.6472 \| 0.7847 \| nan \| 0.4972 \| 0.7972 \| 0.0 \| 0.2539 \| 0.7839 \| \| 0.8069 \| 45.0 \| 90 \| 0.7640 \| 0.3567 \| 0.6617 \| 0.8041 \| nan \| 0.5063 \| 0.8170 \| 0.0 \| 0.2668 \| 0.8033 \| \| 0.7779 \| 50.0 \| 100 \| 0.7486 \| 0.3637 \| 0.6792 \| 0.8145 \| nan \| 0.5316 \| 0.8268 \| 0.0 \| 0.2778 \| 0.8132 \| \| 0.7695 \| 55.0 \| 110 \| 0.7354 \| 0.3684 \| 0.6936 \| 0.8214 \| nan \| 0.5542 \| 0.8329 \| 0.0 \| 0.2858 \| 0.8195 \| \| 0.7568 \| 60.0 \| 120 \| 0.7164 \| 0.3757 \| 0.7032 \| 0.8365 \| nan \| 0.5577 \| 0.8486 \| 0.0 \| 0.2924 \| 0.8347 \| \| 0.7285 \| 65.0 \| 130 \| 0.6976 \| 0.3836 \| 0.7119 \| 0.8484 \| nan \| 0.5630 \| 0.8608 \| 0.0 \| 0.3042 \| 0.8467 \| \| 0.7217 \| 70.0 \| 140 \| 0.6922 \| 0.3857 \| 0.7217 \| 0.8499 \| nan \| 0.5817 \| 0.8616 \| 0.0 \| 0.3091 \| 0.8480 \| \| 0.7095 \| 75.0 \| 150 \| 0.6708 \| 0.3926 \| 0.7287 \| 0.8624 \| nan \| 0.5828 \| 0.8745 \| 0.0 \| 0.3172 \| 0.8605 \| \| 0.6944 \| 80.0 \| 160 \| 0.6637 \| 0.3951 \| 0.7320 \| 0.8660 \| nan \| 0.5858 \| 0.8781 \| 0.0 \| 0.3212 \| 0.8641 \| \| 0.6878 \| 85.0 \| 170 \| 0.6632 \| 0.3942 \| 0.7397 \| 0.8673 \| nan \| 0.6005 \| 0.8788 \| 0.0 \| 0.3175 \| 0.8652 \| \| 0.6868 \| 90.0 \| 180 \| 0.6468 \| 0.3998 \| 0.7391 \| 0.8756 \| nan \| 0.5902 \| 0.8880 \| 0.0 \| 0.3257 \| 0.8739 \| \| 0.6581 \| 95.0 \| 190 \| 0.6444 \| 0.4003 \| 0.7421 \| 0.8776 \| nan \| 0.5942 \| 0.8899 \| 0.0 \| 0.3249 \| 0.8759 \| \| 0.6587 \| 100.0 \| 200 \| 0.6383 \| 0.4026 \| 0.7427 \| 0.8814 \| nan \| 0.5914 \| 0.8940 \| 0.0 \| 0.3281 \| 0.8797 \| \| 0.6525 \| 105.0 \| 210 \| 0.6334 \| 0.4032 \| 0.7434 \| 0.8825 \| nan \| 0.5918 \| 0.8951 \| 0.0 \| 0.3289 \| 0.8808 \| \| 0.658 \| 110.0 \| 220 \| 0.6345 \| 0.4026 \| 0.7451 \| 0.8811 \| nan \| 0.5968 \| 0.8934 \| 0.0 \| 0.3285 \| 0.8793 \| \| 0.6575 \| 115.0 \| 230 \| 0.6300 \| 0.4050 \| 0.7463 \| 0.8851 \| nan \| 0.5948 \| 0.8977 \| 0.0 \| 0.3314 \| 0.8835 \| \| 0.6625 \| 120.0 \| 240 \| 0.6279 \| 0.4054 \| 0.7471 \| 0.8860 \| nan \| 0.5956 \| 0.8986 \| 0.0 \| 0.3318 \| 0.8843 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "dropoff-utcustom-train-SF-RGB-b5_1", "results": []}]}	image-segmentation	sam1120/dropoff-utcustom-train-SF-RGB-b5_1	[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	2024-02-12T14:24:17+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us	dropoff-utcustom-train-SF-RGB-b5\_1 =================================== This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: * Loss: 0.6279 * Mean Iou: 0.4054 * Mean Accuracy: 0.7471 * Overall Accuracy: 0.8860 * Accuracy Unlabeled: nan * Accuracy Dropoff: 0.5956 * Accuracy Undropoff: 0.8986 * Iou Unlabeled: 0.0 * Iou Dropoff: 0.3318 * Iou Undropoff: 0.8843 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-06 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.05 * num\_epochs: 120 ### Training results ### Framework versions * Transformers 4.30.2 * Pytorch 2.0.1+cu117 * Datasets 2.13.1 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ 48, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ -0.10578995198011398, 0.0363011509180069, -0.002025465713813901, 0.11662387102842331, 0.1717010736465454, 0.028218386694788933, 0.11702316999435425, 0.11551129817962646, -0.10852751135826111, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b5_2 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.4848 - Mean Iou: 0.4257 - Mean Accuracy: 0.7972 - Overall Accuracy: 0.9466 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.6343 - Accuracy Undropoff: 0.9601 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.3321 - Iou Undropoff: 0.9451 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.0108 \| 5.0 \| 10 \| 1.0721 \| 0.1514 \| 0.5401 \| 0.4205 \| nan \| 0.6706 \| 0.4096 \| 0.0 \| 0.0494 \| 0.4047 \| \| 0.9654 \| 10.0 \| 20 \| 0.9802 \| 0.2190 \| 0.6570 \| 0.5944 \| nan \| 0.7253 \| 0.5887 \| 0.0 \| 0.0745 \| 0.5826 \| \| 0.9175 \| 15.0 \| 30 \| 0.9047 \| 0.2553 \| 0.7350 \| 0.6792 \| nan \| 0.7960 \| 0.6741 \| 0.0 \| 0.0973 \| 0.6686 \| \| 0.9052 \| 20.0 \| 40 \| 0.8427 \| 0.2812 \| 0.7661 \| 0.7377 \| nan \| 0.7971 \| 0.7351 \| 0.0 \| 0.1146 \| 0.7290 \| \| 0.8555 \| 25.0 \| 50 \| 0.7970 \| 0.3063 \| 0.7827 \| 0.7900 \| nan \| 0.7748 \| 0.7906 \| 0.0 \| 0.1357 \| 0.7832 \| \| 0.8291 \| 30.0 \| 60 \| 0.7543 \| 0.3289 \| 0.7891 \| 0.8332 \| nan \| 0.7410 \| 0.8372 \| 0.0 \| 0.1586 \| 0.8282 \| \| 0.7923 \| 35.0 \| 70 \| 0.7327 \| 0.3375 \| 0.7961 \| 0.8471 \| nan \| 0.7405 \| 0.8517 \| 0.0 \| 0.1701 \| 0.8425 \| \| 0.7724 \| 40.0 \| 80 \| 0.6994 \| 0.3529 \| 0.7968 \| 0.8719 \| nan \| 0.7149 \| 0.8787 \| 0.0 \| 0.1906 \| 0.8682 \| \| 0.7215 \| 45.0 \| 90 \| 0.6675 \| 0.3694 \| 0.7935 \| 0.8954 \| nan \| 0.6824 \| 0.9047 \| 0.0 \| 0.2157 \| 0.8926 \| \| 0.6907 \| 50.0 \| 100 \| 0.6521 \| 0.3742 \| 0.7998 \| 0.9000 \| nan \| 0.6904 \| 0.9091 \| 0.0 \| 0.2252 \| 0.8973 \| \| 0.6768 \| 55.0 \| 110 \| 0.6260 \| 0.3850 \| 0.8022 \| 0.9118 \| nan \| 0.6827 \| 0.9217 \| 0.0 \| 0.2455 \| 0.9094 \| \| 0.659 \| 60.0 \| 120 \| 0.6010 \| 0.3965 \| 0.7973 \| 0.9244 \| nan \| 0.6586 \| 0.9359 \| 0.0 \| 0.2671 \| 0.9224 \| \| 0.6265 \| 65.0 \| 130 \| 0.5847 \| 0.4005 \| 0.7992 \| 0.9276 \| nan \| 0.6592 \| 0.9393 \| 0.0 \| 0.2757 \| 0.9258 \| \| 0.6134 \| 70.0 \| 140 \| 0.5673 \| 0.4060 \| 0.8022 \| 0.9316 \| nan \| 0.6611 \| 0.9433 \| 0.0 \| 0.2881 \| 0.9297 \| \| 0.5864 \| 75.0 \| 150 \| 0.5401 \| 0.4132 \| 0.7961 \| 0.9383 \| nan \| 0.6410 \| 0.9511 \| 0.0 \| 0.3029 \| 0.9366 \| \| 0.5686 \| 80.0 \| 160 \| 0.5289 \| 0.4153 \| 0.7974 \| 0.9395 \| nan \| 0.6424 \| 0.9524 \| 0.0 \| 0.3080 \| 0.9379 \| \| 0.5597 \| 85.0 \| 170 \| 0.5386 \| 0.4114 \| 0.8079 \| 0.9350 \| nan \| 0.6692 \| 0.9465 \| 0.0 \| 0.3011 \| 0.9331 \| \| 0.5718 \| 90.0 \| 180 \| 0.5080 \| 0.4210 \| 0.7947 \| 0.9438 \| nan \| 0.6321 \| 0.9573 \| 0.0 \| 0.3208 \| 0.9423 \| \| 0.517 \| 95.0 \| 190 \| 0.5026 \| 0.4222 \| 0.7956 \| 0.9445 \| nan \| 0.6332 \| 0.9580 \| 0.0 \| 0.3236 \| 0.9430 \| \| 0.5252 \| 100.0 \| 200 \| 0.4990 \| 0.4232 \| 0.7969 \| 0.9450 \| nan \| 0.6354 \| 0.9584 \| 0.0 \| 0.3261 \| 0.9435 \| \| 0.5174 \| 105.0 \| 210 \| 0.4951 \| 0.4223 \| 0.8012 \| 0.9437 \| nan \| 0.6457 \| 0.9567 \| 0.0 \| 0.3249 \| 0.9422 \| \| 0.5217 \| 110.0 \| 220 \| 0.4882 \| 0.4238 \| 0.7993 \| 0.9450 \| nan \| 0.6404 \| 0.9582 \| 0.0 \| 0.3280 \| 0.9435 \| \| 0.5224 \| 115.0 \| 230 \| 0.4846 \| 0.4258 \| 0.7968 \| 0.9467 \| nan \| 0.6333 \| 0.9603 \| 0.0 \| 0.3321 \| 0.9452 \| \| 0.5399 \| 120.0 \| 240 \| 0.4848 \| 0.4257 \| 0.7972 \| 0.9466 \| nan \| 0.6343 \| 0.9601 \| 0.0 \| 0.3321 \| 0.9451 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "dropoff-utcustom-train-SF-RGB-b5_2", "results": []}]}	image-segmentation	sam1120/dropoff-utcustom-train-SF-RGB-b5_2	[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	2024-02-12T14:24:47+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us	dropoff-utcustom-train-SF-RGB-b5\_2 =================================== This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: * Loss: 0.4848 * Mean Iou: 0.4257 * Mean Accuracy: 0.7972 * Overall Accuracy: 0.9466 * Accuracy Unlabeled: nan * Accuracy Dropoff: 0.6343 * Accuracy Undropoff: 0.9601 * Iou Unlabeled: 0.0 * Iou Dropoff: 0.3321 * Iou Undropoff: 0.9451 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 3e-06 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.05 * num\_epochs: 120 ### Training results ### Framework versions * Transformers 4.30.2 * Pytorch 2.0.1+cu117 * Datasets 2.13.1 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ 48, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ -0.10557883977890015, 0.034187328070402145, -0.0019901443738490343, 0.11674907058477402, 0.17250721156597137, 0.028266573324799538, 0.11677761375904083, 0.11515800654888153, -0.11076497286558151, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b5_3 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.3770 - Mean Iou: 0.4572 - Mean Accuracy: 0.7822 - Overall Accuracy: 0.9640 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.5839 - Accuracy Undropoff: 0.9805 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.4086 - Iou Undropoff: 0.9631 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 15 - eval_batch_size: 15 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.3135 \| 5.0 \| 10 \| 1.2008 \| 0.0546 \| 0.2586 \| 0.1227 \| nan \| 0.4069 \| 0.1103 \| 0.0 \| 0.0535 \| 0.1102 \| \| 1.2309 \| 10.0 \| 20 \| 1.1294 \| 0.1176 \| 0.3397 \| 0.2490 \| nan \| 0.4388 \| 0.2407 \| 0.0 \| 0.1129 \| 0.2400 \| \| 1.1346 \| 15.0 \| 30 \| 1.0395 \| 0.2171 \| 0.4865 \| 0.5022 \| nan \| 0.4694 \| 0.5036 \| 0.0 \| 0.1524 \| 0.4989 \| \| 1.1088 \| 20.0 \| 40 \| 0.9755 \| 0.2608 \| 0.5521 \| 0.6176 \| nan \| 0.4808 \| 0.6235 \| 0.0 \| 0.1661 \| 0.6163 \| \| 1.007 \| 25.0 \| 50 \| 0.9197 \| 0.2895 \| 0.5959 \| 0.6775 \| nan \| 0.5068 \| 0.6849 \| 0.0 \| 0.1923 \| 0.6763 \| \| 0.9145 \| 30.0 \| 60 \| 0.8635 \| 0.3162 \| 0.6299 \| 0.7335 \| nan \| 0.5168 \| 0.7429 \| 0.0 \| 0.2156 \| 0.7329 \| \| 0.8745 \| 35.0 \| 70 \| 0.8070 \| 0.3398 \| 0.6784 \| 0.7808 \| nan \| 0.5667 \| 0.7901 \| 0.0 \| 0.2404 \| 0.7791 \| \| 0.8088 \| 40.0 \| 80 \| 0.7442 \| 0.3667 \| 0.7191 \| 0.8290 \| nan \| 0.5993 \| 0.8389 \| 0.0 \| 0.2730 \| 0.8272 \| \| 0.7184 \| 45.0 \| 90 \| 0.6956 \| 0.3832 \| 0.7513 \| 0.8603 \| nan \| 0.6323 \| 0.8702 \| 0.0 \| 0.2915 \| 0.8580 \| \| 0.6908 \| 50.0 \| 100 \| 0.6751 \| 0.3931 \| 0.7592 \| 0.8748 \| nan \| 0.6332 \| 0.8853 \| 0.0 \| 0.3067 \| 0.8728 \| \| 0.643 \| 55.0 \| 110 \| 0.6101 \| 0.4134 \| 0.7714 \| 0.9108 \| nan \| 0.6194 \| 0.9234 \| 0.0 \| 0.3308 \| 0.9094 \| \| 0.6014 \| 60.0 \| 120 \| 0.5971 \| 0.4166 \| 0.7826 \| 0.9189 \| nan \| 0.6339 \| 0.9313 \| 0.0 \| 0.3324 \| 0.9175 \| \| 0.5685 \| 65.0 \| 130 \| 0.5595 \| 0.4304 \| 0.7946 \| 0.9328 \| nan \| 0.6439 \| 0.9453 \| 0.0 \| 0.3599 \| 0.9314 \| \| 0.5172 \| 70.0 \| 140 \| 0.5344 \| 0.4373 \| 0.8010 \| 0.9406 \| nan \| 0.6488 \| 0.9532 \| 0.0 \| 0.3727 \| 0.9393 \| \| 0.4757 \| 75.0 \| 150 \| 0.4963 \| 0.4434 \| 0.7997 \| 0.9490 \| nan \| 0.6368 \| 0.9626 \| 0.0 \| 0.3822 \| 0.9479 \| \| 0.4288 \| 80.0 \| 160 \| 0.4599 \| 0.4488 \| 0.7936 \| 0.9556 \| nan \| 0.6169 \| 0.9702 \| 0.0 \| 0.3918 \| 0.9546 \| \| 0.4124 \| 85.0 \| 170 \| 0.4710 \| 0.4469 \| 0.7989 \| 0.9540 \| nan \| 0.6296 \| 0.9681 \| 0.0 \| 0.3876 \| 0.9529 \| \| 0.4995 \| 90.0 \| 180 \| 0.4209 \| 0.4537 \| 0.7883 \| 0.9606 \| nan \| 0.6004 \| 0.9762 \| 0.0 \| 0.4015 \| 0.9597 \| \| 0.3815 \| 95.0 \| 190 \| 0.4287 \| 0.4524 \| 0.7919 \| 0.9595 \| nan \| 0.6090 \| 0.9748 \| 0.0 \| 0.3988 \| 0.9586 \| \| 0.3764 \| 100.0 \| 200 \| 0.4245 \| 0.4529 \| 0.7913 \| 0.9600 \| nan \| 0.6073 \| 0.9753 \| 0.0 \| 0.3998 \| 0.9590 \| \| 0.4074 \| 105.0 \| 210 \| 0.4096 \| 0.4542 \| 0.7894 \| 0.9613 \| nan \| 0.6018 \| 0.9769 \| 0.0 \| 0.4021 \| 0.9603 \| \| 0.3975 \| 110.0 \| 220 \| 0.4107 \| 0.4538 \| 0.7905 \| 0.9610 \| nan \| 0.6045 \| 0.9765 \| 0.0 \| 0.4013 \| 0.9601 \| \| 0.3598 \| 115.0 \| 230 \| 0.3918 \| 0.4558 \| 0.7863 \| 0.9627 \| nan \| 0.5939 \| 0.9787 \| 0.0 \| 0.4057 \| 0.9618 \| \| 0.3709 \| 120.0 \| 240 \| 0.3770 \| 0.4572 \| 0.7822 \| 0.9640 \| nan \| 0.5839 \| 0.9805 \| 0.0 \| 0.4086 \| 0.9631 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "dropoff-utcustom-train-SF-RGB-b5_3", "results": []}]}	image-segmentation	sam1120/dropoff-utcustom-train-SF-RGB-b5_3	[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	2024-02-12T14:24:49+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us	dropoff-utcustom-train-SF-RGB-b5\_3 =================================== This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: * Loss: 0.3770 * Mean Iou: 0.4572 * Mean Accuracy: 0.7822 * Overall Accuracy: 0.9640 * Accuracy Unlabeled: nan * Accuracy Dropoff: 0.5839 * Accuracy Undropoff: 0.9805 * Iou Unlabeled: 0.0 * Iou Dropoff: 0.4086 * Iou Undropoff: 0.9631 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-06 * train\_batch\_size: 15 * eval\_batch\_size: 15 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.05 * num\_epochs: 120 ### Training results ### Framework versions * Transformers 4.30.2 * Pytorch 2.0.1+cu117 * Datasets 2.13.1 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ 48, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ -0.10599785298109055, 0.03578374907374382, -0.001980931730940938, 0.11470667272806168, 0.17304065823554993, 0.028470339253544807, 0.11612924188375473, 0.11376673728227615, -0.10989533364772797, 0.030647696927189827, 0.10509989410638809, 0.1421472430229187, 0.015868188813328743, 0.09631344676017761, -0.02112257480621338, -0.3076513111591339, -0.025059247389435768, 0.032038576900959015, -0.08679423481225967, 0.12586866319179535, 0.06506085395812988, -0.1631171852350235, 0.09098067879676819, -0.004193675704300404, -0.2214123159646988, 0.01642296090722084, -0.004632070194929838, -0.03144506737589836, 0.15938067436218262, 0.0233444906771183, 0.11589717864990234, 0.009730372577905655, 0.1131984144449234, -0.20343546569347382, 0.01824348419904709, 0.055901363492012024, -0.004015943501144648, 0.0677042305469513, 0.06474506855010986, 0.0020185213070362806, 0.15129311382770538, -0.10766469687223434, 0.06800296157598495, 0.0015600386541336775, -0.144645094871521, -0.21063238382339478, -0.07281782478094101, 0.023649126291275024, 0.07880499213933945, 0.09665782004594803, -0.005898830480873585, 0.11329825967550278, -0.09313972294330597, 0.11280244588851929, 0.27152836322784424, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b5_4 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2242 - Mean Iou: 0.4568 - Mean Accuracy: 0.7402 - Overall Accuracy: 0.9696 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.4899 - Accuracy Undropoff: 0.9904 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.4016 - Iou Undropoff: 0.9690 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 0.9465 \| 5.0 \| 10 \| 0.9974 \| 0.2695 \| 0.5001 \| 0.6771 \| nan \| 0.3071 \| 0.6931 \| 0.0 \| 0.1261 \| 0.6824 \| \| 0.8558 \| 10.0 \| 20 \| 0.8237 \| 0.3822 \| 0.7119 \| 0.8664 \| nan \| 0.5434 \| 0.8804 \| 0.0 \| 0.2787 \| 0.8678 \| \| 0.7585 \| 15.0 \| 30 \| 0.6801 \| 0.4232 \| 0.7487 \| 0.9194 \| nan \| 0.5625 \| 0.9349 \| 0.0 \| 0.3494 \| 0.9202 \| \| 0.715 \| 20.0 \| 40 \| 0.6076 \| 0.4298 \| 0.7663 \| 0.9232 \| nan \| 0.5952 \| 0.9375 \| 0.0 \| 0.3661 \| 0.9233 \| \| 0.6145 \| 25.0 \| 50 \| 0.5298 \| 0.4398 \| 0.7760 \| 0.9380 \| nan \| 0.5994 \| 0.9527 \| 0.0 \| 0.3819 \| 0.9375 \| \| 0.5355 \| 30.0 \| 60 \| 0.4821 \| 0.4426 \| 0.7749 \| 0.9428 \| nan \| 0.5918 \| 0.9581 \| 0.0 \| 0.3857 \| 0.9422 \| \| 0.4619 \| 35.0 \| 70 \| 0.4266 \| 0.4493 \| 0.7716 \| 0.9524 \| nan \| 0.5743 \| 0.9688 \| 0.0 \| 0.3962 \| 0.9517 \| \| 0.4367 \| 40.0 \| 80 \| 0.3941 \| 0.4519 \| 0.7738 \| 0.9568 \| nan \| 0.5742 \| 0.9734 \| 0.0 \| 0.3997 \| 0.9559 \| \| 0.3839 \| 45.0 \| 90 \| 0.3801 \| 0.4528 \| 0.7796 \| 0.9577 \| nan \| 0.5853 \| 0.9738 \| 0.0 \| 0.4017 \| 0.9567 \| \| 0.3164 \| 50.0 \| 100 \| 0.3549 \| 0.4543 \| 0.7785 \| 0.9608 \| nan \| 0.5797 \| 0.9773 \| 0.0 \| 0.4030 \| 0.9599 \| \| 0.3018 \| 55.0 \| 110 \| 0.3327 \| 0.4573 \| 0.7731 \| 0.9639 \| nan \| 0.5650 \| 0.9812 \| 0.0 \| 0.4087 \| 0.9631 \| \| 0.2646 \| 60.0 \| 120 \| 0.3127 \| 0.4590 \| 0.7703 \| 0.9658 \| nan \| 0.5571 \| 0.9835 \| 0.0 \| 0.4121 \| 0.9650 \| \| 0.2378 \| 65.0 \| 130 \| 0.2958 \| 0.4628 \| 0.7728 \| 0.9673 \| nan \| 0.5607 \| 0.9850 \| 0.0 \| 0.4217 \| 0.9666 \| \| 0.2076 \| 70.0 \| 140 \| 0.2778 \| 0.4675 \| 0.7729 \| 0.9693 \| nan \| 0.5586 \| 0.9871 \| 0.0 \| 0.4340 \| 0.9686 \| \| 0.1951 \| 75.0 \| 150 \| 0.2648 \| 0.4666 \| 0.7719 \| 0.9692 \| nan \| 0.5567 \| 0.9871 \| 0.0 \| 0.4314 \| 0.9685 \| \| 0.1734 \| 80.0 \| 160 \| 0.2522 \| 0.4673 \| 0.7643 \| 0.9703 \| nan \| 0.5397 \| 0.9890 \| 0.0 \| 0.4322 \| 0.9696 \| \| 0.1569 \| 85.0 \| 170 \| 0.2436 \| 0.4660 \| 0.7603 \| 0.9703 \| nan \| 0.5312 \| 0.9894 \| 0.0 \| 0.4282 \| 0.9697 \| \| 0.1691 \| 90.0 \| 180 \| 0.2411 \| 0.4647 \| 0.7624 \| 0.9697 \| nan \| 0.5363 \| 0.9885 \| 0.0 \| 0.4250 \| 0.9690 \| \| 0.1498 \| 95.0 \| 190 \| 0.2335 \| 0.4623 \| 0.7537 \| 0.9699 \| nan \| 0.5179 \| 0.9895 \| 0.0 \| 0.4176 \| 0.9692 \| \| 0.1478 \| 100.0 \| 200 \| 0.2281 \| 0.4585 \| 0.7420 \| 0.9700 \| nan \| 0.4934 \| 0.9906 \| 0.0 \| 0.4062 \| 0.9693 \| \| 0.1407 \| 105.0 \| 210 \| 0.2278 \| 0.4615 \| 0.7501 \| 0.9701 \| nan \| 0.5102 \| 0.9900 \| 0.0 \| 0.4151 \| 0.9694 \| \| 0.1397 \| 110.0 \| 220 \| 0.2305 \| 0.4610 \| 0.7512 \| 0.9698 \| nan \| 0.5129 \| 0.9896 \| 0.0 \| 0.4140 \| 0.9691 \| \| 0.1317 \| 115.0 \| 230 \| 0.2265 \| 0.4576 \| 0.7430 \| 0.9695 \| nan \| 0.4959 \| 0.9901 \| 0.0 \| 0.4038 \| 0.9689 \| \| 0.1548 \| 120.0 \| 240 \| 0.2242 \| 0.4568 \| 0.7402 \| 0.9696 \| nan \| 0.4899 \| 0.9904 \| 0.0 \| 0.4016 \| 0.9690 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "dropoff-utcustom-train-SF-RGB-b5_4", "results": []}]}	image-segmentation	sam1120/dropoff-utcustom-train-SF-RGB-b5_4	[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	2024-02-12T14:25:44+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us	dropoff-utcustom-train-SF-RGB-b5\_4 =================================== This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: * Loss: 0.2242 * Mean Iou: 0.4568 * Mean Accuracy: 0.7402 * Overall Accuracy: 0.9696 * Accuracy Unlabeled: nan * Accuracy Dropoff: 0.4899 * Accuracy Undropoff: 0.9904 * Iou Unlabeled: 0.0 * Iou Dropoff: 0.4016 * Iou Undropoff: 0.9690 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 7e-06 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.05 * num\_epochs: 120 ### Training results ### Framework versions * Transformers 4.30.2 * Pytorch 2.0.1+cu117 * Datasets 2.13.1 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ 48, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ -0.10655785351991653, 0.03439371660351753, -0.0019419043092057109, 0.11647038161754608, 0.17142370343208313, 0.028207143768668175, 0.11584724485874176, 0.1156783401966095, -0.10815674811601639, 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null	null	sklearn	# human-disease-prediction Try it on [🤗 Spaces](https://huggingface.co/spaces/AWeirdDev/human-disease-prediction) ```python import joblib import numpy from huggingface_hub import hf_hub_download model = joblib.load( hf_hub_download("AWeirdDev/human-disease-prediction", "sklearn_model.joblib") ) model.predict( numpy.array([your_data]) ) ``` The `your_data` variable should be a vector of zeros and ones. A zero means "False," and a one means "True." Create a vector that pairs with the following symptoms, then the model will predict what disease it might be. ```python ['itching', 'skin_rash', 'nodal_skin_eruptions', 'continuous_sneezing', 'shivering', 'chills', 'joint_pain', 'stomach_pain', 'acidity', 'ulcers_on_tongue', 'muscle_wasting', 'vomiting', 'burning_micturition', 'spotting_ urination', 'fatigue', 'weight_gain', 'anxiety', 'cold_hands_and_feets', 'mood_swings', 'weight_loss', 'restlessness', 'lethargy', 'patches_in_throat', 'irregular_sugar_level', 'cough', 'high_fever', 'sunken_eyes', 'breathlessness', 'sweating', 'dehydration', 'indigestion', 'headache', 'yellowish_skin', 'dark_urine', 'nausea', 'loss_of_appetite', 'pain_behind_the_eyes', 'back_pain', 'constipation', 'abdominal_pain', 'diarrhoea', 'mild_fever', 'yellow_urine', 'yellowing_of_eyes', 'acute_liver_failure', 'fluid_overload', 'swelling_of_stomach', 'swelled_lymph_nodes', 'malaise', 'blurred_and_distorted_vision', 'phlegm', 'throat_irritation', 'redness_of_eyes', 'sinus_pressure', 'runny_nose', 'congestion', 'chest_pain', 'weakness_in_limbs', 'fast_heart_rate', 'pain_during_bowel_movements', 'pain_in_anal_region', 'bloody_stool', 'irritation_in_anus', 'neck_pain', 'dizziness', 'cramps', 'bruising', 'obesity', 'swollen_legs', 'swollen_blood_vessels', 'puffy_face_and_eyes', 'enlarged_thyroid', 'brittle_nails', 'swollen_extremeties', 'excessive_hunger', 'extra_marital_contacts', 'drying_and_tingling_lips', 'slurred_speech', 'knee_pain', 'hip_joint_pain', 'muscle_weakness', 'stiff_neck', 'swelling_joints', 'movement_stiffness', 'spinning_movements', 'loss_of_balance', 'unsteadiness', 'weakness_of_one_body_side', 'loss_of_smell', 'bladder_discomfort', 'foul_smell_of urine', 'continuous_feel_of_urine', 'passage_of_gases', 'internal_itching', 'toxic_look_(typhos)', 'depression', 'irritability', 'muscle_pain', 'altered_sensorium', 'red_spots_over_body', 'belly_pain', 'abnormal_menstruation', 'dischromic _patches', 'watering_from_eyes', 'increased_appetite', 'polyuria', 'family_history', 'mucoid_sputum', 'rusty_sputum', 'lack_of_concentration', 'visual_disturbances', 'receiving_blood_transfusion', 'receiving_unsterile_injections', 'coma', 'stomach_bleeding', 'distention_of_abdomen', 'history_of_alcohol_consumption', 'fluid_overload.1', 'blood_in_sputum', 'prominent_veins_on_calf', 'palpitations', 'painful_walking', 'pus_filled_pimples', 'blackheads', 'scurring', 'skin_peeling', 'silver_like_dusting', 'small_dents_in_nails', 'inflammatory_nails', 'blister', 'red_sore_around_nose', 'yellow_crust_ooze'] ``` ## Accuracy It has been reported as `1.0` (100%), but I don't believe it.	{"license": "mit", "library_name": "sklearn", "pipeline_tag": "tabular-classification", "model-index": [{"name": "human-disease-prediction", "results": [{"task": {"type": "tabular-classification"}, "dataset": {"name": "human-disease-prediction", "type": "kaggle"}, "metrics": [{"type": "Score", "value": 1, "name": "Score"}]}]}]}	tabular-classification	AWeirdDev/human-disease-prediction	[ "sklearn", "joblib", "tabular-classification", "license:mit", "model-index", "has_space", "region:us" ]	2024-02-12T14:26:07+00:00	[]	[]	TAGS #sklearn #joblib #tabular-classification #license-mit #model-index #has_space #region-us	# human-disease-prediction Try it on Spaces The 'your_data' variable should be a vector of zeros and ones. A zero means "False," and a one means "True." Create a vector that pairs with the following symptoms, then the model will predict what disease it might be. ## Accuracy It has been reported as '1.0' (100%), but I don't believe it.	[ "# human-disease-prediction\n\nTry it on Spaces\n\n\n\nThe 'your_data' variable should be a vector of zeros and ones.\n\nA zero means \"False,\" and a one means \"True.\"\n\nCreate a vector that pairs with the following symptoms, then the model will predict what disease it might be.", "## Accuracy\n\nIt has been reported as '1.0' (100%), but I don't believe it." ]	[ "TAGS\n#sklearn #joblib #tabular-classification #license-mit #model-index #has_space #region-us \n", "# human-disease-prediction\n\nTry it on Spaces\n\n\n\nThe 'your_data' variable should be a vector of zeros and ones.\n\nA zero means \"False,\" and a one means \"True.\"\n\nCreate a vector that pairs with the following symptoms, then the model will predict what disease it might be.", "## Accuracy\n\nIt has been reported as '1.0' (100%), but I don't believe it." ]	[ 33, 73, 23 ]	[ "passage: TAGS\n#sklearn #joblib #tabular-classification #license-mit #model-index #has_space #region-us \n# human-disease-prediction\n\nTry it on Spaces\n\n\n\nThe 'your_data' variable should be a vector of zeros and ones.\n\nA zero means \"False,\" and a one means \"True.\"\n\nCreate a vector that pairs with the following symptoms, then the model will predict what disease it might be.## Accuracy\n\nIt has been reported as '1.0' (100%), but I don't believe it." ]	[ -0.12609298527240753, 0.16294868290424347, -0.0026341478805989027, -0.05762802064418793, 0.042572781443595886, -0.04165844991803169, 0.06791404634714127, 0.08450977504253387, 0.12730439007282257, 0.22155381739139557, 0.302296906709671, 0.10473272949457169, 0.011510465294122696, 0.1297617107629776, -0.11969324201345444, 0.0640820637345314, 0.11187630146741867, 0.05777070298790932, 0.11273381859064102, 0.0859329104423523, -0.0773874819278717, -0.041520338505506516, 0.022089049220085144, 0.0370636060833931, -0.16862384974956512, -0.018390672281384468, 0.12378541380167007, -0.0033962263260036707, 0.06683580577373505, -0.04903369024395943, 0.029274731874465942, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b5_6 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.2315 - Mean Iou: 0.6980 - Mean Accuracy: 0.7503 - Overall Accuracy: 0.9714 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.5091 - Accuracy Undropoff: 0.9915 - Iou Unlabeled: nan - Iou Dropoff: 0.4253 - Iou Undropoff: 0.9708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.0694 \| 5.0 \| 10 \| 1.0190 \| 0.2533 \| 0.6371 \| 0.6676 \| nan \| 0.6038 \| 0.6703 \| 0.0 \| 0.0976 \| 0.6624 \| \| 0.8457 \| 10.0 \| 20 \| 0.7681 \| 0.4126 \| 0.7662 \| 0.9307 \| nan \| 0.5867 \| 0.9457 \| 0.0 \| 0.3078 \| 0.9300 \| \| 0.6049 \| 15.0 \| 30 \| 0.5718 \| 0.4362 \| 0.7527 \| 0.9568 \| nan \| 0.5301 \| 0.9753 \| 0.0 \| 0.3527 \| 0.9561 \| \| 0.5206 \| 20.0 \| 40 \| 0.4181 \| 0.4522 \| 0.7468 \| 0.9662 \| nan \| 0.5076 \| 0.9861 \| 0.0 \| 0.3909 \| 0.9656 \| \| 0.3478 \| 25.0 \| 50 \| 0.3144 \| 0.4603 \| 0.7376 \| 0.9709 \| nan \| 0.4832 \| 0.9920 \| 0.0 \| 0.4105 \| 0.9705 \| \| 0.2023 \| 30.0 \| 60 \| 0.2893 \| 0.4654 \| 0.7612 \| 0.9701 \| nan \| 0.5332 \| 0.9891 \| 0.0 \| 0.4267 \| 0.9695 \| \| 0.1367 \| 35.0 \| 70 \| 0.2351 \| 0.6813 \| 0.7176 \| 0.9715 \| nan \| 0.4407 \| 0.9946 \| nan \| 0.3916 \| 0.9710 \| \| 0.1272 \| 40.0 \| 80 \| 0.2364 \| 0.6824 \| 0.7217 \| 0.9713 \| nan \| 0.4495 \| 0.9939 \| nan \| 0.3941 \| 0.9707 \| \| 0.0929 \| 45.0 \| 90 \| 0.2536 \| 0.4704 \| 0.7617 \| 0.9718 \| nan \| 0.5326 \| 0.9909 \| 0.0 \| 0.4401 \| 0.9712 \| \| 0.0756 \| 50.0 \| 100 \| 0.2253 \| 0.6950 \| 0.7479 \| 0.9710 \| nan \| 0.5045 \| 0.9912 \| nan \| 0.4197 \| 0.9704 \| \| 0.0756 \| 55.0 \| 110 \| 0.2305 \| 0.7043 \| 0.7606 \| 0.9716 \| nan \| 0.5305 \| 0.9908 \| nan \| 0.4375 \| 0.9710 \| \| 0.0721 \| 60.0 \| 120 \| 0.2213 \| 0.6964 \| 0.7448 \| 0.9716 \| nan \| 0.4974 \| 0.9922 \| nan \| 0.4218 \| 0.9711 \| \| 0.0683 \| 65.0 \| 130 \| 0.2338 \| 0.7047 \| 0.7631 \| 0.9715 \| nan \| 0.5359 \| 0.9904 \| nan \| 0.4385 \| 0.9708 \| \| 0.0642 \| 70.0 \| 140 \| 0.2314 \| 0.7046 \| 0.7637 \| 0.9714 \| nan \| 0.5373 \| 0.9902 \| nan \| 0.4385 \| 0.9707 \| \| 0.0623 \| 75.0 \| 150 \| 0.2205 \| 0.7013 \| 0.7565 \| 0.9714 \| nan \| 0.5222 \| 0.9909 \| nan \| 0.4317 \| 0.9708 \| \| 0.0601 \| 80.0 \| 160 \| 0.2209 \| 0.6983 \| 0.7496 \| 0.9715 \| nan \| 0.5075 \| 0.9917 \| nan \| 0.4257 \| 0.9709 \| \| 0.0557 \| 85.0 \| 170 \| 0.2067 \| 0.6982 \| 0.7463 \| 0.9719 \| nan \| 0.5003 \| 0.9923 \| nan \| 0.4252 \| 0.9713 \| \| 0.0571 \| 90.0 \| 180 \| 0.2354 \| 0.7022 \| 0.7603 \| 0.9712 \| nan \| 0.5302 \| 0.9904 \| nan \| 0.4339 \| 0.9706 \| \| 0.0544 \| 95.0 \| 190 \| 0.2240 \| 0.7010 \| 0.7562 \| 0.9714 \| nan \| 0.5215 \| 0.9909 \| nan \| 0.4311 \| 0.9708 \| \| 0.0553 \| 100.0 \| 200 \| 0.2204 \| 0.6968 \| 0.7454 \| 0.9717 \| nan \| 0.4987 \| 0.9922 \| nan \| 0.4225 \| 0.9711 \| \| 0.0525 \| 105.0 \| 210 \| 0.2332 \| 0.7050 \| 0.7625 \| 0.9716 \| nan \| 0.5344 \| 0.9906 \| nan \| 0.4390 \| 0.9710 \| \| 0.0524 \| 110.0 \| 220 \| 0.2371 \| 0.7033 \| 0.7605 \| 0.9715 \| nan \| 0.5304 \| 0.9906 \| nan \| 0.4359 \| 0.9708 \| \| 0.0513 \| 115.0 \| 230 \| 0.2333 \| 0.6987 \| 0.7519 \| 0.9714 \| nan \| 0.5125 \| 0.9913 \| nan \| 0.4267 \| 0.9707 \| \| 0.0537 \| 120.0 \| 240 \| 0.2315 \| 0.6980 \| 0.7503 \| 0.9714 \| nan \| 0.5091 \| 0.9915 \| nan \| 0.4253 \| 0.9708 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "dropoff-utcustom-train-SF-RGB-b5_6", "results": []}]}	image-segmentation	sam1120/dropoff-utcustom-train-SF-RGB-b5_6	[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	2024-02-12T14:26:12+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us	dropoff-utcustom-train-SF-RGB-b5\_6 =================================== This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: * Loss: 0.2315 * Mean Iou: 0.6980 * Mean Accuracy: 0.7503 * Overall Accuracy: 0.9714 * Accuracy Unlabeled: nan * Accuracy Dropoff: 0.5091 * Accuracy Undropoff: 0.9915 * Iou Unlabeled: nan * Iou Dropoff: 0.4253 * Iou Undropoff: 0.9708 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.05 * num\_epochs: 120 ### Training results ### Framework versions * Transformers 4.30.2 * Pytorch 2.0.1+cu117 * Datasets 2.13.1 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ 48, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ -0.10591059178113937, 0.036406707018613815, -0.002041781088337302, 0.11596930027008057, 0.1714659482240677, 0.028318241238594055, 0.11641968786716461, 0.11560021340847015, -0.10877452790737152, 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null	null	fastai	# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed	{"tags": ["fastai"]}	null	maviced/chest_xray	[ "fastai", "has_space", "region:us" ]	2024-02-12T14:26:14+00:00	[]	[]	TAGS #fastai #has_space #region-us	# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed	[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]	[ "TAGS\n#fastai #has_space #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]	[ 13, 20, 79, 3, 6, 12, 8 ]	[ "passage: TAGS\n#fastai #has_space #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]	[ -0.048121724277734756, -0.024616125971078873, 0.002038001548498869, 0.10439170897006989, 0.135872021317482, 0.11887997388839722, 0.07405775785446167, 0.09980081021785736, 0.07783667743206024, 0.02590852417051792, 0.08961158245801926, -0.08088712394237518, 0.08744348585605621, 0.271692156791687, 0.06988707184791565, -0.22761479020118713, 0.04051019623875618, -0.00024903909070417285, 0.08053462207317352, 0.06629016250371933, 0.13507555425167084, -0.05464952811598778, 0.14010503888130188, -0.004088983871042728, -0.19050447642803192, -0.042929794639348984, -0.01773718371987343, -0.02527874894440174, 0.12317648530006409, -0.04744937643408775, 0.05381017178297043, 0.015037551522254944, 0.007565062493085861, -0.07253646105527878, 0.0623294934630394, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b5_7 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1841 - Mean Iou: 0.7025 - Mean Accuracy: 0.7532 - Overall Accuracy: 0.9721 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.5145 - Accuracy Undropoff: 0.9919 - Iou Unlabeled: nan - Iou Dropoff: 0.4336 - Iou Undropoff: 0.9715 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 0.8255 \| 5.0 \| 10 \| 0.7949 \| 0.4128 \| 0.7856 \| 0.9393 \| nan \| 0.6179 \| 0.9533 \| 0.0 \| 0.3007 \| 0.9377 \| \| 0.4434 \| 10.0 \| 20 \| 0.4247 \| 0.4471 \| 0.7066 \| 0.9705 \| nan \| 0.4187 \| 0.9944 \| 0.0 \| 0.3714 \| 0.9700 \| \| 0.2107 \| 15.0 \| 30 \| 0.2726 \| 0.6711 \| 0.7003 \| 0.9715 \| nan \| 0.4046 \| 0.9961 \| nan \| 0.3713 \| 0.9710 \| \| 0.1678 \| 20.0 \| 40 \| 0.2388 \| 0.6801 \| 0.7343 \| 0.9691 \| nan \| 0.4782 \| 0.9904 \| nan \| 0.3917 \| 0.9685 \| \| 0.0972 \| 25.0 \| 50 \| 0.1849 \| 0.6764 \| 0.7096 \| 0.9715 \| nan \| 0.4241 \| 0.9952 \| nan \| 0.3818 \| 0.9709 \| \| 0.0604 \| 30.0 \| 60 \| 0.2019 \| 0.4644 \| 0.7568 \| 0.9704 \| nan \| 0.5239 \| 0.9897 \| 0.0 \| 0.4236 \| 0.9697 \| \| 0.0497 \| 35.0 \| 70 \| 0.1793 \| 0.6838 \| 0.7345 \| 0.9700 \| nan \| 0.4775 \| 0.9914 \| nan \| 0.3983 \| 0.9694 \| \| 0.0492 \| 40.0 \| 80 \| 0.2000 \| 0.4639 \| 0.7567 \| 0.9702 \| nan \| 0.5239 \| 0.9896 \| 0.0 \| 0.4223 \| 0.9695 \| \| 0.0409 \| 45.0 \| 90 \| 0.1893 \| 0.7030 \| 0.7778 \| 0.9696 \| nan \| 0.5687 \| 0.9869 \| nan \| 0.4372 \| 0.9688 \| \| 0.0328 \| 50.0 \| 100 \| 0.1842 \| 0.7040 \| 0.7715 \| 0.9704 \| nan \| 0.5545 \| 0.9885 \| nan \| 0.4382 \| 0.9697 \| \| 0.0332 \| 55.0 \| 110 \| 0.1781 \| 0.7015 \| 0.7563 \| 0.9715 \| nan \| 0.5216 \| 0.9910 \| nan \| 0.4322 \| 0.9709 \| \| 0.0314 \| 60.0 \| 120 \| 0.1732 \| 0.6890 \| 0.7305 \| 0.9717 \| nan \| 0.4675 \| 0.9935 \| nan \| 0.4068 \| 0.9711 \| \| 0.0318 \| 65.0 \| 130 \| 0.1786 \| 0.6971 \| 0.7477 \| 0.9715 \| nan \| 0.5037 \| 0.9918 \| nan \| 0.4233 \| 0.9709 \| \| 0.0291 \| 70.0 \| 140 \| 0.1814 \| 0.7119 \| 0.7687 \| 0.9725 \| nan \| 0.5466 \| 0.9909 \| nan \| 0.4521 \| 0.9718 \| \| 0.0273 \| 75.0 \| 150 \| 0.1755 \| 0.7101 \| 0.7677 \| 0.9722 \| nan \| 0.5446 \| 0.9907 \| nan \| 0.4487 \| 0.9715 \| \| 0.0274 \| 80.0 \| 160 \| 0.1786 \| 0.7006 \| 0.7494 \| 0.9720 \| nan \| 0.5066 \| 0.9922 \| nan \| 0.4297 \| 0.9714 \| \| 0.0248 \| 85.0 \| 170 \| 0.1741 \| 0.7029 \| 0.7526 \| 0.9722 \| nan \| 0.5131 \| 0.9921 \| nan \| 0.4341 \| 0.9716 \| \| 0.0248 \| 90.0 \| 180 \| 0.1832 \| 0.7050 \| 0.7595 \| 0.9719 \| nan \| 0.5278 \| 0.9912 \| nan \| 0.4387 \| 0.9713 \| \| 0.0242 \| 95.0 \| 190 \| 0.1808 \| 0.7028 \| 0.7539 \| 0.9720 \| nan \| 0.5160 \| 0.9918 \| nan \| 0.4341 \| 0.9714 \| \| 0.024 \| 100.0 \| 200 \| 0.1796 \| 0.7022 \| 0.7501 \| 0.9723 \| nan \| 0.5077 \| 0.9925 \| nan \| 0.4327 \| 0.9717 \| \| 0.0231 \| 105.0 \| 210 \| 0.1835 \| 0.7137 \| 0.7731 \| 0.9724 \| nan \| 0.5557 \| 0.9905 \| nan \| 0.4556 \| 0.9717 \| \| 0.0238 \| 110.0 \| 220 \| 0.1823 \| 0.7046 \| 0.7565 \| 0.9721 \| nan \| 0.5214 \| 0.9917 \| nan \| 0.4376 \| 0.9715 \| \| 0.0228 \| 115.0 \| 230 \| 0.1833 \| 0.7009 \| 0.7504 \| 0.9720 \| nan \| 0.5088 \| 0.9921 \| nan \| 0.4305 \| 0.9714 \| \| 0.0255 \| 120.0 \| 240 \| 0.1841 \| 0.7025 \| 0.7532 \| 0.9721 \| nan \| 0.5145 \| 0.9919 \| nan \| 0.4336 \| 0.9715 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "dropoff-utcustom-train-SF-RGB-b5_7", "results": []}]}	image-segmentation	sam1120/dropoff-utcustom-train-SF-RGB-b5_7	[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	2024-02-12T14:26:14+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us	dropoff-utcustom-train-SF-RGB-b5\_7 =================================== This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: * Loss: 0.1841 * Mean Iou: 0.7025 * Mean Accuracy: 0.7532 * Overall Accuracy: 0.9721 * Accuracy Unlabeled: nan * Accuracy Dropoff: 0.5145 * Accuracy Undropoff: 0.9919 * Iou Unlabeled: nan * Iou Dropoff: 0.4336 * Iou Undropoff: 0.9715 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.05 * num\_epochs: 120 ### Training results ### Framework versions * Transformers 4.30.2 * Pytorch 2.0.1+cu117 * Datasets 2.13.1 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ 48, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ -0.10649233311414719, 0.03541902080178261, -0.002008110284805298, 0.11587068438529968, 0.17249611020088196, 0.02851674146950245, 0.11544682830572128, 0.11457392573356628, -0.10972921550273895, 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null	null	transformers	# DPOB-INMTOB-7B DPOB-INMTOB-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [liminerity/Omningotex-7b-slerp](https://huggingface.co/liminerity/Omningotex-7b-slerp) * [eren23/merged-dpo-binarized-NeutrixOmnibe-7B](https://huggingface.co/eren23/merged-dpo-binarized-NeutrixOmnibe-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: liminerity/Omningotex-7b-slerp layer_range: [0, 32] - model: eren23/merged-dpo-binarized-NeutrixOmnibe-7B layer_range: [0, 32] merge_method: slerp base_model: liminerity/Omningotex-7b-slerp parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "paulml/DPOB-INMTOB-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_paulml__DPOB-INMTOB-7B) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|76.21\| \|AI2 Reasoning Challenge (25-Shot)\|73.21\| \|HellaSwag (10-Shot) \|89.00\| \|MMLU (5-Shot) \|64.54\| \|TruthfulQA (0-shot) \|76.60\| \|Winogrande (5-shot) \|84.69\| \|GSM8k (5-shot) \|69.22\|	{"license": "cc-by-nc-4.0", "tags": ["merge", "mergekit", "lazymergekit", "liminerity/Omningotex-7b-slerp", "eren23/merged-dpo-binarized-NeutrixOmnibe-7B"], "base_model": ["liminerity/Omningotex-7b-slerp", "eren23/merged-dpo-binarized-NeutrixOmnibe-7B"], "model-index": [{"name": "DPOB-INMTOB-7B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 73.21, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulml/DPOB-INMTOB-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 89.0, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulml/DPOB-INMTOB-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 64.54, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulml/DPOB-INMTOB-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 76.6}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulml/DPOB-INMTOB-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 84.69, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulml/DPOB-INMTOB-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 69.22, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=paulml/DPOB-INMTOB-7B", "name": "Open LLM Leaderboard"}}]}]}	text-generation	paulml/DPOB-INMTOB-7B	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "liminerity/Omningotex-7b-slerp", "eren23/merged-dpo-binarized-NeutrixOmnibe-7B", "base_model:liminerity/Omningotex-7b-slerp", "base_model:eren23/merged-dpo-binarized-NeutrixOmnibe-7B", "license:cc-by-nc-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T14:27:41+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #liminerity/Omningotex-7b-slerp #eren23/merged-dpo-binarized-NeutrixOmnibe-7B #base_model-liminerity/Omningotex-7b-slerp #base_model-eren23/merged-dpo-binarized-NeutrixOmnibe-7B #license-cc-by-nc-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	DPOB-INMTOB-7B ============== DPOB-INMTOB-7B is a merge of the following models using LazyMergekit: * liminerity/Omningotex-7b-slerp * eren23/merged-dpo-binarized-NeutrixOmnibe-7B Configuration ------------- Usage ----- Open LLM Leaderboard Evaluation Results ======================================= Detailed results can be found here	[]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #liminerity/Omningotex-7b-slerp #eren23/merged-dpo-binarized-NeutrixOmnibe-7B #base_model-liminerity/Omningotex-7b-slerp #base_model-eren23/merged-dpo-binarized-NeutrixOmnibe-7B #license-cc-by-nc-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 155 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #liminerity/Omningotex-7b-slerp #eren23/merged-dpo-binarized-NeutrixOmnibe-7B #base_model-liminerity/Omningotex-7b-slerp #base_model-eren23/merged-dpo-binarized-NeutrixOmnibe-7B #license-cc-by-nc-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.042349670082330704, 0.052338678389787674, -0.007697097957134247, -0.004805305972695351, 0.05051292106509209, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-beans This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2658 - Accuracy: 0.4938 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 4.7295 \| 0.25 \| 10 \| 2.7467 \| 0.1875 \| \| 2.3133 \| 0.5 \| 20 \| 2.1258 \| 0.2437 \| \| 2.031 \| 0.75 \| 30 \| 1.9442 \| 0.3187 \| \| 1.8773 \| 1.0 \| 40 \| 1.6159 \| 0.375 \| \| 1.4132 \| 1.25 \| 50 \| 1.5585 \| 0.4188 \| \| 1.4581 \| 1.5 \| 60 \| 1.5269 \| 0.35 \| \| 1.4697 \| 1.75 \| 70 \| 1.5535 \| 0.3625 \| \| 1.3575 \| 2.0 \| 80 \| 1.3056 \| 0.4375 \| \| 1.0615 \| 2.25 \| 90 \| 1.4774 \| 0.4 \| \| 1.1895 \| 2.5 \| 100 \| 1.2384 \| 0.45 \| \| 1.0659 \| 2.75 \| 110 \| 1.3315 \| 0.4938 \| \| 1.1517 \| 3.0 \| 120 \| 1.1040 \| 0.575 \| \| 0.7957 \| 3.25 \| 130 \| 1.3480 \| 0.4375 \| \| 0.8037 \| 3.5 \| 140 \| 1.2879 \| 0.525 \| \| 1.0157 \| 3.75 \| 150 \| 1.1900 \| 0.5 \| \| 0.7665 \| 4.0 \| 160 \| 1.2039 \| 0.4938 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224", "model-index": [{"name": "vit-base-beans", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.49375, "name": "Accuracy"}]}]}]}	image-classification	nashirab/vit-base-beans	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T14:28:14+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	vit-base-beans ============== This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.2658 * Accuracy: 0.4938 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0002 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 4 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 83, 97, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.12315208464860916, 0.13569286465644836, -0.0016159812221303582, 0.13330447673797607, 0.1586962193250656, 0.013193019665777683, 0.1480993926525116, 0.11931754648685455, -0.05031381919980049, 0.05487760528922081, 0.14189650118350983, 0.10716687887907028, 0.020802505314350128, 0.14494308829307556, -0.04635012522339821, -0.21047723293304443, 0.02009153552353382, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dropoff-utcustom-train-SF-RGB-b5_5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.1911 - Mean Iou: 0.4677 - Mean Accuracy: 0.7472 - Overall Accuracy: 0.9719 - Accuracy Unlabeled: nan - Accuracy Dropoff: 0.5020 - Accuracy Undropoff: 0.9923 - Iou Unlabeled: 0.0 - Iou Dropoff: 0.4318 - Iou Undropoff: 0.9713 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Mean Iou \| Mean Accuracy \| Overall Accuracy \| Accuracy Unlabeled \| Accuracy Dropoff \| Accuracy Undropoff \| Iou Unlabeled \| Iou Dropoff \| Iou Undropoff \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:-------------:\|:----------------:\|:------------------:\|:----------------:\|:------------------:\|:-------------:\|:-----------:\|:-------------:\| \| 1.0685 \| 5.0 \| 10 \| 1.0222 \| 0.2189 \| 0.3725 \| 0.5989 \| nan \| 0.1256 \| 0.6194 \| 0.0 \| 0.0497 \| 0.6070 \| \| 0.9481 \| 10.0 \| 20 \| 0.8419 \| 0.3703 \| 0.6398 \| 0.8451 \| nan \| 0.4159 \| 0.8637 \| 0.0 \| 0.2633 \| 0.8476 \| \| 0.8268 \| 15.0 \| 30 \| 0.7165 \| 0.3949 \| 0.6938 \| 0.8694 \| nan \| 0.5023 \| 0.8853 \| 0.0 \| 0.3136 \| 0.8711 \| \| 0.7573 \| 20.0 \| 40 \| 0.6206 \| 0.4084 \| 0.7186 \| 0.8994 \| nan \| 0.5214 \| 0.9158 \| 0.0 \| 0.3243 \| 0.9010 \| \| 0.636 \| 25.0 \| 50 \| 0.5194 \| 0.4239 \| 0.7253 \| 0.9300 \| nan \| 0.5020 \| 0.9485 \| 0.0 \| 0.3401 \| 0.9316 \| \| 0.5238 \| 30.0 \| 60 \| 0.4507 \| 0.4365 \| 0.7368 \| 0.9461 \| nan \| 0.5085 \| 0.9651 \| 0.0 \| 0.3618 \| 0.9476 \| \| 0.4296 \| 35.0 \| 70 \| 0.4064 \| 0.4410 \| 0.7422 \| 0.9530 \| nan \| 0.5123 \| 0.9721 \| 0.0 \| 0.3683 \| 0.9546 \| \| 0.4105 \| 40.0 \| 80 \| 0.3547 \| 0.4502 \| 0.7467 \| 0.9619 \| nan \| 0.5120 \| 0.9814 \| 0.0 \| 0.3880 \| 0.9627 \| \| 0.3436 \| 45.0 \| 90 \| 0.3304 \| 0.4571 \| 0.7596 \| 0.9644 \| nan \| 0.5361 \| 0.9830 \| 0.0 \| 0.4066 \| 0.9647 \| \| 0.2729 \| 50.0 \| 100 \| 0.2953 \| 0.4614 \| 0.7552 \| 0.9680 \| nan \| 0.5232 \| 0.9873 \| 0.0 \| 0.4163 \| 0.9678 \| \| 0.2546 \| 55.0 \| 110 \| 0.2770 \| 0.4629 \| 0.7579 \| 0.9691 \| nan \| 0.5276 \| 0.9882 \| 0.0 \| 0.4201 \| 0.9686 \| \| 0.2281 \| 60.0 \| 120 \| 0.2591 \| 0.4647 \| 0.7566 \| 0.9702 \| nan \| 0.5235 \| 0.9896 \| 0.0 \| 0.4245 \| 0.9696 \| \| 0.2041 \| 65.0 \| 130 \| 0.2453 \| 0.4657 \| 0.7556 \| 0.9708 \| nan \| 0.5209 \| 0.9903 \| 0.0 \| 0.4269 \| 0.9701 \| \| 0.1772 \| 70.0 \| 140 \| 0.2292 \| 0.4676 \| 0.7542 \| 0.9717 \| nan \| 0.5171 \| 0.9914 \| 0.0 \| 0.4317 \| 0.9711 \| \| 0.169 \| 75.0 \| 150 \| 0.2161 \| 0.4681 \| 0.7520 \| 0.9719 \| nan \| 0.5122 \| 0.9919 \| 0.0 \| 0.4331 \| 0.9713 \| \| 0.1543 \| 80.0 \| 160 \| 0.2111 \| 0.4682 \| 0.7530 \| 0.9715 \| nan \| 0.5147 \| 0.9913 \| 0.0 \| 0.4336 \| 0.9709 \| \| 0.1374 \| 85.0 \| 170 \| 0.1973 \| 0.4659 \| 0.7450 \| 0.9715 \| nan \| 0.4980 \| 0.9921 \| 0.0 \| 0.4268 \| 0.9709 \| \| 0.1523 \| 90.0 \| 180 \| 0.1974 \| 0.4681 \| 0.7501 \| 0.9717 \| nan \| 0.5085 \| 0.9918 \| 0.0 \| 0.4332 \| 0.9711 \| \| 0.1323 \| 95.0 \| 190 \| 0.1928 \| 0.4658 \| 0.7434 \| 0.9717 \| nan \| 0.4944 \| 0.9924 \| 0.0 \| 0.4263 \| 0.9711 \| \| 0.1254 \| 100.0 \| 200 \| 0.1923 \| 0.4671 \| 0.7467 \| 0.9717 \| nan \| 0.5013 \| 0.9921 \| 0.0 \| 0.4301 \| 0.9711 \| \| 0.125 \| 105.0 \| 210 \| 0.1867 \| 0.4637 \| 0.7380 \| 0.9717 \| nan \| 0.4831 \| 0.9929 \| 0.0 \| 0.4201 \| 0.9711 \| \| 0.1239 \| 110.0 \| 220 \| 0.1912 \| 0.4694 \| 0.7520 \| 0.9719 \| nan \| 0.5121 \| 0.9919 \| 0.0 \| 0.4369 \| 0.9713 \| \| 0.1252 \| 115.0 \| 230 \| 0.1913 \| 0.4689 \| 0.7503 \| 0.9720 \| nan \| 0.5085 \| 0.9921 \| 0.0 \| 0.4354 \| 0.9714 \| \| 0.1357 \| 120.0 \| 240 \| 0.1911 \| 0.4677 \| 0.7472 \| 0.9719 \| nan \| 0.5020 \| 0.9923 \| 0.0 \| 0.4318 \| 0.9713 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "dropoff-utcustom-train-SF-RGB-b5_5", "results": []}]}	image-segmentation	sam1120/dropoff-utcustom-train-SF-RGB-b5_5	[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	2024-02-12T14:29:57+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us	dropoff-utcustom-train-SF-RGB-b5\_5 =================================== This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: * Loss: 0.1911 * Mean Iou: 0.4677 * Mean Accuracy: 0.7472 * Overall Accuracy: 0.9719 * Accuracy Unlabeled: nan * Accuracy Dropoff: 0.5020 * Accuracy Undropoff: 0.9923 * Iou Unlabeled: 0.0 * Iou Dropoff: 0.4318 * Iou Undropoff: 0.9713 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 9e-06 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.05 * num\_epochs: 120 ### Training results ### Framework versions * Transformers 4.30.2 * Pytorch 2.0.1+cu117 * Datasets 2.13.1 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ 48, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 9e-06\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ -0.10685057938098907, 0.03626430779695511, -0.001935673295520246, 0.11635380983352661, 0.17187130451202393, 0.027859587222337723, 0.11530084162950516, 0.11533981561660767, -0.10984227806329727, 0.03149544820189476, 0.10524952411651611, 0.143252432346344, 0.01654832810163498, 0.09448825567960739, -0.019710686057806015, -0.3064554035663605, -0.02497927099466324, 0.032077379524707794, -0.0844716802239418, 0.12509827315807343, 0.06434638053178787, -0.16338524222373962, 0.09151484072208405, -0.0040703509002923965, -0.2204686552286148, 0.016166582703590393, -0.005493758711963892, -0.030991580337285995, 0.1595882922410965, 0.023075681179761887, 0.1162770614027977, 0.00943325087428093, 0.11400919407606125, -0.2004028707742691, 0.0177561417222023, 0.05652876943349838, -0.004191652871668339, 0.06761171668767929, 0.06547973304986954, 0.0025243882555514574, 0.15156961977481842, -0.10634521394968033, 0.0674901083111763, 0.001579924370162189, -0.14478418231010437, -0.2124166041612625, -0.0751550942659378, 0.02231522649526596, 0.07801754772663116, 0.09615685790777206, -0.005620093550533056, 0.11418497562408447, -0.09215269982814789, 0.11302897334098816, 0.2677992880344391, 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null	null	null	# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="xncy/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.52 +/- 2.73", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	xncy/Taxi-v3	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	2024-02-12T14:30:50+00:00	[]	[]	TAGS #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us	# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage	[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 32, 33 ]	[ "passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 0.048862796276807785, -0.16549694538116455, -0.005485367961227894, 0.02960980497300625, 0.1345081776380539, -0.01784728653728962, 0.11895976960659027, 0.07759871333837509, -0.07461097836494446, -0.055395450443029404, 0.1418241262435913, 0.09088201075792313, 0.055222880095243454, 0.05699880048632622, 0.09511256217956543, 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null	null	null	# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="hugo-massonnat/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	{"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	hugo-massonnat/q-FrozenLake-v1-4x4-noSlippery	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	2024-02-12T14:37:00+00:00	[]	[]	TAGS #FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us	# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage	[ "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ "TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ 40, 39 ]	[ "passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ 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null	null	null	# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="hugo-massonnat/taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	hugo-massonnat/taxi-v3	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	2024-02-12T14:40:17+00:00	[]	[]	TAGS #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us	# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage	[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 32, 33 ]	[ "passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 0.048862796276807785, -0.16549694538116455, -0.005485367961227894, 0.02960980497300625, 0.1345081776380539, -0.01784728653728962, 0.11895976960659027, 0.07759871333837509, -0.07461097836494446, -0.055395450443029404, 0.1418241262435913, 0.09088201075792313, 0.055222880095243454, 0.05699880048632622, 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # electra-base-generator-rank64 This model is a fine-tuned version of [google/electra-base-generator](https://huggingface.co/google/electra-base-generator) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.2951 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 8.2066 \| 1.0 \| 179 \| 3.8785 \| \| 3.6834 \| 2.0 \| 358 \| 3.3549 \| \| 3.4351 \| 3.0 \| 537 \| 3.2951 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu118 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/electra-base-generator", "model-index": [{"name": "electra-base-generator-rank64", "results": []}]}	null	alitolga/electra-base-generator-rank64	[ "safetensors", "generated_from_trainer", "base_model:google/electra-base-generator", "license:apache-2.0", "region:us" ]	2024-02-12T14:41:00+00:00	[]	[]	TAGS #safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us	electra-base-generator-rank64 ============================= This model is a fine-tuned version of google/electra-base-generator on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.2951 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu118 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 40, 98, 4, 33 ]	[ "passage: TAGS\n#safetensors #generated_from_trainer #base_model-google/electra-base-generator #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu118\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.0986127182841301, 0.018834460526704788, -0.0008125067106448114, 0.10979532450437546, 0.18232838809490204, 0.014075469225645065, 0.14945824444293976, 0.05513402447104454, -0.11618620902299881, 0.027743428945541382, 0.11155061423778534, 0.12338999658823013, -0.0060639819130301476, 0.12559986114501953, -0.0439465306699276, -0.19480036199092865, 0.025722401216626167, 0.0023020326625555754, -0.06931312382221222, 0.12410648912191391, 0.10072977840900421, -0.1471579670906067, 0.0789017304778099, -0.0045234463177621365, -0.218098446726799, 0.03776150941848755, 0.037302516400814056, -0.06900284439325333, 0.1463816612958908, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	tommymarto/LernnaviBERT_mcqbert1_students_answers_384_lstm_seq_len_30	[ "transformers", "safetensors", "bert", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T14:41:30+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 33, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05835729464888573, 0.21513818204402924, -0.0027643628418445587, 0.027697166427969933, 0.12558044493198395, -0.00036080856807529926, 0.038943830877542496, 0.12901438772678375, -0.01060954574495554, 0.1100858673453331, 0.03811120614409447, 0.09515609592199326, 0.09883695095777512, 0.1663336604833603, 0.04276633635163307, -0.21661408245563507, 0.003279293654486537, -0.08966897428035736, 0.019332116469740868, 0.10749275237321854, 0.13046206533908844, -0.10735081136226654, 0.07876921445131302, -0.03911958634853363, -0.01563864015042782, -0.002511978382244706, -0.09296175837516785, -0.07015316188335419, 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null	null	transformers	A question generation model trained on `SQuAD` dataset. Example usage: ```py from transformers import BartConfig, BartForConditionalGeneration, BartTokenizer model_name = "alinet/bart-base-squad-qg" tokenizer = BartTokenizer.from_pretrained(model_name) model = BartForConditionalGeneration.from_pretrained(model_name) def run_model(input_string, generator_args): input_ids = tokenizer.encode(input_string, return_tensors="pt") res = model.generate(input_ids, generator_args) output = tokenizer.batch_decode(res, skip_special_tokens=True) print(output) run_model("Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.", max_length=32, num_beams=4) # ['What is the Stanford Question Answering Dataset?'] ```	{"language": ["en"], "datasets": ["squad"], "model-index": [{"name": "alinet/bart-base-squad-qg", "results": [{"task": {"type": "text2text-generation", "name": "Question Generation"}, "dataset": {"name": "MRQA", "type": "mrqa"}, "metrics": [{"type": "bertscore", "value": 0.6818813686383791, "name": "BERTScore F1"}, {"type": "bertscore", "value": 0.6918038470502067, "name": "BERTScore Precision"}, {"type": "bertscore", "value": 0.6755750492952126, "name": "BERTScore Recall"}]}, {"task": {"type": "text2text-generation", "name": "Question Generation"}, "dataset": {"name": "Spoken-SQuAD", "type": "alinet/spoken_squad"}, "metrics": [{"type": "bertscore", "value": 0.6037420180342389, "name": "BERTScore F1"}, {"type": "bertscore", "value": 0.5958670210949816, "name": "BERTScore Precision"}, {"type": "bertscore", "value": 0.6153761332016946, "name": "BERTScore Recall"}]}]}]}	text2text-generation	alinet/bart-base-squad-qg	[ "transformers", "pytorch", "bart", "text2text-generation", "en", "dataset:squad", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T14:44:38+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bart #text2text-generation #en #dataset-squad #model-index #autotrain_compatible #endpoints_compatible #region-us	A question generation model trained on 'SQuAD' dataset. Example usage:	[]	[ "TAGS\n#transformers #pytorch #bart #text2text-generation #en #dataset-squad #model-index #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ 50 ]	[ "passage: TAGS\n#transformers #pytorch #bart #text2text-generation #en #dataset-squad #model-index #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ -0.08805952966213226, 0.07101669907569885, -0.005594032350927591, 0.013208555057644844, 0.1618894785642624, 0.05533156171441078, 0.14796429872512817, 0.11948926001787186, 0.05454312264919281, -0.04363541677594185, 0.13875102996826172, 0.19659936428070068, 0.009250272996723652, 0.17553092539310455, -0.08571828156709671, -0.244764506816864, 0.066041000187397, 0.07959256321191788, 0.011364615522325039, 0.12045249342918396, 0.10274779051542282, -0.08544951677322388, 0.08665027469396591, -0.027629993855953217, -0.16321735084056854, 0.025082305073738098, 0.025790126994252205, -0.10566488653421402, 0.1052493080496788, 0.0425558015704155, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # melita1mu This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_hr_fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.5013 - Wer: 45.5961 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.0204 \| 4.17 \| 1000 \| 0.4216 \| 36.3580 \| \| 0.0017 \| 8.33 \| 2000 \| 0.4697 \| 37.7222 \| \| 0.0008 \| 12.5 \| 3000 \| 0.4922 \| 39.6015 \| \| 0.0006 \| 16.67 \| 4000 \| 0.5013 \| 45.5961 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"language": ["hr"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["google/fleurs"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "melita1mu", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "common_voice_hr_fleurs", "type": "google/fleurs", "config": "hr_hr", "split": "test", "args": "config: hr, split: test"}, "metrics": [{"type": "wer", "value": 45.596060228687875, "name": "Wer"}]}]}]}	automatic-speech-recognition	Luka041/melita1mu	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hr", "dataset:google/fleurs", "base_model:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2024-02-12T14:45:03+00:00	[]	[ "hr" ]	TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hr #dataset-google/fleurs #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us	melita1mu ========= This model is a fine-tuned version of openai/whisper-small on the common\_voice\_hr\_fleurs dataset. It achieves the following results on the evaluation set: * Loss: 0.5013 * Wer: 45.5961 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * training\_steps: 4000 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hr #dataset-google/fleurs #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 93, 130, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hr #dataset-google/fleurs #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 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null	null	keras	## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| weight_decay \| None \| \| clipnorm \| None \| \| global_clipnorm \| None \| \| clipvalue \| None \| \| use_ema \| False \| \| ema_momentum \| 0.99 \| \| ema_overwrite_frequency \| None \| \| jit_compile \| True \| \| is_legacy_optimizer \| False \| \| learning_rate \| 3.7500001781154424e-05 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \|	{"library_name": "keras", "tags": ["time-series"]}	null	kadasterdst/wrz-test	[ "keras", "time-series", "region:us" ]	2024-02-12T14:45:47+00:00	[]	[]	TAGS #keras #time-series #region-us	Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training:	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:" ]	[ "TAGS\n#keras #time-series #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:" ]	[ 14, 18 ]	[ "passage: TAGS\n#keras #time-series #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:" ]	[ 0.03931060805916786, -0.10588671267032623, -0.005607119761407375, -0.009519034996628761, 0.1737571358680725, 0.10446348786354065, 0.1143084466457367, 0.05416339635848999, -0.09599184989929199, -0.020420847460627556, 0.15546315908432007, 0.0800081416964531, -0.04289175942540169, -0.03028191812336445, 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null	null	pruna-engine	<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching. - *How does the model quality change?* The quality of the model output might slightly vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. ## Setup You can run the smashed model with these steps: 0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`. 1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install. ```bash pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/ ``` 3. Download the model files using one of these three options. - Option 1 - Use command line interface (CLI): ```bash mkdir Linaqruf-animagine-xl-turbo-tiny-green-smashed huggingface-cli download PrunaAI/Linaqruf-animagine-xl-turbo-tiny-green-smashed --local-dir Linaqruf-animagine-xl-turbo-tiny-green-smashed --local-dir-use-symlinks False ``` - Option 2 - Use Python: ```python import subprocess repo_name = "Linaqruf-animagine-xl-turbo-tiny-green-smashed" subprocess.run(["mkdir", repo_name]) subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"]) ``` - Option 3 - Download them manually on the HuggingFace model page. 3. Load & run the model. ```python from pruna_engine.PrunaModel import PrunaModel model_path = "Linaqruf-animagine-xl-turbo-tiny-green-smashed/model" # Specify the downloaded model path. smashed_model = PrunaModel.load_model(model_path) # Load the model. smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input of. ``` ## Configurations The configuration info are in `config.json`. ## Credits & License We follow the same license as the original model. Please check the license of the original model Linaqruf/animagine-xl before using this model which provided the base model. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).	{"license": "apache-2.0", "library_name": "pruna-engine", "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"}	null	PrunaAI/Linaqruf-animagine-xl-turbo-tiny-green-smashed	[ "pruna-engine", "license:apache-2.0", "region:us" ]	2024-02-12T14:45:54+00:00	[]	[]	TAGS #pruna-engine #license-apache-2.0 #region-us	<div style="width: auto; margin-left: auto; margin-right: auto"> <a href="URL target="_blank" rel="noopener noreferrer"> <img src="https://i.URL alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> ![Twitter](URL ![GitHub](URL ![LinkedIn](URL ![Discord](URL # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next here. - Request access to easily compress your own AI models here. - Read the documentations to know more here - Join Pruna AI community on Discord here to share feedback/suggestions or get help. ## Results !image info Frequently Asked Questions - How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching. - How does the model quality change? The quality of the model output might slightly vary compared to the base model. - How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed. - What is the naming convention for Pruna Huggingface models? We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here. - What are "first" metrics? Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. ## Setup You can run the smashed model with these steps: 0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'. 1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install. 3. Download the model files using one of these three options. - Option 1 - Use command line interface (CLI): - Option 2 - Use Python: - Option 3 - Download them manually on the HuggingFace model page. 3. Load & run the model. ## Configurations The configuration info are in 'URL'. ## Credits & License We follow the same license as the original model. Please check the license of the original model Linaqruf/animagine-xl before using this model which provided the base model. ## Want to compress other models? - Contact us and tell us which model to compress next here. - Request access to easily compress your own AI models here.	[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.\n- How does the model quality change? The quality of the model output might slightly vary compared to the base model.\n- How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed.\n- What is the naming convention for Pruna Huggingface models? We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here.\n- What are \"first\" metrics? Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'.\n1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install.\n \n3. Download the model files using one of these three options. \n - Option 1 - Use command line interface (CLI):\n \n - Option 2 - Use Python:\n \n - Option 3 - Download them manually on the HuggingFace model page.\n3. Load & run the model.", "## Configurations\n\nThe configuration info are in 'URL'.", "## Credits & License\n\nWe follow the same license as the original model. Please check the license of the original model Linaqruf/animagine-xl before using this model which provided the base model.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]	[ "TAGS\n#pruna-engine #license-apache-2.0 #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.\n- How does the model quality change? The quality of the model output might slightly vary compared to the base model.\n- How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed.\n- What is the naming convention for Pruna Huggingface models? We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here.\n- What are \"first\" metrics? Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'.\n1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install.\n \n3. Download the model files using one of these three options. \n - Option 1 - Use command line interface (CLI):\n \n - Option 2 - Use Python:\n \n - Option 3 - Download them manually on the HuggingFace model page.\n3. Load & run the model.", "## Configurations\n\nThe configuration info are in 'URL'.", "## Credits & License\n\nWe follow the same license as the original model. Please check the license of the original model Linaqruf/animagine-xl before using this model which provided the base model.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]	[ 19, 92, 402, 155, 13, 41, 36 ]	[ "passage: TAGS\n#pruna-engine #license-apache-2.0 #region-us \n# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help." ]	[ -0.03573288768529892, 0.13100412487983704, -0.001540288794785738, 0.022444402799010277, 0.10067341476678848, 0.055228229612112045, 0.07351797074079514, 0.10892018675804138, 0.048860158771276474, 0.008610251359641552, 0.1138949766755104, 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null	null	pruna-engine	<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/CP4VSgck) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching. - *How does the model quality change?* The quality of the model output might slightly vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. ## Setup You can run the smashed model with these steps: 0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`. 1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install. ```bash pip install pruna-engine[gpu]==0.6.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/ ``` 3. Download the model files using one of these three options. - Option 1 - Use command line interface (CLI): ```bash mkdir SG161222-RealVisXL_V3.0_Turbo-turbo-tiny-green-smashed huggingface-cli download PrunaAI/SG161222-RealVisXL_V3.0_Turbo-turbo-tiny-green-smashed --local-dir SG161222-RealVisXL_V3.0_Turbo-turbo-tiny-green-smashed --local-dir-use-symlinks False ``` - Option 2 - Use Python: ```python import subprocess repo_name = "SG161222-RealVisXL_V3.0_Turbo-turbo-tiny-green-smashed" subprocess.run(["mkdir", repo_name]) subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"]) ``` - Option 3 - Download them manually on the HuggingFace model page. 3. Load & run the model. ```python from pruna_engine.PrunaModel import PrunaModel model_path = "SG161222-RealVisXL_V3.0_Turbo-turbo-tiny-green-smashed/model" # Specify the downloaded model path. smashed_model = PrunaModel.load_model(model_path) # Load the model. smashed_model(prompt='Beautiful fruits in trees', height=1024, width=1024)[0][0] # Run the model where x is the expected input of. ``` ## Configurations The configuration info are in `config.json`. ## Credits & License We follow the same license as the original model. Please check the license of the original model SG161222/RealVisXL_V3.0_Turbo before using this model which provided the base model. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).	{"license": "apache-2.0", "library_name": "pruna-engine", "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"}	null	PrunaAI/SG161222-RealVisXL_V3.0_Turbo-turbo-tiny-green-smashed	[ "pruna-engine", "license:apache-2.0", "region:us" ]	2024-02-12T14:48:37+00:00	[]	[]	TAGS #pruna-engine #license-apache-2.0 #region-us	<div style="width: auto; margin-left: auto; margin-right: auto"> <a href="URL target="_blank" rel="noopener noreferrer"> <img src="https://i.URL alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> ![Twitter](URL ![GitHub](URL ![LinkedIn](URL ![Discord](URL # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next here. - Request access to easily compress your own AI models here. - Read the documentations to know more here - Join Pruna AI community on Discord here to share feedback/suggestions or get help. ## Results !image info Frequently Asked Questions - How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching. - How does the model quality change? The quality of the model output might slightly vary compared to the base model. - How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed. - What is the naming convention for Pruna Huggingface models? We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here. - What are "first" metrics? Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. ## Setup You can run the smashed model with these steps: 0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'. 1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install. 3. Download the model files using one of these three options. - Option 1 - Use command line interface (CLI): - Option 2 - Use Python: - Option 3 - Download them manually on the HuggingFace model page. 3. Load & run the model. ## Configurations The configuration info are in 'URL'. ## Credits & License We follow the same license as the original model. Please check the license of the original model SG161222/RealVisXL_V3.0_Turbo before using this model which provided the base model. ## Want to compress other models? - Contact us and tell us which model to compress next here. - Request access to easily compress your own AI models here.	[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.\n- How does the model quality change? The quality of the model output might slightly vary compared to the base model.\n- How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed.\n- What is the naming convention for Pruna Huggingface models? We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here.\n- What are \"first\" metrics? Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'.\n1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install.\n \n3. Download the model files using one of these three options. \n - Option 1 - Use command line interface (CLI):\n \n - Option 2 - Use Python:\n \n - Option 3 - Download them manually on the HuggingFace model page.\n3. Load & run the model.", "## Configurations\n\nThe configuration info are in 'URL'.", "## Credits & License\n\nWe follow the same license as the original model. Please check the license of the original model SG161222/RealVisXL_V3.0_Turbo before using this model which provided the base model.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]	[ "TAGS\n#pruna-engine #license-apache-2.0 #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- How does the compression work? The model is compressed by combining xformers, triton, jit, cuda graphs, tiling, and step caching.\n- How does the model quality change? The quality of the model output might slightly vary compared to the base model.\n- How is the model efficiency evaluated? These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- What is the model format? We used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation here if needed.\n- What is the naming convention for Pruna Huggingface models? We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- How to compress my own models? You can request premium access to more compression methods and tech support for your specific use-cases here.\n- What are \"first\" metrics? Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with 'nvcc --version' and install with 'conda install nvidia/label/cuda-12.1.0::cuda'.\n1. Install the 'pruna-engine' available here on Pypi. It might take up to 15 minutes to install.\n \n3. Download the model files using one of these three options. \n - Option 1 - Use command line interface (CLI):\n \n - Option 2 - Use Python:\n \n - Option 3 - Download them manually on the HuggingFace model page.\n3. Load & run the model.", "## Configurations\n\nThe configuration info are in 'URL'.", "## Credits & License\n\nWe follow the same license as the original model. Please check the license of the original model SG161222/RealVisXL_V3.0_Turbo before using this model which provided the base model.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]	[ 19, 92, 402, 155, 13, 46, 36 ]	[ "passage: TAGS\n#pruna-engine #license-apache-2.0 #region-us \n# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help." ]	[ -0.03573288768529892, 0.13100412487983704, -0.001540288794785738, 0.022444402799010277, 0.10067341476678848, 0.055228229612112045, 0.07351797074079514, 0.10892018675804138, 0.048860158771276474, 0.008610251359641552, 0.1138949766755104, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text2text-generation	elderberry17/base-pokemon-finetuned	[ "transformers", "safetensors", "blip", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T14:49:11+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #blip #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #blip #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 49, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #blip #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.07800597697496414, 0.15627166628837585, -0.003729335032403469, 0.025803571566939354, 0.11313135921955109, 0.0071767764165997505, 0.07711801677942276, 0.10639526695013046, -0.01925649866461754, 0.12445364147424698, 0.04081743583083153, 0.09858276695013046, 0.10915220528841019, 0.20282134413719177, -0.0036864476278424263, -0.20051886141300201, 0.05712289363145828, -0.1150035560131073, 0.01867586188018322, 0.1279556006193161, 0.13733862340450287, -0.11053169518709183, 0.06700434535741806, -0.04425474628806114, -0.026135969907045364, -0.03221189230680466, -0.05797227844595909, -0.05051439255475998, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # furina_seed42_eng_amh_esp_roman This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0144 - Spearman Corr: 0.8461 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 128 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Spearman Corr \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------------:\| \| No log \| 0.59 \| 200 \| 0.0299 \| 0.6782 \| \| No log \| 1.18 \| 400 \| 0.0251 \| 0.7278 \| \| No log \| 1.76 \| 600 \| 0.0202 \| 0.7493 \| \| 0.0425 \| 2.35 \| 800 \| 0.0194 \| 0.7584 \| \| 0.0425 \| 2.94 \| 1000 \| 0.0184 \| 0.7737 \| \| 0.0425 \| 3.53 \| 1200 \| 0.0189 \| 0.7734 \| \| 0.0184 \| 4.12 \| 1400 \| 0.0180 \| 0.7906 \| \| 0.0184 \| 4.71 \| 1600 \| 0.0188 \| 0.7909 \| \| 0.0184 \| 5.29 \| 1800 \| 0.0171 \| 0.7971 \| \| 0.0184 \| 5.88 \| 2000 \| 0.0165 \| 0.8055 \| \| 0.0134 \| 6.47 \| 2200 \| 0.0162 \| 0.8059 \| \| 0.0134 \| 7.06 \| 2400 \| 0.0164 \| 0.8085 \| \| 0.0134 \| 7.65 \| 2600 \| 0.0169 \| 0.8131 \| \| 0.0098 \| 8.24 \| 2800 \| 0.0169 \| 0.8171 \| \| 0.0098 \| 8.82 \| 3000 \| 0.0158 \| 0.8169 \| \| 0.0098 \| 9.41 \| 3200 \| 0.0152 \| 0.8201 \| \| 0.0073 \| 10.0 \| 3400 \| 0.0165 \| 0.8197 \| \| 0.0073 \| 10.59 \| 3600 \| 0.0150 \| 0.8234 \| \| 0.0073 \| 11.18 \| 3800 \| 0.0152 \| 0.8284 \| \| 0.0073 \| 11.76 \| 4000 \| 0.0141 \| 0.8338 \| \| 0.0059 \| 12.35 \| 4200 \| 0.0144 \| 0.8315 \| \| 0.0059 \| 12.94 \| 4400 \| 0.0147 \| 0.8348 \| \| 0.0059 \| 13.53 \| 4600 \| 0.0157 \| 0.8327 \| \| 0.0049 \| 14.12 \| 4800 \| 0.0147 \| 0.8379 \| \| 0.0049 \| 14.71 \| 5000 \| 0.0149 \| 0.8365 \| \| 0.0049 \| 15.29 \| 5200 \| 0.0142 \| 0.8360 \| \| 0.0049 \| 15.88 \| 5400 \| 0.0140 \| 0.8409 \| \| 0.0042 \| 16.47 \| 5600 \| 0.0135 \| 0.8414 \| \| 0.0042 \| 17.06 \| 5800 \| 0.0141 \| 0.8410 \| \| 0.0042 \| 17.65 \| 6000 \| 0.0144 \| 0.8402 \| \| 0.0037 \| 18.24 \| 6200 \| 0.0151 \| 0.8435 \| \| 0.0037 \| 18.82 \| 6400 \| 0.0140 \| 0.8431 \| \| 0.0037 \| 19.41 \| 6600 \| 0.0140 \| 0.8454 \| \| 0.0033 \| 20.0 \| 6800 \| 0.0136 \| 0.8453 \| \| 0.0033 \| 20.59 \| 7000 \| 0.0137 \| 0.8446 \| \| 0.0033 \| 21.18 \| 7200 \| 0.0144 \| 0.8461 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"tags": ["generated_from_trainer"], "base_model": "yihongLiu/furina", "model-index": [{"name": "furina_seed42_eng_amh_esp_roman", "results": []}]}	text-classification	Shijia/furina_seed42_eng_amh_esp_roman	[ "transformers", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:yihongLiu/furina", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T14:50:32+00:00	[]	[]	TAGS #transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us	furina\_seed42\_eng\_amh\_esp\_roman ==================================== This model is a fine-tuned version of yihongLiu/furina on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0144 * Spearman Corr: 0.8461 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 32 * eval\_batch\_size: 128 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 30 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 60, 141, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-yihongLiu/furina #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 128\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.14007613062858582, 0.12448705732822418, 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null	null	transformers	Model description: Model: pgajo/mbert-xlwa-en-it Dataset: TASTEset Unshuffled ratio: ['1'] Shuffled ratio: ['0'] Best exact match epoch: 8 Best exact match: 98.07 Best epoch: 8 Drop duplicates: ['1'] Max epochs = 10 Optimizer lr = 3e-05 Optimizer eps = 1e-08 Batch size = 32 Dataset path = pgajo/EW-TT-PE_U1_S0_DROP1_mbert Results \| epoch \| train_loss \| train_f1 \| train_exact \| dev_loss \| dev_f1 \| dev_exact \| test_loss \| test_f1 \| test_exact \| \|--------:\|-------------:\|-----------:\|--------------:\|-----------:\|---------:\|------------:\|------------:\|----------:\|-------------:\| \| 1 \| 0.42 \| 88.03 \| 77.33 \| 0.08 \| 97.54 \| 95.58 \| 0 \| 0 \| 0 \| \| 2 \| 0.05 \| 99.22 \| 97.72 \| 0.05 \| 98.33 \| 97.24 \| 0 \| 0 \| 0 \| \| 3 \| 0.02 \| 99.66 \| 99.1 \| 0.07 \| 98.37 \| 96.69 \| 0 \| 0 \| 0 \| \| 4 \| 0.02 \| 99.61 \| 99.1 \| 0.06 \| 98.43 \| 96.96 \| 0 \| 0 \| 0 \| \| 5 \| 0.01 \| 99.69 \| 99.31 \| 0.05 \| 98.72 \| 97.51 \| 0 \| 0 \| 0 \| \| 6 \| 0.01 \| 99.75 \| 99.38 \| 0.03 \| 98.62 \| 97.24 \| 0 \| 0 \| 0 \| \| 7 \| 0.01 \| 99.97 \| 99.86 \| 0.04 \| 98.83 \| 97.79 \| 0 \| 0 \| 0 \| \| 8 \| 0 \| 99.91 \| 99.86 \| 0.04 \| 98.98 \| 98.07 \| 0 \| 0 \| 0 \| \| 9 \| 0 \| 99.88 \| 99.79 \| 0.03 \| 99.22 \| 98.07 \| 0 \| 0 \| 0 \| \| 10 \| 0 \| 99.88 \| 99.72 \| 0.05 \| 98.84 \| 97.51 \| 0 \| 0 \| 0 \|	{}	question-answering	pgajo/mbert-xlwa-en-it_EW-TT-PE_U1_S0_DROP1_mbert_E8_DEV98.0	[ "transformers", "safetensors", "bert", "question-answering", "endpoints_compatible", "region:us" ]	2024-02-12T14:50:50+00:00	[]	[]	TAGS #transformers #safetensors #bert #question-answering #endpoints_compatible #region-us	Model description: ``` Model: pgajo/mbert-xlwa-en-it Dataset: TASTEset Unshuffled ratio: ['1'] Shuffled ratio: ['0'] Best exact match epoch: 8 Best exact match: 98.07 Best epoch: 8 Drop duplicates: ['1'] Max epochs = 10 Optimizer lr = 3e-05 Optimizer eps = 1e-08 Batch size = 32 Dataset path = pgajo/EW-TT-PE_U1_S0_DROP1_mbert ``` Results	[]	[ "TAGS\n#transformers #safetensors #bert #question-answering #endpoints_compatible #region-us \n" ]	[ 30 ]	[ "passage: TAGS\n#transformers #safetensors #bert #question-answering #endpoints_compatible #region-us \n" ]	[ -0.03100396879017353, 0.011429967358708382, -0.009655450470745564, -0.0477571114897728, 0.071015864610672, 0.001686002011410892, 0.08008057624101639, 0.05985769256949425, 0.11401950567960739, 0.02590048313140869, 0.1903941035270691, 0.16566626727581024, -0.07932274788618088, 0.015106523409485817, -0.13172350823879242, -0.13182127475738525, 0.11529869586229324, 0.03778080269694328, -0.03543904423713684, 0.10329030454158783, 0.05029234290122986, -0.12624382972717285, 0.04368755966424942, -0.06763096153736115, -0.062081653624773026, 0.06668882071971893, 0.04820772260427475, -0.08198674768209457, 0.13085918128490448, 0.03362511843442917, 0.2047542929649353, 0.04677434265613556, -0.1182841956615448, -0.21163156628608704, 0.03874710574746132, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	tommymarto/LernnaviBERT_mcqbert1_students_answers_384_lstm_seq_len_10	[ "transformers", "safetensors", "bert", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T14:54:40+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 33, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05835729464888573, 0.21513818204402924, -0.0027643628418445587, 0.027697166427969933, 0.12558044493198395, -0.00036080856807529926, 0.038943830877542496, 0.12901438772678375, -0.01060954574495554, 0.1100858673453331, 0.03811120614409447, 0.09515609592199326, 0.09883695095777512, 0.1663336604833603, 0.04276633635163307, -0.21661408245563507, 0.003279293654486537, -0.08966897428035736, 0.019332116469740868, 0.10749275237321854, 0.13046206533908844, -0.10735081136226654, 0.07876921445131302, -0.03911958634853363, -0.01563864015042782, -0.002511978382244706, -0.09296175837516785, -0.07015316188335419, 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null	null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "results", "results": []}]}	null	Kavin0211/results	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:microsoft/phi-2", "license:mit", "region:us" ]	2024-02-12T14:54:51+00:00	[]	[]	TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us	# results This model is a fine-tuned version of microsoft/phi-2 on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	[ "# results\n\nThis model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 500\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n", "# results\n\nThis model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 500\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ 45, 25, 6, 12, 8, 3, 140, 4, 39 ]	[ "passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n# results\n\nThis model is a fine-tuned version of microsoft/phi-2 on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: constant\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 500\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ -0.12164483219385147, 0.12909680604934692, -0.0035190486814826727, 0.06941638141870499, 0.10261702537536621, 0.017325563356280327, 0.07567822933197021, 0.14053556323051453, -0.043014463037252426, 0.12302422523498535, 0.09354069083929062, 0.04600201174616814, 0.07442367821931839, 0.2031952291727066, -0.0050381748005747795, -0.24036844074726105, 0.03340320289134979, -0.052628498524427414, -0.03819865733385086, 0.09494207054376602, 0.0974312275648117, -0.08965053409337997, 0.054942794144153595, 0.00014700624160468578, -0.09203195571899414, -0.02104009874165058, -0.04252941906452179, -0.04383267089724541, 0.08410395681858063, -0.0017905086278915405, 0.05941466614603996, 0.027177434414625168, 0.12094796448945999, -0.23564887046813965, 0.0003176175814587623, 0.0755743682384491, 0.050007399171590805, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	nchen909/llama2_7b_sft_52580	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T14:55:48+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 56, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06061961501836777, 0.15481999516487122, -0.004844071343541145, 0.02074851468205452, 0.0983177199959755, 0.007407687604427338, 0.07119518518447876, 0.11185134947299957, -0.023851769044995308, 0.1167980208992958, 0.031993988901376724, 0.09781743586063385, 0.11217817664146423, 0.16186554729938507, 0.0015333457849919796, -0.22897611558437347, 0.049678247421979904, -0.125278040766716, -0.0294334813952446, 0.11977242678403854, 0.1422213912010193, -0.10954539477825165, 0.0752737894654274, -0.038042325526475906, -0.005828251596540213, -0.0323176346719265, -0.06205610930919647, -0.05266609415411949, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "baffo32/decapoda-research-llama-7B-hf"}	null	barbonara/alpaca7B-lora	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:baffo32/decapoda-research-llama-7B-hf", "region:us" ]	2024-02-12T14:55:55+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-baffo32/decapoda-research-llama-7B-hf #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-baffo32/decapoda-research-llama-7B-hf #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 46, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-baffo32/decapoda-research-llama-7B-hf #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.11315761506557465, 0.20693697035312653, -0.0033914181403815746, 0.023472558706998825, 0.0715053528547287, 0.017208939418196678, 0.07128063589334488, 0.1285555511713028, 0.03310807794332504, 0.12896162271499634, 0.05852179974317551, 0.11027583479881287, 0.11672219634056091, 0.21474410593509674, -0.007294683717191219, -0.17607176303863525, 0.023287659510970116, -0.0586344338953495, 0.0242341049015522, 0.12702539563179016, 0.13509057462215424, -0.09366968274116516, 0.07438086718320847, -0.024117162451148033, -0.0063532814383506775, -0.02792987786233425, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	nchen909/yi_6b_sft_20710	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T14:56:21+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 56, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06061961501836777, 0.15481999516487122, -0.004844071343541145, 0.02074851468205452, 0.0983177199959755, 0.007407687604427338, 0.07119518518447876, 0.11185134947299957, -0.023851769044995308, 0.1167980208992958, 0.031993988901376724, 0.09781743586063385, 0.11217817664146423, 0.16186554729938507, 0.0015333457849919796, -0.22897611558437347, 0.049678247421979904, -0.125278040766716, -0.0294334813952446, 0.11977242678403854, 0.1422213912010193, -0.10954539477825165, 0.0752737894654274, -0.038042325526475906, -0.005828251596540213, -0.0323176346719265, -0.06205610930919647, -0.05266609415411949, 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null	null	peft	## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0	{"library_name": "peft"}	null	NBA55/llama2-7B-without-diversity-epoch-5-new	[ "peft", "region:us" ]	2024-02-12T14:57:34+00:00	[]	[]	TAGS #peft #region-us	## Training procedure The following 'bitsandbytes' quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0	[ "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n\n- PEFT 0.4.0" ]	[ "TAGS\n#peft #region-us \n", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n\n- PEFT 0.4.0" ]	[ 9, 154, 11 ]	[ "passage: TAGS\n#peft #region-us \n## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16### Framework versions\n\n\n- PEFT 0.4.0" ]	[ -0.06979092955589294, 0.021877916529774666, -0.0024098597932606936, 0.1378258466720581, 0.10862318426370621, 0.07474296540021896, 0.10942144691944122, 0.12339992076158524, 0.0572756864130497, 0.09055759757757187, 0.09443594515323639, 0.05080708488821983, 0.07617583870887756, 0.15425844490528107, -0.02472618594765663, -0.030937515199184418, 0.05461447685956955, 0.0024960634764283895, 0.003683591727167368, 0.07837489992380142, 0.05202331766486168, -0.036243125796318054, 0.037176959216594696, -0.09029994904994965, -0.1683429628610611, -0.0007763148751109838, 0.010803398676216602, 0.03168312832713127, 0.04643981158733368, 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null	null	transformers	A question generation model trained on `alinet/balanced_qg` dataset. Example usage: ```py from transformers import BartConfig, BartForConditionalGeneration, BartTokenizer model_name = "alinet/bart-base-balanced-qg" tokenizer = BartTokenizer.from_pretrained(model_name) model = BartForConditionalGeneration.from_pretrained(model_name) def run_model(input_string, generator_args): input_ids = tokenizer.encode(input_string, return_tensors="pt") res = model.generate(input_ids, generator_args) output = tokenizer.batch_decode(res, skip_special_tokens=True) print(output) run_model("Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.", max_length=32, num_beams=4) # ['What is the Stanford Question Answering Dataset?'] ```	{"language": ["en"], "datasets": ["alinet/balanced_qg"], "model-index": [{"name": "alinet/bart-base-balanced-qg", "results": [{"task": {"type": "text2text-generation", "name": "Question Generation"}, "dataset": {"name": "MRQA", "type": "mrqa"}, "metrics": [{"type": "bertscore", "value": 0.6579994835741414, "name": "BERTScore F1"}, {"type": "bertscore", "value": 0.6617731395187654, "name": "BERTScore Precision"}, {"type": "bertscore", "value": 0.6576008430831539, "name": "BERTScore Recall"}]}]}]}	text2text-generation	alinet/bart-base-balanced-qg	[ "transformers", "pytorch", "bart", "text2text-generation", "en", "dataset:alinet/balanced_qg", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T14:58:35+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #bart #text2text-generation #en #dataset-alinet/balanced_qg #model-index #autotrain_compatible #endpoints_compatible #region-us	A question generation model trained on 'alinet/balanced_qg' dataset. Example usage:	[]	[ "TAGS\n#transformers #pytorch #bart #text2text-generation #en #dataset-alinet/balanced_qg #model-index #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ 56 ]	[ "passage: TAGS\n#transformers #pytorch #bart #text2text-generation #en #dataset-alinet/balanced_qg #model-index #autotrain_compatible #endpoints_compatible #region-us \n" ]	[ -0.09323570132255554, 0.07155559957027435, -0.004813769832253456, 0.02690618298947811, 0.13386039435863495, 0.01978040672838688, 0.12169664353132248, 0.1319703310728073, 0.06169817969202995, -0.05282193794846535, 0.12525714933872223, 0.14474919438362122, -0.0010926296235993505, 0.19348874688148499, -0.11911067366600037, -0.2385481297969818, 0.04158686473965645, 0.09204407781362534, -0.011993187479674816, 0.13328532874584198, 0.1124522015452385, -0.0761725902557373, 0.08205059170722961, -0.006194424815475941, -0.13253901898860931, 0.01984042301774025, 0.022403733804821968, -0.10359407961368561, 0.11886349320411682, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	Guilherme34/Jennifer-uwu-modelnotlora	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T15:02:16+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 56, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05921921506524086, 0.15253323316574097, -0.004925556480884552, 0.01970141939818859, 0.09812989830970764, 0.008722675032913685, 0.07155127823352814, 0.11091651022434235, -0.02038503810763359, 0.11541511863470078, 0.03161177039146423, 0.09504877775907516, 0.11244720220565796, 0.1593349277973175, 0.0006018498679623008, -0.22924894094467163, 0.050943523645401, -0.12565383315086365, -0.028005311265587807, 0.1202453151345253, 0.14323006570339203, -0.10873830318450928, 0.07482945919036865, -0.03924073651432991, -0.006830108352005482, -0.03327549248933792, -0.06254202127456665, -0.05196645110845566, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	tomaszki/nous-twenty-seven	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T15:02:34+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04654794931411743, 0.16618601977825165, -0.005445904564112425, 0.01853804849088192, 0.0981811136007309, 0.011998992413282394, 0.06433123350143433, 0.11398410052061081, -0.0230073444545269, 0.11406639218330383, 0.03047988750040531, 0.10172267258167267, 0.11317981779575348, 0.14841650426387787, -0.002152352826669812, -0.22403094172477722, 0.050844956189394, -0.12105348706245422, -0.033293843269348145, 0.11749980598688126, 0.1483822613954544, -0.09928343445062637, 0.07274559140205383, -0.029687678441405296, -0.012143402360379696, -0.030057786032557487, -0.05890674889087677, -0.046214159578084946, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	feature-extraction	furrutiav/bert_qa_extractor_cockatiel_2022_z_value_it_812	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T15:04:57+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 39, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.052746038883924484, 0.20255789160728455, -0.0045078229159116745, 0.0248473659157753, 0.10497838258743286, 0.00675728265196085, 0.06521498411893845, 0.11486967653036118, -0.0023755673319101334, 0.12028469145298004, 0.027631845325231552, 0.08119397610425949, 0.12110675126314163, 0.15393014252185822, 0.005160121712833643, -0.24253977835178375, 0.05344875901937485, -0.09366832673549652, 0.004077504388988018, 0.11452110856771469, 0.1343945860862732, -0.10780399292707443, 0.08976872265338898, -0.00683097867295146, -0.01712046191096306, -0.015751034021377563, -0.07134060561656952, -0.06668227165937424, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BERT_pretraining_h_100_wo_deepspeed This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the gokuls/wiki_book_corpus_complete_processed_bert_dataset dataset. It achieves the following results on the evaluation set: - Loss: 5.7778 - Accuracy: 0.1539 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 208 - eval_batch_size: 208 - seed: 10 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100000 - num_epochs: 100 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 6.8769 \| 0.36 \| 10000 \| 6.7582 \| 0.1101 \| \| 6.4647 \| 0.71 \| 20000 \| 6.4764 \| 0.1314 \| \| 6.3679 \| 1.07 \| 30000 \| 6.3218 \| 0.1407 \| \| 6.252 \| 1.42 \| 40000 \| 6.2139 \| 0.1454 \| \| 6.2132 \| 1.78 \| 50000 \| 6.1398 \| 0.1478 \| \| 6.0407 \| 2.13 \| 60000 \| 6.0774 \| 0.1502 \| \| 6.0694 \| 2.49 \| 70000 \| 6.0303 \| 0.1516 \| \| 5.9996 \| 2.84 \| 80000 \| 5.9893 \| 0.1521 \| \| 5.9166 \| 3.2 \| 90000 \| 5.9553 \| 0.1526 \| \| 5.8915 \| 3.55 \| 100000 \| 5.9261 \| 0.1530 \| \| 5.8924 \| 3.91 \| 110000 \| 5.8996 \| 0.1534 \| \| 5.8972 \| 4.26 \| 120000 \| 5.8814 \| 0.1533 \| \| 5.8454 \| 4.62 \| 130000 \| 5.8626 \| 0.1532 \| \| 5.8104 \| 4.97 \| 140000 \| 5.8494 \| 0.1534 \| \| 5.8461 \| 5.33 \| 150000 \| 5.8378 \| 0.1534 \| \| 5.8476 \| 5.68 \| 160000 \| 5.8246 \| 0.1536 \| \| 5.7255 \| 6.04 \| 170000 \| 5.8155 \| 0.1532 \| \| 5.8431 \| 6.39 \| 180000 \| 5.8068 \| 0.1537 \| \| 5.7526 \| 6.75 \| 190000 \| 5.7981 \| 0.1537 \| \| 5.7826 \| 7.1 \| 200000 \| 5.7886 \| 0.1537 \| ### Framework versions - Transformers 4.37.1 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["gokuls/wiki_book_corpus_complete_processed_bert_dataset"], "metrics": ["accuracy"], "base_model": "bert-large-uncased", "model-index": [{"name": "BERT_pretraining_h_100_wo_deepspeed", "results": [{"task": {"type": "fill-mask", "name": "Masked Language Modeling"}, "dataset": {"name": "gokuls/wiki_book_corpus_complete_processed_bert_dataset", "type": "gokuls/wiki_book_corpus_complete_processed_bert_dataset"}, "metrics": [{"type": "accuracy", "value": 0.15387755648267093, "name": "Accuracy"}]}]}]}	fill-mask	gokuls/BERT_pretraining_h_100_wo_deepspeed	[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "dataset:gokuls/wiki_book_corpus_complete_processed_bert_dataset", "base_model:bert-large-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T15:05:26+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #dataset-gokuls/wiki_book_corpus_complete_processed_bert_dataset #base_model-bert-large-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	BERT\_pretraining\_h\_100\_wo\_deepspeed ======================================== This model is a fine-tuned version of bert-large-uncased on the gokuls/wiki\_book\_corpus\_complete\_processed\_bert\_dataset dataset. It achieves the following results on the evaluation set: * Loss: 5.7778 * Accuracy: 0.1539 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 208 * eval\_batch\_size: 208 * seed: 10 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 100000 * num\_epochs: 100 ### Training results ### Framework versions * Transformers 4.37.1 * Pytorch 2.1.2+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 208\n* eval\\_batch\\_size: 208\n* seed: 10\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100000\n* num\\_epochs: 100", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.1\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #dataset-gokuls/wiki_book_corpus_complete_processed_bert_dataset #base_model-bert-large-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 208\n* eval\\_batch\\_size: 208\n* seed: 10\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100000\n* num\\_epochs: 100", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.1\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 98, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #fill-mask #generated_from_trainer #dataset-gokuls/wiki_book_corpus_complete_processed_bert_dataset #base_model-bert-large-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 208\n* eval\\_batch\\_size: 208\n* seed: 10\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100000\n* num\\_epochs: 100### Training results### Framework versions\n\n\n* Transformers 4.37.1\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.157412588596344, 0.12723976373672485, -0.001634515356272459, 0.08222009241580963, 0.10929345339536667, 0.018741700798273087, 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null	null	stable-baselines3	# DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Federm1512 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Federm1512 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Federm1512 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 40), ('buffer_size', 101000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```	{"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "751.50 +/- 218.62", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	Federm1512/dqn-SpaceInvadersNoFrameskip-v4	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-12T15:08:33+00:00	[]	[]	TAGS #stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library and the RL Zoo. The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: URL SB3: URL SB3 Contrib: URL Install the RL Zoo (with SB3 and SB3-Contrib): If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do: ## Training (with the RL Zoo) ## Hyperparameters # Environment Arguments	[ "# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.", "## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:", "## Training (with the RL Zoo)", "## Hyperparameters", "# Environment Arguments" ]	[ "TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.", "## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:", "## Training (with the RL Zoo)", "## Hyperparameters", "# Environment Arguments" ]	[ 43, 90, 73, 9, 5, 7 ]	[ "passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments" ]	[ 0.043572068214416504, 0.2414778620004654, -0.0026879787910729647, 0.012635791674256325, 0.05784223601222038, 0.0030472534708678722, 0.08585051447153091, 0.10650663822889328, 0.024212315678596497, -0.001382096204906702, 0.003954293206334114, 0.17533031105995178, 0.03632635250687599, 0.13125447928905487, 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null	null	transformers	Model description: Model: bert-base-multilingual-cased Dataset: TASTEset Unshuffled ratio: ['0'] Shuffled ratio: ['1'] Best exact match epoch: 10 Best exact match: 72.1 Best epoch: 10 Drop duplicates: ['1'] Max epochs = 10 Optimizer lr = 3e-05 Optimizer eps = 1e-08 Batch size = 32 Dataset path = pgajo/EW-TT-PE_U0_S1_DROP1_mbert Results \| epoch \| train_loss \| train_f1 \| train_exact \| dev_loss \| dev_f1 \| dev_exact \| test_loss \| test_f1 \| test_exact \| \|--------:\|-------------:\|-----------:\|--------------:\|-----------:\|---------:\|------------:\|------------:\|----------:\|-------------:\| \| 1 \| 3.35 \| 8.36 \| 0.76 \| 2.78 \| 13.81 \| 3.04 \| 0 \| 0 \| 0 \| \| 2 \| 2.25 \| 27.1 \| 16.59 \| 1.57 \| 55.74 \| 46.96 \| 0 \| 0 \| 0 \| \| 3 \| 1.14 \| 64.77 \| 55.56 \| 1.35 \| 66.21 \| 59.12 \| 0 \| 0 \| 0 \| \| 4 \| 0.62 \| 79.58 \| 73.88 \| 1.19 \| 68.45 \| 62.15 \| 0 \| 0 \| 0 \| \| 5 \| 0.36 \| 87.7 \| 84.73 \| 1.4 \| 72.12 \| 66.3 \| 0 \| 0 \| 0 \| \| 6 \| 0.23 \| 92.1 \| 88.94 \| 1.2 \| 74.8 \| 70.17 \| 0 \| 0 \| 0 \| \| 7 \| 0.16 \| 94.35 \| 92.47 \| 1.3 \| 74.04 \| 67.13 \| 0 \| 0 \| 0 \| \| 8 \| 0.12 \| 95.37 \| 94.75 \| 1.34 \| 76.44 \| 69.61 \| 0 \| 0 \| 0 \| \| 9 \| 0.08 \| 96.92 \| 95.99 \| 1.33 \| 77.65 \| 71.55 \| 0 \| 0 \| 0 \| \| 10 \| 0.08 \| 97.02 \| 96.13 \| 1.57 \| 77.43 \| 72.1 \| 0 \| 0 \| 0 \|	{}	question-answering	pgajo/mbert_EW-TT-PE_U0_S1_DROP1_mbert_E10_DEV72.0	[ "transformers", "safetensors", "bert", "question-answering", "endpoints_compatible", "region:us" ]	2024-02-12T15:10:52+00:00	[]	[]	TAGS #transformers #safetensors #bert #question-answering #endpoints_compatible #region-us	Model description: ``` Model: bert-base-multilingual-cased Dataset: TASTEset Unshuffled ratio: ['0'] Shuffled ratio: ['1'] Best exact match epoch: 10 Best exact match: 72.1 Best epoch: 10 Drop duplicates: ['1'] Max epochs = 10 Optimizer lr = 3e-05 Optimizer eps = 1e-08 Batch size = 32 Dataset path = pgajo/EW-TT-PE_U0_S1_DROP1_mbert ``` Results	[]	[ "TAGS\n#transformers #safetensors #bert #question-answering #endpoints_compatible #region-us \n" ]	[ 30 ]	[ "passage: TAGS\n#transformers #safetensors #bert #question-answering #endpoints_compatible #region-us \n" ]	[ -0.03100396879017353, 0.011429967358708382, -0.009655450470745564, -0.0477571114897728, 0.071015864610672, 0.001686002011410892, 0.08008057624101639, 0.05985769256949425, 0.11401950567960739, 0.02590048313140869, 0.1903941035270691, 0.16566626727581024, -0.07932274788618088, 0.015106523409485817, -0.13172350823879242, -0.13182127475738525, 0.11529869586229324, 0.03778080269694328, -0.03543904423713684, 0.10329030454158783, 0.05029234290122986, -0.12624382972717285, 0.04368755966424942, -0.06763096153736115, -0.062081653624773026, 0.06668882071971893, 0.04820772260427475, -0.08198674768209457, 0.13085918128490448, 0.03362511843442917, 0.2047542929649353, 0.04677434265613556, -0.1182841956615448, -0.21163156628608704, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	automatic-speech-recognition	IbrahimSalah/Quran_tarteel_v1	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T15:17:38+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 47, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06877388060092926, 0.1546701192855835, -0.0037609888240695, 0.013798683881759644, 0.11170210689306259, 0.0049477447755634785, 0.07622946053743362, 0.1076156347990036, -0.024175573140382767, 0.12644733488559723, 0.04164152219891548, 0.09870775043964386, 0.11074616760015488, 0.18980292975902557, 0.0015578214079141617, -0.20271944999694824, 0.06667982041835785, -0.11557482928037643, 0.02210802026093006, 0.12125445902347565, 0.14131462574005127, -0.10717527568340302, 0.06805222481489182, -0.03453851491212845, -0.022604284808039665, -0.03256304934620857, -0.06200181692838669, -0.0628168061375618, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-with-pubmed-noise-data-0.1-v2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2115 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.4161 \| 0.11 \| 500 \| 0.3441 \| \| 0.342 \| 0.21 \| 1000 \| 0.3091 \| \| 0.2694 \| 0.32 \| 1500 \| 0.2969 \| \| 0.3792 \| 0.43 \| 2000 \| 0.2712 \| \| 0.3219 \| 0.54 \| 2500 \| 0.2601 \| \| 0.3001 \| 0.64 \| 3000 \| 0.2574 \| \| 0.2606 \| 0.75 \| 3500 \| 0.2489 \| \| 0.2716 \| 0.86 \| 4000 \| 0.2415 \| \| 0.2714 \| 0.96 \| 4500 \| 0.2382 \| \| 0.2072 \| 1.07 \| 5000 \| 0.2429 \| \| 0.2111 \| 1.18 \| 5500 \| 0.2377 \| \| 0.1977 \| 1.28 \| 6000 \| 0.2455 \| \| 0.2171 \| 1.39 \| 6500 \| 0.2309 \| \| 0.1853 \| 1.5 \| 7000 \| 0.2314 \| \| 0.2436 \| 1.61 \| 7500 \| 0.2269 \| \| 0.171 \| 1.71 \| 8000 \| 0.2220 \| \| 0.2032 \| 1.82 \| 8500 \| 0.2226 \| \| 0.2028 \| 1.93 \| 9000 \| 0.2175 \| \| 0.1448 \| 2.03 \| 9500 \| 0.2227 \| \| 0.1447 \| 2.14 \| 10000 \| 0.2216 \| \| 0.1516 \| 2.25 \| 10500 \| 0.2200 \| \| 0.1294 \| 2.35 \| 11000 \| 0.2197 \| \| 0.1569 \| 2.46 \| 11500 \| 0.2157 \| \| 0.1505 \| 2.57 \| 12000 \| 0.2160 \| \| 0.152 \| 2.68 \| 12500 \| 0.2151 \| \| 0.1588 \| 2.78 \| 13000 \| 0.2117 \| \| 0.1451 \| 2.89 \| 13500 \| 0.2134 \| \| 0.1644 \| 3.0 \| 14000 \| 0.2115 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-with-pubmed-noise-data-0.1-v2", "results": []}]}	text2text-generation	gayanin/bart-with-pubmed-noise-data-0.1-v2	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T15:18:34+00:00	[]	[]	TAGS #transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	bart-with-pubmed-noise-data-0.1-v2 ================================== This model is a fine-tuned version of facebook/bart-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.2115 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 10 * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.2+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 64, 131, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.12290263921022415, 0.13467338681221008, -0.0025401196908205748, 0.07776759564876556, 0.11219188570976257, 0.018951870501041412, 0.15990781784057617, 0.13776415586471558, 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null	null	null	### My--Dog Dreambooth model trained by BharatMata following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: Roll-No.27 Sample pictures of this concept: ![0](https://huggingface.co/BharatMata/my-dog/resolve/main/sample_images/Screenshot_2024-02-12_084810.png)	{"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]}	text-to-image	BharatMata/my-dog	[ "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]	2024-02-12T15:20:20+00:00	[]	[]	TAGS #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #region-us	### My--Dog Dreambooth model trained by BharatMata following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: Roll-No.27 Sample pictures of this concept: !0	[ "### My--Dog Dreambooth model trained by BharatMata following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: Roll-No.27\n\nSample pictures of this concept:\n\n !0" ]	[ "TAGS\n#safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #region-us \n", "### My--Dog Dreambooth model trained by BharatMata following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: Roll-No.27\n\nSample pictures of this concept:\n\n !0" ]	[ 48, 53 ]	[ "passage: TAGS\n#safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #region-us \n### My--Dog Dreambooth model trained by BharatMata following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: Roll-No.27\n\nSample pictures of this concept:\n\n !0" ]	[ -0.07838904857635498, 0.19582056999206543, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-with-woz-noise-data-0.1-v2 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0845 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.2188 \| 0.13 \| 500 \| 0.1794 \| \| 0.1741 \| 0.26 \| 1000 \| 0.1518 \| \| 0.1631 \| 0.39 \| 1500 \| 0.1327 \| \| 0.1318 \| 0.53 \| 2000 \| 0.1272 \| \| 0.1238 \| 0.66 \| 2500 \| 0.1168 \| \| 0.1451 \| 0.79 \| 3000 \| 0.1103 \| \| 0.1166 \| 0.92 \| 3500 \| 0.1068 \| \| 0.0833 \| 1.05 \| 4000 \| 0.1054 \| \| 0.1029 \| 1.18 \| 4500 \| 0.1017 \| \| 0.1174 \| 1.31 \| 5000 \| 0.0971 \| \| 0.0786 \| 1.44 \| 5500 \| 0.0956 \| \| 0.1184 \| 1.58 \| 6000 \| 0.0951 \| \| 0.0984 \| 1.71 \| 6500 \| 0.0926 \| \| 0.0959 \| 1.84 \| 7000 \| 0.0893 \| \| 0.093 \| 1.97 \| 7500 \| 0.0893 \| \| 0.0783 \| 2.1 \| 8000 \| 0.0910 \| \| 0.0678 \| 2.23 \| 8500 \| 0.0927 \| \| 0.0756 \| 2.36 \| 9000 \| 0.0889 \| \| 0.0684 \| 2.5 \| 9500 \| 0.0877 \| \| 0.0573 \| 2.63 \| 10000 \| 0.0872 \| \| 0.0544 \| 2.76 \| 10500 \| 0.0855 \| \| 0.0579 \| 2.89 \| 11000 \| 0.0845 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-with-woz-noise-data-0.1-v2", "results": []}]}	text2text-generation	gayanin/bart-with-woz-noise-data-0.1-v2	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T15:21:35+00:00	[]	[]	TAGS #transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	bart-with-woz-noise-data-0.1-v2 =============================== This model is a fine-tuned version of facebook/bart-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0845 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 10 * num\_epochs: 3 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.2+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 64, 131, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.12290263921022415, 0.13467338681221008, -0.0025401196908205748, 0.07776759564876556, 0.11219188570976257, 0.018951870501041412, 0.15990781784057617, 0.13776415586471558, 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null	null	diffusers	# Stable Cascade Prior <!-- Provide a quick summary of what the model is/does. --> <img src="figures/collage_1.jpg" width="800"> This model is built upon the [Würstchen](https://openreview.net/forum?id=gU58d5QeGv) architecture and its main difference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this important? The smaller the latent space, the faster you can run inference and the cheaper the training becomes. How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being encoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a 1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable Diffusion 1.5. <br> <br> Therefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions like finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well. ## Model Details ### Model Description Stable Cascade is a diffusion model trained to generate images given a text prompt. - Developed by: Stability AI - Funded by: Stability AI - Model type: Generative text-to-image model ### Model Sources For research purposes, we recommend our `StableCascade` Github repository (https://github.com/Stability-AI/StableCascade). - Repository: https://github.com/Stability-AI/StableCascade - Paper: https://openreview.net/forum?id=gU58d5QeGv ### Model Overview Stable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images, hence the name "Stable Cascade". Stage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion. However, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a spatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves a compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the image. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible for generating the small 24 x 24 latents given a text prompt. The following picture shows this visually. <img src="figures/model-overview.jpg" width="600"> For this release, we are providing two checkpoints for Stage C, two for Stage B and one for Stage A. Stage C comes with a 1 billion and 3.6 billion parameter version, but we highly recommend using the 3.6 billion version, as most work was put into its finetuning. The two versions for Stage B amount to 700 million and 1.5 billion parameters. Both achieve great results, however the 1.5 billion excels at reconstructing small and fine details. Therefore, you will achieve the best results if you use the larger variant of each. Lastly, Stage A contains 20 million parameters and is fixed due to its small size. ## Evaluation <img height="300" src="figures/comparison.png"/> According to our evaluation, Stable Cascade performs best in both prompt alignment and aesthetic quality in almost all comparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and aesthetic prompts. Specifically, Stable Cascade (30 inference steps) was compared against Playground v2 (50 inference steps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps). ## Code Example ```shell #install `diffusers` from this branch while the PR is WIP pip install git+https://github.com/kashif/diffusers.git@wuerstchen-v3 ``` ```python import torch from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline device = "cuda" dtype = torch.bfloat16 num_images_per_prompt = 2 prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=dtype).to(device) decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=dtype).to(device) prompt = "Anthropomorphic cat dressed as a pilot" negative_prompt = "" with torch.cuda.amp.autocast(dtype=dtype): prior_output = prior( prompt=prompt, height=1024, width=1024, negative_prompt=negative_prompt, guidance_scale=4.0, num_images_per_prompt=num_images_per_prompt, ) decoder_output = decoder( image_embeddings=prior_output.image_embeddings, prompt=prompt, negative_prompt=negative_prompt, guidance_scale=0.0, output_type="pil", ).images ``` ## Uses ### Direct Use The model is intended for research purposes for now. Possible research areas and tasks include - Research on generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. Excluded uses are described below. ### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. The model should not be used in any way that violates Stability AI's [Acceptable Use Policy](https://stability.ai/use-policy). ## Limitations and Bias ### Limitations - Faces and people in general may not be generated properly. - The autoencoding part of the model is lossy. ### Recommendations The model is intended for research purposes only. ## How to Get Started with the Model Check out https://github.com/Stability-AI/StableCascade	{"license": "other", "pipeline_tag": "text-to-image", "license_name": "stable-cascade-nc-community", "license_link": "LICENSE"}	text-to-image	stabilityai/stable-cascade-prior	[ "diffusers", "safetensors", "text-to-image", "license:other", "has_space", "diffusers:StableCascadePriorPipeline", "region:us" ]	2024-02-12T15:22:59+00:00	[]	[]	TAGS #diffusers #safetensors #text-to-image #license-other #has_space #diffusers-StableCascadePriorPipeline #region-us	# Stable Cascade Prior <img src="figures/collage_1.jpg" width="800"> This model is built upon the Würstchen architecture and its main difference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this important? The smaller the latent space, the faster you can run inference and the cheaper the training becomes. How small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being encoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a 1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the highly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable Diffusion 1.5. <br> <br> Therefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions like finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well. ## Model Details ### Model Description Stable Cascade is a diffusion model trained to generate images given a text prompt. - Developed by: Stability AI - Funded by: Stability AI - Model type: Generative text-to-image model ### Model Sources For research purposes, we recommend our 'StableCascade' Github repository (URL - Repository: URL - Paper: URL ### Model Overview Stable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images, hence the name "Stable Cascade". Stage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion. However, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a spatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves a compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the image. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible for generating the small 24 x 24 latents given a text prompt. The following picture shows this visually. <img src="figures/URL" width="600"> For this release, we are providing two checkpoints for Stage C, two for Stage B and one for Stage A. Stage C comes with a 1 billion and 3.6 billion parameter version, but we highly recommend using the 3.6 billion version, as most work was put into its finetuning. The two versions for Stage B amount to 700 million and 1.5 billion parameters. Both achieve great results, however the 1.5 billion excels at reconstructing small and fine details. Therefore, you will achieve the best results if you use the larger variant of each. Lastly, Stage A contains 20 million parameters and is fixed due to its small size. ## Evaluation <img height="300" src="figures/URL"/> According to our evaluation, Stable Cascade performs best in both prompt alignment and aesthetic quality in almost all comparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and aesthetic prompts. Specifically, Stable Cascade (30 inference steps) was compared against Playground v2 (50 inference steps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps). ## Code Example ## Uses ### Direct Use The model is intended for research purposes for now. Possible research areas and tasks include - Research on generative models. - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. Excluded uses are described below. ### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. The model should not be used in any way that violates Stability AI's Acceptable Use Policy. ## Limitations and Bias ### Limitations - Faces and people in general may not be generated properly. - The autoencoding part of the model is lossy. ### Recommendations The model is intended for research purposes only. ## How to Get Started with the Model Check out URL	[ "# Stable Cascade Prior\n\n\n<img src=\"figures/collage_1.jpg\" width=\"800\">\n\nThis model is built upon the Würstchen architecture and its main \ndifference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this \nimportant? The smaller the latent space, the faster you can run inference and the cheaper the training becomes. \nHow small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being \nencoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a \n1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the \nhighly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable \nDiffusion 1.5. <br> <br>\nTherefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions\nlike finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.", "## Model Details", "### Model Description\n\nStable Cascade is a diffusion model trained to generate images given a text prompt.\n\n- Developed by: Stability AI\n- Funded by: Stability AI\n- Model type: Generative text-to-image model", "### Model Sources\n\nFor research purposes, we recommend our 'StableCascade' Github repository (URL\n\n- Repository: URL\n- Paper: URL", "### Model Overview\nStable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images,\nhence the name \"Stable Cascade\".\nStage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion. \nHowever, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a \nspatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves \na compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the \nimage. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible \nfor generating the small 24 x 24 latents given a text prompt. The following picture shows this visually.\n\n<img src=\"figures/URL\" width=\"600\">\n\nFor this release, we are providing two checkpoints for Stage C, two for Stage B and one for Stage A. Stage C comes with \na 1 billion and 3.6 billion parameter version, but we highly recommend using the 3.6 billion version, as most work was \nput into its finetuning. The two versions for Stage B amount to 700 million and 1.5 billion parameters. Both achieve \ngreat results, however the 1.5 billion excels at reconstructing small and fine details. Therefore, you will achieve the \nbest results if you use the larger variant of each. Lastly, Stage A contains 20 million parameters and is fixed due to \nits small size.", "## Evaluation\n<img height=\"300\" src=\"figures/URL\"/>\nAccording to our evaluation, Stable Cascade performs best in both prompt alignment and aesthetic quality in almost all \ncomparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and \naesthetic prompts. Specifically, Stable Cascade (30 inference steps) was compared against Playground v2 (50 inference \nsteps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps).", "## Code Example", "## Uses", "### Direct Use\n\nThe model is intended for research purposes for now. Possible research areas and tasks include\n\n- Research on generative models.\n- Safe deployment of models which have the potential to generate harmful content.\n- Probing and understanding the limitations and biases of generative models.\n- Generation of artworks and use in design and other artistic processes.\n- Applications in educational or creative tools.\n\nExcluded uses are described below.", "### Out-of-Scope Use\n\nThe model was not trained to be factual or true representations of people or events, \nand therefore using the model to generate such content is out-of-scope for the abilities of this model.\nThe model should not be used in any way that violates Stability AI's Acceptable Use Policy.", "## Limitations and Bias", "### Limitations\n- Faces and people in general may not be generated properly.\n- The autoencoding part of the model is lossy.", "### Recommendations\n\nThe model is intended for research purposes only.", "## How to Get Started with the Model\n\nCheck out URL" ]	[ "TAGS\n#diffusers #safetensors #text-to-image #license-other #has_space #diffusers-StableCascadePriorPipeline #region-us \n", "# Stable Cascade Prior\n\n\n<img src=\"figures/collage_1.jpg\" width=\"800\">\n\nThis model is built upon the Würstchen architecture and its main \ndifference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this \nimportant? The smaller the latent space, the faster you can run inference and the cheaper the training becomes. \nHow small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being \nencoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a \n1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the \nhighly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable \nDiffusion 1.5. <br> <br>\nTherefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions\nlike finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.", "## Model Details", "### Model Description\n\nStable Cascade is a diffusion model trained to generate images given a text prompt.\n\n- Developed by: Stability AI\n- Funded by: Stability AI\n- Model type: Generative text-to-image model", "### Model Sources\n\nFor research purposes, we recommend our 'StableCascade' Github repository (URL\n\n- Repository: URL\n- Paper: URL", "### Model Overview\nStable Cascade consists of three models: Stage A, Stage B and Stage C, representing a cascade to generate images,\nhence the name \"Stable Cascade\".\nStage A & B are used to compress images, similar to what the job of the VAE is in Stable Diffusion. \nHowever, with this setup, a much higher compression of images can be achieved. While the Stable Diffusion models use a \nspatial compression factor of 8, encoding an image with resolution of 1024 x 1024 to 128 x 128, Stable Cascade achieves \na compression factor of 42. This encodes a 1024 x 1024 image to 24 x 24, while being able to accurately decode the \nimage. This comes with the great benefit of cheaper training and inference. Furthermore, Stage C is responsible \nfor generating the small 24 x 24 latents given a text prompt. The following picture shows this visually.\n\n<img src=\"figures/URL\" width=\"600\">\n\nFor this release, we are providing two checkpoints for Stage C, two for Stage B and one for Stage A. Stage C comes with \na 1 billion and 3.6 billion parameter version, but we highly recommend using the 3.6 billion version, as most work was \nput into its finetuning. The two versions for Stage B amount to 700 million and 1.5 billion parameters. Both achieve \ngreat results, however the 1.5 billion excels at reconstructing small and fine details. Therefore, you will achieve the \nbest results if you use the larger variant of each. Lastly, Stage A contains 20 million parameters and is fixed due to \nits small size.", "## Evaluation\n<img height=\"300\" src=\"figures/URL\"/>\nAccording to our evaluation, Stable Cascade performs best in both prompt alignment and aesthetic quality in almost all \ncomparisons. The above picture shows the results from a human evaluation using a mix of parti-prompts (link) and \naesthetic prompts. Specifically, Stable Cascade (30 inference steps) was compared against Playground v2 (50 inference \nsteps), SDXL (50 inference steps), SDXL Turbo (1 inference step) and Würstchen v2 (30 inference steps).", "## Code Example", "## Uses", "### Direct Use\n\nThe model is intended for research purposes for now. Possible research areas and tasks include\n\n- Research on generative models.\n- Safe deployment of models which have the potential to generate harmful content.\n- Probing and understanding the limitations and biases of generative models.\n- Generation of artworks and use in design and other artistic processes.\n- Applications in educational or creative tools.\n\nExcluded uses are described below.", "### Out-of-Scope Use\n\nThe model was not trained to be factual or true representations of people or events, \nand therefore using the model to generate such content is out-of-scope for the abilities of this model.\nThe model should not be used in any way that violates Stability AI's Acceptable Use Policy.", "## Limitations and Bias", "### Limitations\n- Faces and people in general may not be generated properly.\n- The autoencoding part of the model is lossy.", "### Recommendations\n\nThe model is intended for research purposes only.", "## How to Get Started with the Model\n\nCheck out URL" ]	[ 45, 279, 3, 51, 38, 358, 129, 4, 3, 100, 73, 6, 31, 16, 12 ]	[ "passage: TAGS\n#diffusers #safetensors #text-to-image #license-other #has_space #diffusers-StableCascadePriorPipeline #region-us \n# Stable Cascade Prior\n\n\n<img src=\"figures/collage_1.jpg\" width=\"800\">\n\nThis model is built upon the Würstchen architecture and its main \ndifference to other models like Stable Diffusion is that it is working at a much smaller latent space. Why is this \nimportant? The smaller the latent space, the faster you can run inference and the cheaper the training becomes. \nHow small is the latent space? Stable Diffusion uses a compression factor of 8, resulting in a 1024x1024 image being \nencoded to 128x128. Stable Cascade achieves a compression factor of 42, meaning that it is possible to encode a \n1024x1024 image to 24x24, while maintaining crisp reconstructions. The text-conditional model is then trained in the \nhighly compressed latent space. Previous versions of this architecture, achieved a 16x cost reduction over Stable \nDiffusion 1.5. <br> <br>\nTherefore, this kind of model is well suited for usages where efficiency is important. Furthermore, all known extensions\nlike finetuning, LoRA, ControlNet, IP-Adapter, LCM etc. are possible with this method as well.## Model Details### Model Description\n\nStable Cascade is a diffusion model trained to generate images given a text prompt.\n\n- Developed by: Stability AI\n- Funded by: Stability AI\n- Model type: Generative text-to-image model### Model Sources\n\nFor research purposes, we recommend our 'StableCascade' Github repository (URL\n\n- Repository: URL\n- Paper: URL" ]	[ -0.04553224518895149, 0.028652779757976532, -0.0026443121023476124, 0.05589521676301956, 0.08423837274312973, 0.006148392800241709, 0.037588536739349365, 0.0682448297739029, -0.0747796818614006, 0.12006303668022156, -0.019078917801380157, -0.09032853692770004, 0.07727912813425064, 0.021806402131915092, 0.04636978358030319, -0.2237199991941452, 0.04328487813472748, -0.07244826853275299, 0.10992462933063507, 0.05142925679683685, 0.10439178347587585, -0.11346792429685593, 0.05963541194796562, 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null	null	null	SHOWCASE HAVEALL MODEL -> https://haveall.net/ is featured on the model's website. The site shows several hundred examples with images and complete prompts for generation. Recommendations for generating images with the Haveall model: - CFG Scale: 2 - For portraits and realistic images, use CFG Scale: 2. For graphics, you can use CFG Scale: 3 - Standard image resolution for generation: 768х768, 832х576, 576х832 - You can generate images in other formats: 896х576, 832х512 - No Lora and embeddings are required, just simple prompts. --- license: wtfpl ---	{}	null	sonangroup/HaveallX	[ "region:us" ]	2024-02-12T15:23:44+00:00	[]	[]	TAGS #region-us	SHOWCASE HAVEALL MODEL -> URL is featured on the model's website. The site shows several hundred examples with images and complete prompts for generation. Recommendations for generating images with the Haveall model: - CFG Scale: 2 - For portraits and realistic images, use CFG Scale: 2. For graphics, you can use CFG Scale: 3 - Standard image resolution for generation: 768х768, 832х576, 576х832 - You can generate images in other formats: 896х576, 832х512 - No Lora and embeddings are required, just simple prompts. --- license: wtfpl ---	[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]	[ 0.024608636274933815, -0.026205500587821007, -0.009666500613093376, -0.10395516455173492, 0.08638657629489899, 0.059816278517246246, 0.01882290467619896, 0.020661840215325356, 0.23975107073783875, -0.005599027033895254, 0.1219947561621666, 0.0015615287702530622, -0.037353623658418655, 0.03733762726187706, -0.0035912662278860807, -0.17583473026752472, 0.03876631706953049, -0.018274923786520958, 0.01843859627842903, 0.026470553129911423, -0.07776834815740585, -0.07564429938793182, 0.015296397730708122, -0.10247814655303955, -0.083692267537117, 0.11002834886312485, 0.031466204673051834, -0.019670886918902397, 0.10779199749231339, -0.04243955761194229, 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null	null	setfit	# SetFit with ppsingh/SECTOR-multilabel-mpnet_w This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [ppsingh/SECTOR-multilabel-mpnet_w](https://huggingface.co/ppsingh/SECTOR-multilabel-mpnet_w) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [ppsingh/SECTOR-multilabel-mpnet_w](https://huggingface.co/ppsingh/SECTOR-multilabel-mpnet_w) - Classification head: a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 4 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("ppsingh/iki_sector_setfit") # Run inference preds = model("In the shipping and aviation sectors, emission reduction efforts will be focused on distributing eco-friendly ships and enhancing the operational efficiency of aircraft. Agriculture, livestock farming and fisheries: The Republic Korea is introducing various options to accelerate low-carbon farming, for instance, improving irrigation techniques in rice paddies and adopting low-input systems for nitrogen fertilizers.") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:-------\|:----\| \| Word count \| 35 \| 76.164 \| 170 \| - Training Dataset: 250 \| Class \| Positive Count of Class\| \|:-------------\|:--------\| \| Economy-wide \| 88 \| \| Energy \| 63 \| \| Other Sector \| 64 \| \| Transport \| 139 \| - Validation Dataset: 42 \| Class \| Positive Count of Class\| \|:-------------\|:--------\| \| Economy-wide \| 15 \| \| Energy \| 11 \| \| Other Sector \| 11 \| \| Transport \| 24 \| ### Training Hyperparameters - batch_size: (16, 2) - num_epochs: (1, 10) - max_steps: -1 - sampling_strategy: oversampling - body_learning_rate: (2e-05, 1e-05) - head_learning_rate: 0.01 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.01 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| 0.0005 \| 1 \| 0.2029 \| - \| \| 0.0993 \| 200 \| 0.0111 \| 0.1124 \| \| 0.1985 \| 400 \| 0.0063 \| 0.111 \| \| 0.2978 \| 600 \| 0.0183 \| 0.1214 \| \| 0.3970 \| 800 \| 0.0197 \| 0.1248 \| \| 0.4963 \| 1000 \| 0.0387 \| 0.1339 \| \| 0.5955 \| 1200 \| 0.0026 \| 0.1181 \| \| 0.6948 \| 1400 \| 0.0378 \| 0.1208 \| \| 0.7940 \| 1600 \| 0.0285 \| 0.1267 \| \| 0.8933 \| 1800 \| 0.0129 \| 0.1254 \| \| 0.9926 \| 2000 \| 0.0341 \| 0.1271 \| ### Classifier Training Results \| Epoch \| Training F1-micro\|Training F1-Samples \|Training F1-weighted\|Validation F1-micro \|Validation F1-samples \|Validation F1-weighted \| \|:------:\|:----------------:\|:------------------:\|:------------------:\|:------------------:\|:--------------------:\|:---------------------:\| \| 0 \| 0.954 \| 0.972 \| 0.945 \|0.824 \| 0.819 \| 0.813 \| \| 1 \| 0.994 \| 0.996 \| 0.994 \|0.850 \| 0.832 \| 0.852 \| \| 2 \| 0.981 \| 0.989 \| 0.979 \|0.850 \| 0.843 \| 0.852 \| \| 3 \| 0.995 \| 0.997 \| 0.995 \|0.852 \| 0.843 \| 0.858 \| \| 4 \| 0.994 \| 0.996 \| 0.994 \|0.852 \| 0.843 \| 0.858 \| \| 5 \| 0.995 \| 0.997 \| 0.995 \|0.859 \| 0.848 \| 0.863 \| \|label \| precision \|recall \|f1-score\| support\| \|:-------------:\|:---------:\|:-----:\|:------:\|:------:\| \|Economy-wide \|0.857 \|0.800 \|0.827 \| 15.0 \| \|Energy \|1.00 \|0.818 \|0.900 \| 11.0 \| \|Other Sector \|0.615 \|0.727 \|0.667 \| 11.0 \| \|Transport \|0.958 \|0.958 \|0.958 \| 24.0 \| - Micro Avg: Precision = 0.866, Recall = 0.852, F1 = 0.859504 - Samples Avg: Precision = 0.869, Recall = 0.861, F1 = 0.848 ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - Carbon Emitted: 0.026 kg of CO2 - Hours Used: 0.622 hours ### Training Hardware - On Cloud: No - GPU Model: 1 x Tesla T4 - CPU Model: Intel(R) Xeon(R) CPU @ 2.00GHz - RAM Size: 12.67 GB ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.3.1 - Transformers: 4.35.2 - PyTorch: 2.1.0+cu121 - Datasets: 2.3.0 - Tokenizers: 0.15.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->	{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "widget": [{"text": "Specific information applicable to Parties, including regional economic integration organizations and their member States, that have reached an agreement to act jointly under Article 4, paragraph 2, of the Paris Agreement, including the Parties that agreed to act jointly and the terms of the agreement, in accordance with Article 4, paragraphs 16\u201318, of the Paris Agreement. Not applicable. (c). How the Party\u2019s preparation of its nationally determined contribution has been informed by the outcomes of the global stocktake, in accordance with Article 4, paragraph 9, of the Paris Agreement."}, {"text": "In the shipping and aviation sectors, emission reduction efforts will be focused on distributing eco-friendly ships and enhancing the operational efficiency of aircraft. Agriculture, livestock farming and fisheries: The Republic Korea is introducing various options to accelerate low-carbon farming, for instance, improving irrigation techniques in rice paddies and adopting low-input systems for nitrogen fertilizers."}, {"text": "As part of this commitment, Oman s upstream oil and gas industry is developing economically viable solutions to phase out routine flaring as quickly as possible and ahead of the World Bank s target date. IV. Climate Preparedness and Resilience. The Sultanate of Oman has stepped up its efforts in advancing its expertise and methodologies to better manage the climate change risks over the past five years. The adaptation efforts are underway, and the status of adaptation planning is still at a nascent stage."}, {"text": "Synergy and coherence 46 VII- Gender and youth 46 VIII- Education and employment 48 ANNEXES. 49 Annex No. 1: Details of mitigation measures, conditional and non-conditional, by sector 49 Annex No.2: List of adaptation actions proposed by sectors. 57 Annex No.3: GCF project portfolio. 63 CONTRIBUTION DENTERMINEE AT NATIONAL LEVEL CDN MAURITANIE LIST OF TABLES Table 1: Summary of funding needs for the CND 2021-2030 updated. 12 Table 2: CND 2021-2030 mitigation measures updated by sector (cumulative cost and reduction potential for the period). 14 Table 3: CND 2021-2030 adaptation measures updated by sector. Error!"}, {"text": "In the transport sector, restructuing is planned through a number of large infrastructure initiatives aiming to revive the role of public transport and achieving a relevant share of fuel efficient vehicles. Under both the conditional and unconditional mitigation scenarios, Lebanon will achieve sizeable emission reductions. With regards to adaptation, Lebanon has planned comprehensive sectoral actions related to water, agriculture/forestry and biodiversity, for example related to irrigation, forest management, etc. It also continues developing adaptation strategies in the remaining sectors."}], "pipeline_tag": "text-classification", "inference": false, "co2_eq_emissions": {"emissions": 25.8151164022705, "source": "codecarbon", "training_type": "fine-tuning", "on_cloud": false, "cpu_model": "Intel(R) Xeon(R) CPU @ 2.00GHz", "ram_total_size": 12.674781799316406, "hours_used": 0.622, "hardware_used": "1 x Tesla T4"}, "base_model": "ppsingh/SECTOR-multilabel-mpnet_w"}	text-classification	ppsingh/iki_sector_setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:ppsingh/SECTOR-multilabel-mpnet_w", "co2_eq_emissions", "region:us" ]	2024-02-12T15:28:40+00:00	[ "2209.11055" ]	[]	TAGS #setfit #safetensors #mpnet #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-ppsingh/SECTOR-multilabel-mpnet_w #co2_eq_emissions #region-us	SetFit with ppsingh/SECTOR-multilabel-mpnet\_w ============================================== This is a SetFit model that can be used for Text Classification. This SetFit model uses ppsingh/SECTOR-multilabel-mpnet\_w as the Sentence Transformer embedding model. A SetFitHead instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a Sentence Transformer with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. Model Details ------------- ### Model Description * Model Type: SetFit * Sentence Transformer body: ppsingh/SECTOR-multilabel-mpnet\_w * Classification head: a SetFitHead instance * Maximum Sequence Length: 512 tokens * Number of Classes: 4 classes ### Model Sources * Repository: SetFit on GitHub * Paper: Efficient Few-Shot Learning Without Prompts * Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts Uses ---- ### Direct Use for Inference First install the SetFit library: Then you can load this model and run inference. Training Details ---------------- ### Training Set Metrics * Training Dataset: 250 \| Class \| Positive Count of Class\| \|:-------------\|:--------\| \| Economy-wide \| 88 \| \| Energy \| 63 \| \| Other Sector \| 64 \| \| Transport \| 139 \| * Validation Dataset: 42 \| Class \| Positive Count of Class\| \|:-------------\|:--------\| \| Economy-wide \| 15 \| \| Energy \| 11 \| \| Other Sector \| 11 \| \| Transport \| 24 \| ### Training Hyperparameters * batch\_size: (16, 2) * num\_epochs: (1, 10) * max\_steps: -1 * sampling\_strategy: oversampling * body\_learning\_rate: (2e-05, 1e-05) * head\_learning\_rate: 0.01 * loss: CosineSimilarityLoss * distance\_metric: cosine\_distance * margin: 0.25 * end\_to\_end: False * use\_amp: False * warmup\_proportion: 0.01 * seed: 42 * eval\_max\_steps: -1 * load\_best\_model\_at\_end: False ### Training Results ### Classifier Training Results * Micro Avg: Precision = 0.866, Recall = 0.852, F1 = 0.859504 * Samples Avg: Precision = 0.869, Recall = 0.861, F1 = 0.848 ### Environmental Impact Carbon emissions were measured using CodeCarbon. * Carbon Emitted: 0.026 kg of CO2 * Hours Used: 0.622 hours ### Training Hardware * On Cloud: No * GPU Model: 1 x Tesla T4 * CPU Model: Intel(R) Xeon(R) CPU @ 2.00GHz * RAM Size: 12.67 GB ### Framework Versions * Python: 3.10.12 * SetFit: 1.0.3 * Sentence Transformers: 2.3.1 * Transformers: 4.35.2 * PyTorch: 2.1.0+cu121 * Datasets: 2.3.0 * Tokenizers: 0.15.1 ### BibTeX	[ "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: ppsingh/SECTOR-multilabel-mpnet\\_w\n* Classification head: a SetFitHead instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 4 classes", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics\n\n\n\n* Training Dataset: 250\n\| Class \| Positive Count of Class\|\n\|:-------------\|:--------\|\n\| Economy-wide \| 88 \|\n\| Energy \| 63 \|\n\| Other Sector \| 64 \|\n\| Transport \| 139 \|\n* Validation Dataset: 42\n\| Class \| Positive Count of Class\|\n\|:-------------\|:--------\|\n\| Economy-wide \| 15 \|\n\| Energy \| 11 \|\n\| Other Sector \| 11 \|\n\| Transport \| 24 \|", "### Training Hyperparameters\n\n\n* batch\\_size: (16, 2)\n* num\\_epochs: (1, 10)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.01\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False", "### Training Results", "### Classifier Training Results\n\n\n\n\n* Micro Avg: Precision = 0.866, Recall = 0.852, F1 = 0.859504\n* Samples Avg: Precision = 0.869, Recall = 0.861, F1 = 0.848", "### Environmental Impact\n\n\nCarbon emissions were measured using CodeCarbon.\n\n\n* Carbon Emitted: 0.026 kg of CO2\n* Hours Used: 0.622 hours", "### Training Hardware\n\n\n* On Cloud: No\n* GPU Model: 1 x Tesla T4\n* CPU Model: Intel(R) Xeon(R) CPU @ 2.00GHz\n* RAM Size: 12.67 GB", "### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.3.0\n* Tokenizers: 0.15.1", "### BibTeX" ]	[ "TAGS\n#setfit #safetensors #mpnet #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-ppsingh/SECTOR-multilabel-mpnet_w #co2_eq_emissions #region-us \n", "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: ppsingh/SECTOR-multilabel-mpnet\\_w\n* Classification head: a SetFitHead instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 4 classes", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics\n\n\n\n* Training Dataset: 250\n\| Class \| Positive Count of Class\|\n\|:-------------\|:--------\|\n\| Economy-wide \| 88 \|\n\| Energy \| 63 \|\n\| Other Sector \| 64 \|\n\| Transport \| 139 \|\n* Validation Dataset: 42\n\| Class \| Positive Count of Class\|\n\|:-------------\|:--------\|\n\| Economy-wide \| 15 \|\n\| Energy \| 11 \|\n\| Other Sector \| 11 \|\n\| Transport \| 24 \|", "### Training Hyperparameters\n\n\n* batch\\_size: (16, 2)\n* num\\_epochs: (1, 10)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.01\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False", "### Training Results", "### Classifier Training Results\n\n\n\n\n* Micro Avg: Precision = 0.866, Recall = 0.852, F1 = 0.859504\n* Samples Avg: Precision = 0.869, Recall = 0.861, F1 = 0.848", "### Environmental Impact\n\n\nCarbon emissions were measured using CodeCarbon.\n\n\n* Carbon Emitted: 0.026 kg of CO2\n* Hours Used: 0.622 hours", "### Training Hardware\n\n\n* On Cloud: No\n* GPU Model: 1 x Tesla T4\n* CPU Model: Intel(R) Xeon(R) CPU @ 2.00GHz\n* RAM Size: 12.67 GB", "### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.3.0\n* Tokenizers: 0.15.1", "### BibTeX" ]	[ 74, 63, 55, 31, 137, 169, 4, 59, 37, 43, 58, 6 ]	[ "passage: TAGS\n#setfit #safetensors #mpnet #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-ppsingh/SECTOR-multilabel-mpnet_w #co2_eq_emissions #region-us \n### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: ppsingh/SECTOR-multilabel-mpnet\\_w\n* Classification head: a SetFitHead instance\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 4 classes### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts\n\n\nUses\n----### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------### Training Set Metrics\n\n\n\n* Training Dataset: 250\n\| Class \| Positive Count of Class\|\n\|:-------------\|:--------\|\n\| Economy-wide \| 88 \|\n\| Energy \| 63 \|\n\| Other Sector \| 64 \|\n\| Transport \| 139 \|\n* Validation Dataset: 42\n\| Class \| Positive Count of Class\|\n\|:-------------\|:--------\|\n\| Economy-wide \| 15 \|\n\| Energy \| 11 \|\n\| Other Sector \| 11 \|\n\| Transport \| 24 \|" ]	[ -0.11350846290588379, 0.17688247561454773, -0.0027909560594707727, 0.06764092296361923, 0.1317225694656372, 0.046585775911808014, 0.08001578599214554, 0.11215780675411224, -0.06492895632982254, 0.10052035748958588, 0.06920244544744492, 0.07581286877393723, 0.08287973701953888, 0.20567989349365234, 0.06136443465948105, -0.2513115406036377, 0.054595302790403366, -0.07014815509319305, -0.07360289990901947, 0.09841769188642502, 0.11014153063297272, -0.0865665152668953, 0.07217177748680115, -0.049573734402656555, -0.06843366473913193, -0.0014390443684533238, -0.07830208539962769, -0.04766469448804855, 0.06757697463035583, 0.06446406245231628, 0.09109027683734894, -0.02432386204600334, 0.08192070573568344, -0.26739412546157837, 0.02061866782605648, 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null	null	null	# Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction	{"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Policy_Gradient", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	Hongsong/Policy_Gradient	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	2024-02-12T15:28:56+00:00	[]	[]	TAGS #CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us	# Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL	[ "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ "TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ 39, 54 ]	[ "passage: TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ 0.007526164408773184, -0.12498430907726288, -0.0013541718944907188, 0.09601131081581116, 0.11848696321249008, -0.04186001420021057, 0.11405468732118607, 0.05624859035015106, 0.09539441019296646, 0.04239490255713463, 0.13636724650859833, 0.06906966865062714, -0.004102868959307671, 0.12412862479686737, 0.09840741008520126, -0.26058563590049744, 0.07420794665813446, -0.04403980076313019, -0.009944677352905273, 0.10139261186122894, 0.07836852967739105, -0.08325441926717758, 0.051592715084552765, 0.00009572553972247988, -0.044259943068027496, 0.0321260429918766, 0.013628939166665077, -0.053157225251197815, 0.1606452465057373, -0.07313758134841919, 0.10494591295719147, -0.03843724727630615, 0.14574295282363892, -0.1126825287938118, 0.04758213832974434, 0.05111503228545189, -0.04548581689596176, 0.03848232328891754, -0.12538743019104004, -0.06033875793218613, 0.026815801858901978, -0.015865681692957878, 0.12249194830656052, 0.03647647053003311, -0.1777559220790863, 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null	null	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "258.12 +/- 14.11", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	MarkelTaichi/ppo-LunarLander-v2	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-12T15:31:24+00:00	[]	[]	TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code	[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 39, 41, 17 ]	[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 0.03942384943366051, 0.04900386184453964, -0.005304091144353151, 0.026427261531352997, 0.107408307492733, -0.026511888951063156, 0.11188238859176636, 0.0814051404595375, 0.10722193866968155, 0.04762078449130058, 0.08338645845651627, 0.06030960753560066, 0.05080918222665787, 0.2571701407432556, 0.04754156619310379, -0.22987541556358337, 0.036159250885248184, -0.04869936779141426, 0.12395193427801132, 0.07178173214197159, -0.0038484656251966953, -0.06485428661108017, 0.020415637642145157, -0.013290755450725555, 0.05367108806967735, 0.04282612353563309, -0.01716216839849949, -0.08207534998655319, 0.07169748842716217, -0.06345846503973007, 0.06986866891384125, 0.07677983492612839, 0.13218913972377777, -0.17832116782665253, 0.029566360637545586, 0.02571309357881546, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	mrbmaryam/Yarn-Mistral-7b-128k_Fine-Tuned4LogParsing-r2	[ "transformers", "safetensors", "mistral", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-12T15:32:46+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 61, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #custom_code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04616480693221092, 0.17144028842449188, -0.0055341701954603195, 0.016794556751847267, 0.1041298434138298, 0.012164974585175514, 0.05729488283395767, 0.11374307423830032, -0.027801724150776863, 0.11755378544330597, 0.03426181524991989, 0.09851478040218353, 0.11611800640821457, 0.15170679986476898, 0.003967443946748972, -0.21878890693187714, 0.050096772611141205, -0.11655071377754211, -0.028412170708179474, 0.11944516748189926, 0.14741890132427216, -0.09945914149284363, 0.07144719362258911, -0.030579213052988052, -0.01560430321842432, -0.03153681755065918, -0.06499121338129044, -0.043894823640584946, 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null	null	transformers	# How to use you can the model via the command ```python from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("not-lain/MyRepo1.0", trust_remote_code=True) ``` or you can use the pipeline ```python from transformers import pipeline pipe = pipeline(model="not-lain/MyRepo1.0", trust_remote_code=True) pipe( "url", download=True, # will call the download_img method clean_output=False # will be passed as postprocess_kwargs ) ``` # parameters the pipeline supports the following parameters : * download * clean_output you can also use the following method to download images from the web ```python pipe.download_img(url) ```	{"tags": ["custom_code"]}	image-classification	not-lain/MyRepo1.0	[ "transformers", "safetensors", "MobileNetV1", "image-classification", "custom_code", "autotrain_compatible", "region:us" ]	2024-02-12T15:33:46+00:00	[]	[]	TAGS #transformers #safetensors #MobileNetV1 #image-classification #custom_code #autotrain_compatible #region-us	# How to use you can the model via the command or you can use the pipeline # parameters the pipeline supports the following parameters : * download * clean_output you can also use the following method to download images from the web	[ "# How to use\nyou can the model via the command\n\nor you can use the pipeline", "# parameters\nthe pipeline supports the following parameters :\n* download\n* clean_output\n\nyou can also use the following method to download images from the web" ]	[ "TAGS\n#transformers #safetensors #MobileNetV1 #image-classification #custom_code #autotrain_compatible #region-us \n", "# How to use\nyou can the model via the command\n\nor you can use the pipeline", "# parameters\nthe pipeline supports the following parameters :\n* download\n* clean_output\n\nyou can also use the following method to download images from the web" ]	[ 37, 18, 33 ]	[ "passage: TAGS\n#transformers #safetensors #MobileNetV1 #image-classification #custom_code #autotrain_compatible #region-us \n# How to use\nyou can the model via the command\n\nor you can use the pipeline# parameters\nthe pipeline supports the following parameters :\n* download\n* clean_output\n\nyou can also use the following method to download images from the web" ]	[ -0.08543433994054794, 0.17710722982883453, -0.0020266149658709764, 0.027668774127960205, 0.13996362686157227, -0.00047914619790390134, 0.14528463780879974, 0.009274172596633434, -0.03367611765861511, -0.040988557040691376, 0.16984272003173828, 0.10246869921684265, -0.0211840458214283, 0.21831873059272766, 0.019588157534599304, -0.24902799725532532, 0.012690613977611065, -0.06693205237388611, 0.030223442241549492, 0.10085167735815048, 0.04799564927816391, -0.04849748685956001, 0.11935416609048843, -0.05998532846570015, -0.10321164131164551, 0.025021309033036232, 0.035543981939554214, -0.035619936883449554, 0.0030600959435105324, 0.04650074988603592, 0.11579933017492294, 0.08142668753862381, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-hf"}	null	simonycl/llama-2-7b-hf-cohere-KMeansIter-0.1-Llama-2-7b-hf-round-4-iter-0	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "region:us" ]	2024-02-12T15:34:16+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 41, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.13237035274505615, 0.20393601059913635, -0.002511046128347516, 0.02874687872827053, 0.07912357151508331, 0.019634027034044266, 0.04979075863957405, 0.13531364500522614, 0.020043307915329933, 0.10580451786518097, 0.0737132653594017, 0.11718367785215378, 0.10926163196563721, 0.20654499530792236, 0.003890186781063676, -0.15440793335437775, 0.024214256554841995, -0.08298544585704803, 0.006814117077738047, 0.1290476769208908, 0.14319083094596863, -0.10468140989542007, 0.0831538662314415, -0.014203370548784733, 0.0008161105797626078, -0.03246506303548813, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	tommymarto/LernnaviBERT_mcqbert1_students_answers_384_lstm_seq_len_40	[ "transformers", "safetensors", "bert", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T15:38:17+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 33, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05835729464888573, 0.21513818204402924, -0.0027643628418445587, 0.027697166427969933, 0.12558044493198395, -0.00036080856807529926, 0.038943830877542496, 0.12901438772678375, -0.01060954574495554, 0.1100858673453331, 0.03811120614409447, 0.09515609592199326, 0.09883695095777512, 0.1663336604833603, 0.04276633635163307, -0.21661408245563507, 0.003279293654486537, -0.08966897428035736, 0.019332116469740868, 0.10749275237321854, 0.13046206533908844, -0.10735081136226654, 0.07876921445131302, -0.03911958634853363, -0.01563864015042782, -0.002511978382244706, -0.09296175837516785, -0.07015316188335419, 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null	null	null	pretrained models used in https://github.com/shibing624/parrots # 在线语音生成speaker模型 \| speaker name \| 说话人名 \| character \| 角色特点 \| language \| 语言 \| \|--\|--\|--\|--\|--\|--\| \| KuileBlanc \| 葵·勒布朗 \| lady \| 标准美式女声 \| en \| 英 \| \| LongShouRen \| 龙守仁 \| gentleman \| 标准美式男声 \| en \| 英 \| \| MaiMai \| 卖卖\| singing female anchor \| 唱歌女主播声 \| zh \| 中 \| \| XingTong \| 星瞳 \| singing ai girl \| 活泼女声 \| zh \| 中 \| \| XuanShen \| 炫神 \| game male anchor \| 游戏男主播声 \| zh \| 中 \| \| KusanagiNene \| 草薙寧々 \| loli \| 萝莉女学生声 \| ja \| 日 \| - 【GPT SoVITS】在线合集：https://www.modelscope.cn/studios/xzjosh/GPT-SoVITS - 数据集下载：https://huggingface.co/datasets/XzJosh/audiodataset - 声音归属：扇宝 https://space.bilibili.com/698438232 - GPT-SoVITS项目：https://github.com/RVC-Boss/GPT-SoVITS - 使用本模型请严格遵守法律法规！发布二创作品请标注本项目作者及链接、作品使用GPT-SoVITS AI生成！ #### relate models - [shibing624/parrots-gpt-sovits-speaker-maimai](https://huggingface.co/shibing624/parrots-gpt-sovits-speaker-maimai) \| speaker name \| 说话人名 \| character \| 角色特点 \| language \| 语言 \| \|--\|--\|--\|--\|--\|--\| \| MaiMai \| 卖卖\| singing female anchor \| 唱歌女主播声 \| zh \| 中 \|	{"language": ["zh", "ja", "en"], "license": "cc-by-nc-4.0", "pipeline_tag": "text-to-speech"}	text-to-speech	shibing624/parrots-gpt-sovits-speaker	[ "text-to-speech", "zh", "ja", "en", "license:cc-by-nc-4.0", "region:us" ]	2024-02-12T15:38:20+00:00	[]	[ "zh", "ja", "en" ]	TAGS #text-to-speech #zh #ja #en #license-cc-by-nc-4.0 #region-us	pretrained models used in URL 在线语音生成speaker模型 =============== * 【GPT SoVITS】在线合集：URL * 数据集下载：URL * 声音归属：扇宝 URL * GPT-SoVITS项目：URL * 使用本模型请严格遵守法律法规！发布二创作品请标注本项目作者及链接、作品使用GPT-SoVITS AI生成！ #### relate models * shibing624/parrots-gpt-sovits-speaker-maimai	[ "#### relate models\n\n\n* shibing624/parrots-gpt-sovits-speaker-maimai" ]	[ "TAGS\n#text-to-speech #zh #ja #en #license-cc-by-nc-4.0 #region-us \n", "#### relate models\n\n\n* shibing624/parrots-gpt-sovits-speaker-maimai" ]	[ 30, 27 ]	[ "passage: TAGS\n#text-to-speech #zh #ja #en #license-cc-by-nc-4.0 #region-us \n#### relate models\n\n\n* shibing624/parrots-gpt-sovits-speaker-maimai" ]	[ -0.05455608293414116, 0.009006674401462078, -0.0061087897047400475, 0.007012392859905958, 0.024766376242041588, -0.028441550210118294, 0.18300209939479828, 0.056224532425403595, 0.07433578372001648, 0.008853156119585037, 0.09217044711112976, 0.14482152462005615, 0.10077983140945435, -0.03377651795744896, 0.042936794459819794, -0.27455687522888184, 0.03588246554136276, 0.02273736707866192, -0.05284557119011879, 0.06761408597230911, 0.06152861937880516, 0.06065678969025612, 0.033164843916893005, 0.06764322519302368, 0.0022393567487597466, -0.05704904720187187, -0.039319202303886414, -0.03906207159161568, 0.10832459479570389, 0.03136787191033363, -0.04336227849125862, 0.05432385951280594, 0.0492292158305645, -0.18206211924552917, -0.00526568153873086, -0.07015823572874069, -0.05232797563076019, -0.00883352942764759, 0.01238094363361597, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.2"}	null	Flamoverse/merged_model	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	2024-02-12T15:44:48+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 42, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.12410319596529007, 0.2010166496038437, -0.0028893498238176107, 0.02710896171629429, 0.08296072483062744, 0.01904967427253723, 0.052783653140068054, 0.12995868921279907, 0.03010331466794014, 0.11474749445915222, 0.07281026989221573, 0.1246524378657341, 0.11124330013990402, 0.20343951880931854, 0.005492560565471649, -0.16418711841106415, 0.028107086196541786, -0.08119428902864456, 0.016911618411540985, 0.12729473412036896, 0.14302164316177368, -0.10105469077825546, 0.07827022671699524, -0.019680403172969818, 0.003734445432201028, -0.03514719754457474, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-300m-england-0213-parallel_attempt-11-22-avatar This model is a fine-tuned version of [vitouphy/wav2vec2-xls-r-300m-english](https://huggingface.co/vitouphy/wav2vec2-xls-r-300m-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3333 - Wer: 0.1934 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1227 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 1.5653 \| 1.0 \| 1227 \| 0.2904 \| 0.2835 \| \| 0.2758 \| 2.0 \| 2454 \| 0.2609 \| 0.2547 \| \| 0.2302 \| 3.0 \| 3681 \| 0.2469 \| 0.2395 \| \| 0.1976 \| 4.0 \| 4908 \| 0.2528 \| 0.2473 \| \| 0.1714 \| 5.0 \| 6135 \| 0.2425 \| 0.2242 \| \| 0.1526 \| 6.0 \| 7362 \| 0.2516 \| 0.2335 \| \| 0.1299 \| 7.0 \| 8589 \| 0.2351 \| 0.2058 \| \| 0.1049 \| 8.0 \| 9816 \| 0.2389 \| 0.2039 \| \| 0.0868 \| 9.0 \| 11043 \| 0.2452 \| 0.2037 \| \| 0.0735 \| 10.0 \| 12270 \| 0.2643 \| 0.2039 \| \| 0.0591 \| 11.0 \| 13497 \| 0.2723 \| 0.1983 \| \| 0.0474 \| 12.0 \| 14724 \| 0.2885 \| 0.1965 \| \| 0.0386 \| 13.0 \| 15951 \| 0.3056 \| 0.1950 \| \| 0.0305 \| 14.0 \| 17178 \| 0.3231 \| 0.1951 \| \| 0.0253 \| 15.0 \| 18405 \| 0.3333 \| 0.1934 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.14.7 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "vitouphy/wav2vec2-xls-r-300m-english", "model-index": [{"name": "wav2vec2-300m-england-0213-parallel_attempt-11-22-avatar", "results": []}]}	automatic-speech-recognition	Lin25/wav2vec2-300m-england-0213-parallel_attempt-11-22-avatar	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:vitouphy/wav2vec2-xls-r-300m-english", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-12T15:47:35+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us	wav2vec2-300m-england-0213-parallel\_attempt-11-22-avatar ========================================================= This model is a fine-tuned version of vitouphy/wav2vec2-xls-r-300m-english on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.3333 * Wer: 0.1934 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.001 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1227 * num\_epochs: 15 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.36.0.dev0 * Pytorch 1.12.1+cu113 * Datasets 2.14.7 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0" ]	[ 80, 159, 4, 40 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-vitouphy/wav2vec2-xls-r-300m-english #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1227\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 1.12.1+cu113\n* Datasets 2.14.7\n* Tokenizers 0.15.0" ]	[ -0.12465691566467285, 0.13422846794128418, -0.0033921669237315655, 0.04942885786294937, 0.08694503456354141, 0.02121187187731266, 0.10567963868379593, 0.14321152865886688, -0.05826539918780327, 0.12673917412757874, 0.11136090010404587, 0.0845835879445076, 0.07596516609191895, 0.1459297239780426, -0.02841995656490326, -0.29495757818222046, 0.0224810428917408, -0.016161799430847168, -0.1142239198088646, 0.10135062783956528, 0.08888418972492218, -0.10815936326980591, 0.030373359099030495, 0.006174994166940451, -0.08791209757328033, -0.013285565190017223, -0.03192336484789848, -0.0618252195417881, 0.10925420373678207, 0.05721037834882736, 0.0789627730846405, 0.038640573620796204, 0.0832996517419815, -0.27146294713020325, 0.013236995786428452, 0.05100950598716736, 0.02037181332707405, 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null	null	transformers	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # hdeldar/distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.1972 - Validation Loss: 0.5241 - Train Matthews Correlation: 0.5294 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Matthews Correlation \| Epoch \| \|:----------:\|:---------------:\|:--------------------------:\|:-----:\| \| 0.5250 \| 0.4718 \| 0.4527 \| 0 \| \| 0.3234 \| 0.4414 \| 0.5235 \| 1 \| \| 0.1972 \| 0.5241 \| 0.5294 \| 2 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "hdeldar/distilbert-base-uncased-finetuned-cola", "results": []}]}	text-classification	hdeldar/distilbert-base-uncased-finetuned-cola	[ "transformers", "tf", "tensorboard", "distilbert", "text-classification", "generated_from_keras_callback", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T15:47:40+00:00	[]	[]	TAGS #transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	hdeldar/distilbert-base-uncased-finetuned-cola ============================================== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 0.1972 * Validation Loss: 0.5241 * Train Matthews Correlation: 0.5294 * Epoch: 2 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': 1602, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} * training\_precision: float32 ### Training results ### Framework versions * Transformers 4.35.2 * TensorFlow 2.15.0 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1602, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1602, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 74, 304, 4, 31 ]	[ "passage: TAGS\n#transformers #tf #tensorboard #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 1602, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.0717087835073471, 0.07836209237575531, -0.008731909096240997, 0.07707353681325912, 0.11751093715429306, 0.04217846319079399, 0.08105655014514923, 0.13307541608810425, -0.06084012985229492, 0.15353593230247498, 0.09658828377723694, 0.16891784965991974, 0.04174836352467537, 0.1345803141593933, -0.07348564267158508, -0.17685890197753906, 0.04896768182516098, -0.03810836002230644, -0.05636991932988167, 0.06397277861833572, 0.07607842236757278, -0.05728860944509506, 0.07183077931404114, -0.02829417958855629, -0.06504333019256592, -0.005140550434589386, 0.017101209610700607, -0.05723966285586357, 0.06997264176607132, 0.07838574051856995, 0.0499156154692173, 0.01916869916021824, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	tommymarto/LernnaviBERT_mcqbert1_students_answers_mlp	[ "transformers", "safetensors", "bert", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T15:48:01+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 33, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05835729464888573, 0.21513818204402924, -0.0027643628418445587, 0.027697166427969933, 0.12558044493198395, -0.00036080856807529926, 0.038943830877542496, 0.12901438772678375, -0.01060954574495554, 0.1100858673453331, 0.03811120614409447, 0.09515609592199326, 0.09883695095777512, 0.1663336604833603, 0.04276633635163307, -0.21661408245563507, 0.003279293654486537, -0.08966897428035736, 0.019332116469740868, 0.10749275237321854, 0.13046206533908844, -0.10735081136226654, 0.07876921445131302, -0.03911958634853363, -0.01563864015042782, -0.002511978382244706, -0.09296175837516785, -0.07015316188335419, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	feature-extraction	furrutiav/bert_qa_extractor_cockatiel_2022_z_value_over_subsample_it_727	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T15:51:58+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 39, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.052746038883924484, 0.20255789160728455, -0.0045078229159116745, 0.0248473659157753, 0.10497838258743286, 0.00675728265196085, 0.06521498411893845, 0.11486967653036118, -0.0023755673319101334, 0.12028469145298004, 0.027631845325231552, 0.08119397610425949, 0.12110675126314163, 0.15393014252185822, 0.005160121712833643, -0.24253977835178375, 0.05344875901937485, -0.09366832673549652, 0.004077504388988018, 0.11452110856771469, 0.1343945860862732, -0.10780399292707443, 0.08976872265338898, -0.00683097867295146, -0.01712046191096306, -0.015751034021377563, -0.07134060561656952, -0.06668227165937424, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2952 - Accuracy: 0.4875 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 80 \| 1.6148 \| 0.3375 \| \| 1.6678 \| 2.0 \| 160 \| 1.3553 \| 0.4625 \| \| 0.6788 \| 3.0 \| 240 \| 1.2952 \| 0.4875 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224", "model-index": [{"name": "results", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.4875, "name": "Accuracy"}]}]}]}	image-classification	alhafizfadhil/results	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T15:56:04+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	results ======= This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.2952 * Accuracy: 0.4875 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 83, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.12238069623708725, 0.12749743461608887, -0.0020354536827653646, 0.12762849032878876, 0.15398406982421875, 0.015802308917045593, 0.14556531608104706, 0.1236652359366417, -0.05028417706489563, 0.05516281723976135, 0.14194603264331818, 0.1209644302725792, 0.027463717386126518, 0.15290667116641998, -0.04535863921046257, -0.2119094878435135, 0.02110901102423668, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper This model is a fine-tuned version of [bofenghuang/whisper-large-v3-french-distil-dec16](https://huggingface.co/bofenghuang/whisper-large-v3-french-distil-dec16) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1122 - Wer: 5.3589 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.4042 \| 0.38 \| 20 \| 0.2881 \| 4.5501 \| \| 0.3463 \| 0.77 \| 40 \| 0.2060 \| 4.3478 \| \| 0.125 \| 1.15 \| 60 \| 0.1498 \| 4.7523 \| \| 0.0606 \| 1.54 \| 80 \| 0.1154 \| 4.3478 \| \| 0.0884 \| 1.92 \| 100 \| 0.1026 \| 4.8534 \| \| 0.0189 \| 2.31 \| 120 \| 0.0995 \| 4.8534 \| \| 0.0235 \| 2.69 \| 140 \| 0.1085 \| 4.6512 \| \| 0.0126 \| 3.08 \| 160 \| 0.1100 \| 4.6512 \| \| 0.0096 \| 3.46 \| 180 \| 0.1114 \| 5.2578 \| \| 0.0214 \| 3.85 \| 200 \| 0.1122 \| 5.3589 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "bofenghuang/whisper-large-v3-french-distil-dec16", "model-index": [{"name": "whisper", "results": []}]}	automatic-speech-recognition	sbesnard/whisper	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:bofenghuang/whisper-large-v3-french-distil-dec16", "license:mit", "endpoints_compatible", "region:us" ]	2024-02-12T16:00:17+00:00	[]	[]	TAGS #transformers #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-bofenghuang/whisper-large-v3-french-distil-dec16 #license-mit #endpoints_compatible #region-us	whisper ======= This model is a fine-tuned version of bofenghuang/whisper-large-v3-french-distil-dec16 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.1122 * Wer: 5.3589 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-06 * train\_batch\_size: 8 * eval\_batch\_size: 4 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 20 * training\_steps: 200 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* training\\_steps: 200", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-bofenghuang/whisper-large-v3-french-distil-dec16 #license-mit #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* training\\_steps: 200", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 77, 143, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-bofenghuang/whisper-large-v3-french-distil-dec16 #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* training\\_steps: 200### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	automatic-speech-recognition	BlahBlah314/Whisper_LargeV3FR_V3-Norm	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-12T16:02:40+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 45, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05983767658472061, 0.15663617849349976, -0.00414510490372777, 0.012621625326573849, 0.10675175487995148, 0.00396517850458622, 0.07058298587799072, 0.10818448662757874, -0.014333043247461319, 0.1301925629377365, 0.031459614634513855, 0.10620059072971344, 0.11486424505710602, 0.17755427956581116, -0.00021593451674561948, -0.21627318859100342, 0.06542544066905975, -0.11467250436544418, 0.023902224376797676, 0.1205042228102684, 0.14280648529529572, -0.10782013833522797, 0.0710505023598671, -0.02651231922209263, -0.014152529649436474, -0.030523719266057014, -0.05870387330651283, -0.06662651896476746, 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null	null	diffusers	This a an fp16 variant of Proteus V2.0 https://huggingface.co/dataautogpt3/ProteusV0.2 currently under the gpl-v3 licence. simply created by ```py import torch from diffusers import DiffusionPipeline pipeline = DiffusionPipeline.from_pretrained("dataautogpt3/ProteusV0.2", torch_dtype=torch.float16) pipeline.save_pretrained('fp16_ProteusV0.2', safe_serialization=True, variant='fp16') ``` See the original model for details. The fp32 version of the model, even when converted to fp16 when loading, uses up to much RAM hence my need for this version. Dave	{"license": "gpl-3.0"}	null	Vargol/ProteusV0.2	[ "diffusers", "license:gpl-3.0", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	2024-02-12T16:03:28+00:00	[]	[]	TAGS #diffusers #license-gpl-3.0 #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us	This a an fp16 variant of Proteus V2.0 URL currently under the gpl-v3 licence. simply created by See the original model for details. The fp32 version of the model, even when converted to fp16 when loading, uses up to much RAM hence my need for this version. 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null	null	transformers	<iframe src="https://davmel-georgian-homonym-disambiguation.hf.space" frameborder="0" width="850" height="450" ></iframe> This model is capable of determining the definition of the homonym "ბარი" located at the position marked by the [MASK] token. It is a simple Transformer model trained on a hand classified dataset comprising 6000 hand-classified sentences. I've masked the homonyms from the sentences and replaced them with their synonyms according to the definitions used. For example, I replaced ”ბარი” with ”დაბლობი” (lowland) where the homonym referred to the field. The model predicts "თო" when it interprets the homonym as "Shovel," "დაბ" when it interprets it as "lowland," and "კაფე" when it interprets it as "Cafe." My fine-tuned transformer model is based on a pre-trained transformer model which was downloaded from: https://huggingface.co/Davit6174/georgian-distilbert-mlm	{"language": ["ka"], "license": "mit", "datasets": ["davmel/ka_homonym_disambiguation"]}	fill-mask	davmel/ka_homonym_disambiguation_FM	[ "transformers", "safetensors", "distilbert", "fill-mask", "ka", "dataset:davmel/ka_homonym_disambiguation", "license:mit", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	2024-02-12T16:05:34+00:00	[]	[ "ka" ]	TAGS #transformers #safetensors #distilbert #fill-mask #ka #dataset-davmel/ka_homonym_disambiguation #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us	<iframe src="URL" frameborder="0" width="850" height="450" ></iframe> This model is capable of determining the definition of the homonym "ბარი" located at the position marked by the [MASK] token. It is a simple Transformer model trained on a hand classified dataset comprising 6000 hand-classified sentences. I've masked the homonyms from the sentences and replaced them with their synonyms according to the definitions used. For example, I replaced ”ბარი” with ”დაბლობი” (lowland) where the homonym referred to the field. The model predicts "თო" when it interprets the homonym as "Shovel," "დაბ" when it interprets it as "lowland," and "კაფე" when it interprets it as "Cafe." My fine-tuned transformer model is based on a pre-trained transformer model which was downloaded from: URL	[]	[ "TAGS\n#transformers #safetensors #distilbert #fill-mask #ka #dataset-davmel/ka_homonym_disambiguation #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]	[ 66 ]	[ "passage: TAGS\n#transformers #safetensors #distilbert #fill-mask #ka #dataset-davmel/ka_homonym_disambiguation #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n" ]	[ -0.03903213515877724, 0.0013975773472338915, -0.004077879246324301, 0.013032009825110435, 0.08423586189746857, 0.016335217282176018, 0.13651834428310394, 0.06975005567073822, 0.07438281923532486, 0.030771445482969284, 0.18290261924266815, 0.1620953381061554, -0.020339958369731903, 0.182623952627182, -0.10250137001276016, -0.1758507788181305, 0.11028865724802017, -0.018129093572497368, -0.06385425478219986, 0.05086278170347214, 0.11922258883714676, -0.09613755345344543, 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null	null	transformers	# Whisper Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper). Disclaimer: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. ## Model details Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. The models were trained on either English-only data or multilingual data. The English-only models were trained on the task of speech recognition. The multilingual models were trained on both speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the same language as the audio. For speech translation, the model predicts transcriptions to a different language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The checkpoints are summarised in the following table with links to the models on the Hub: \| Size \| Parameters \| English-only \| Multilingual \| \|----------\|------------\|------------------------------------------------------\|-----------------------------------------------------\| \| tiny \| 39 M \| [✓](https://huggingface.co/openai/whisper-tiny.en) \| [✓](https://huggingface.co/openai/whisper-tiny) \| \| base \| 74 M \| [✓](https://huggingface.co/openai/whisper-base.en) \| [✓](https://huggingface.co/openai/whisper-base) \| \| small \| 244 M \| [✓](https://huggingface.co/openai/whisper-small.en) \| [✓](https://huggingface.co/openai/whisper-small) \| \| medium \| 769 M \| [✓](https://huggingface.co/openai/whisper-medium.en) \| [✓](https://huggingface.co/openai/whisper-medium) \| \| large \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large) \| \| large-v2 \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large-v2) \| # Usage To transcribe audio samples, the model has to be used alongside a [`WhisperProcessor`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor). The `WhisperProcessor` is used to: 1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) 2. Post-process the model outputs (converting them from tokens to text) The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order: 1. The transcription always starts with the `<\|startoftranscript\|>` token 2. The second token is the language token (e.g. `<\|en\|>` for English) 3. The third token is the "task token". It can take one of two values: `<\|transcribe\|>` for speech recognition or `<\|translate\|>` for speech translation 4. In addition, a `<\|notimestamps\|>` token is added if the model should not include timestamp prediction Thus, a typical sequence of context tokens might look as follows: ``` <\|startoftranscript\|> <\|en\|> <\|transcribe\|> <\|notimestamps\|> ``` Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps. These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, the Whisper model will automatically predict the output langauge and task itself. The context tokens can be set accordingly: ```python model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe") ``` Which forces the model to predict in English under the task of speech recognition. ## Transcription ### English to English In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language (English) and task (transcribe). ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> model.config.forced_decoder_ids = None >>> # load dummy dataset and read audio files >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False) ['<\|startoftranscript\|><\|en\|><\|transcribe\|><\|notimestamps\|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<\|endoftext\|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.'] ``` The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`. ### French to French The following example demonstrates French to French transcription by setting the decoder ids appropriately. ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids) ['<\|startoftranscript\|><\|fr\|><\|transcribe\|><\|notimestamps\|> Un vrai travail intéressant va enfin être mené sur ce sujet.<\|endoftext\|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Un vrai travail intéressant va enfin être mené sur ce sujet.'] ``` ## Translation Setting the task to "translate" forces the Whisper model to perform speech translation. ### French to English ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="translate") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' A very interesting work, we will finally be given on this subject.'] ``` ## Evaluation This code snippet shows how to evaluate Whisper Small on [LibriSpeech test-clean](https://huggingface.co/datasets/librispeech_asr): ```python >>> from datasets import load_dataset >>> from transformers import WhisperForConditionalGeneration, WhisperProcessor >>> import torch >>> from evaluate import load >>> librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test") >>> processor = WhisperProcessor.from_pretrained("openai/whisper-small") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to("cuda") >>> def map_to_pred(batch): >>> audio = batch["audio"] >>> input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features >>> batch["reference"] = processor.tokenizer._normalize(batch['text']) >>> >>> with torch.no_grad(): >>> predicted_ids = model.generate(input_features.to("cuda"))[0] >>> transcription = processor.decode(predicted_ids) >>> batch["prediction"] = processor.tokenizer._normalize(transcription) >>> return batch >>> result = librispeech_test_clean.map(map_to_pred) >>> wer = load("wer") >>> print(100 * wer.compute(references=result["reference"], predictions=result["prediction"])) 3.432213777886737 ``` ## Long-Form Transcription The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline can be run with batched inference. It can also be extended to predict sequence level timestamps by passing `return_timestamps=True`: ```python >>> import torch >>> from transformers import pipeline >>> from datasets import load_dataset >>> device = "cuda:0" if torch.cuda.is_available() else "cpu" >>> pipe = pipeline( >>> "automatic-speech-recognition", >>> model="openai/whisper-small", >>> chunk_length_s=30, >>> device=device, >>> ) >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> prediction = pipe(sample.copy(), batch_size=8)["text"] " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel." >>> # we can also return timestamps for the predictions >>> prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"] [{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.', 'timestamp': (0.0, 5.44)}] ``` Refer to the blog post [ASR Chunking](https://huggingface.co/blog/asr-chunking) for more details on the chunking algorithm. ## Fine-Tuning The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step guide to fine-tuning the Whisper model with as little as 5 hours of labelled data. ### Evaluated Use The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. ## Training Data The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. As discussed in [the accompanying paper](https://cdn.openai.com/papers/whisper.pdf), we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language. ## Performance and Limitations Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf). In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. ## Broader Implications We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. ### BibTeX entry and citation info ```bibtex @misc{radford2022whisper, doi = {10.48550/ARXIV.2212.04356}, url = {https://arxiv.org/abs/2212.04356}, author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya}, title = {Robust Speech Recognition via Large-Scale Weak Supervision}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```	{"language": ["en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", false, "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", 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Recognition"}, "dataset": {"name": "LibriSpeech (other)", "type": "librispeech_asr", "config": "other", "split": "test", "args": {"language": "en"}}, "metrics": [{"type": "wer", "value": 7.628304527060248, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "hi", "split": "test", "args": {"language": "hi"}}, "metrics": [{"type": "wer", "value": 87.3, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 13.0", "type": "mozilla-foundation/common_voice_13_0", "config": "dv", "split": "test", "args": {"language": "dv"}}, "metrics": [{"type": "wer", "value": 125.69809089960707, "name": "Wer"}]}]}]}	automatic-speech-recognition	rishabhjain16/whisper-small	[ "transformers", "pytorch", "tf", "jax", "safetensors", "whisper", "automatic-speech-recognition", "audio", "hf-asr-leaderboard", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "arxiv:2212.04356", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2024-02-12T16:05:58+00:00	[ "2212.04356" ]	[ "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", 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#region-us	Whisper ======= Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al from OpenAI. The original code repository can be found here. Disclaimer: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. Model details ------------- Whisper is a Transformer based encoder-decoder model, also referred to as a sequence-to-sequence model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. The models were trained on either English-only data or multilingual data. The English-only models were trained on the task of speech recognition. The multilingual models were trained on both speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the same language as the audio. For speech translation, the model predicts transcriptions to a different language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the Hugging Face Hub. The checkpoints are summarised in the following table with links to the models on the Hub: Usage ===== To transcribe audio samples, the model has to be used alongside a 'WhisperProcessor'. The 'WhisperProcessor' is used to: 1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) 2. Post-process the model outputs (converting them from tokens to text) The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order: 1. The transcription always starts with the '<\|startoftranscript\|>' token 2. The second token is the language token (e.g. '<\|en\|>' for English) 3. The third token is the "task token". It can take one of two values: '<\|transcribe\|>' for speech recognition or '<\|translate\|>' for speech translation 4. In addition, a '<\|notimestamps\|>' token is added if the model should not include timestamp prediction Thus, a typical sequence of context tokens might look as follows: Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps. These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, the Whisper model will automatically predict the output langauge and task itself. The context tokens can be set accordingly: Which forces the model to predict in English under the task of speech recognition. Transcription ------------- ### English to English In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language (English) and task (transcribe). The context tokens can be removed from the start of the transcription by setting 'skip\_special\_tokens=True'. ### French to French The following example demonstrates French to French transcription by setting the decoder ids appropriately. Translation ----------- Setting the task to "translate" forces the Whisper model to perform speech translation. ### French to English Evaluation ---------- This code snippet shows how to evaluate Whisper Small on LibriSpeech test-clean: Long-Form Transcription ----------------------- The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers 'pipeline' method. Chunking is enabled by setting 'chunk\_length\_s=30' when instantiating the pipeline. With chunking enabled, the pipeline can be run with batched inference. It can also be extended to predict sequence level timestamps by passing 'return\_timestamps=True': Refer to the blog post ASR Chunking for more details on the chunking algorithm. Fine-Tuning ----------- The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog post Fine-Tune Whisper with Transformers provides a step-by-step guide to fine-tuning the Whisper model with as little as 5 hours of labelled data. ### Evaluated Use The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. Training Data ------------- The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. As discussed in the accompanying paper, we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language. Performance and Limitations --------------------------- Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in the paper accompanying this release. In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in the paper. It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. Broader Implications -------------------- We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. ### BibTeX entry and citation info	[ "### English to English\n\n\nIn this example, the context tokens are 'unforced', meaning the model automatically predicts the output language\n(English) and task (transcribe).\n\n\nThe context tokens can be removed from the start of the transcription by setting 'skip\\_special\\_tokens=True'.", "### French to French\n\n\nThe following example demonstrates French to French transcription by setting the decoder ids appropriately.\n\n\nTranslation\n-----------\n\n\nSetting the task to \"translate\" forces the Whisper model to perform speech translation.", "### French to English\n\n\nEvaluation\n----------\n\n\nThis code snippet shows how to evaluate Whisper Small on LibriSpeech test-clean:\n\n\nLong-Form Transcription\n-----------------------\n\n\nThe Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking\nalgorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers\n'pipeline'\nmethod. Chunking is enabled by setting 'chunk\\_length\\_s=30' when instantiating the pipeline. With chunking enabled, the pipeline\ncan be run with batched inference. It can also be extended to predict sequence level timestamps by passing 'return\\_timestamps=True':\n\n\nRefer to the blog post ASR Chunking for more details on the chunking algorithm.\n\n\nFine-Tuning\n-----------\n\n\nThe pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However,\nits predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog\npost Fine-Tune Whisper with Transformers provides a step-by-step\nguide to fine-tuning the Whisper model with as little as 5 hours of labelled data.", "### Evaluated Use\n\n\nThe primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research.\n\n\nThe models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them.\n\n\nIn particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes.\n\n\nTraining Data\n-------------\n\n\nThe models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages.\n\n\nAs discussed in the accompanying paper, we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language.\n\n\nPerformance and Limitations\n---------------------------\n\n\nOur studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level.\n\n\nHowever, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself.\n\n\nOur models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in the paper accompanying this release.\n\n\nIn addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in the paper. It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages.\n\n\nBroader Implications\n--------------------\n\n\nWe anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications.\n\n\nThere are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects.", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #en #zh #de #es #ru #ko #fr #ja #pt #tr #pl #ca #nl #ar #sv #it #id #hi #fi #vi #he #uk #el #ms #cs #ro #da #hu #ta #no #th #ur #hr #bg #lt #la #mi #ml #cy #sk #te #fa #lv #bn #sr #az #sl #kn #et #mk #br #eu #is #hy #ne #mn #bs #kk #sq #sw #gl #mr #pa #si #km #sn #yo #so #af #oc #ka #be #tg #sd #gu #am #yi #lo #uz #fo #ht #ps #tk #nn #mt #sa #lb #my #bo #tl #mg #as #tt #haw #ln #ha #ba #jw #su #arxiv-2212.04356 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### English to English\n\n\nIn this example, the context tokens are 'unforced', meaning the model automatically predicts the output language\n(English) and task (transcribe).\n\n\nThe context tokens can be removed from the start of the transcription by setting 'skip\\_special\\_tokens=True'.", "### French to French\n\n\nThe following example demonstrates French to French transcription by setting the decoder ids appropriately.\n\n\nTranslation\n-----------\n\n\nSetting the task to \"translate\" forces the Whisper model to perform speech translation.", "### French to English\n\n\nEvaluation\n----------\n\n\nThis code snippet shows how to evaluate Whisper Small on LibriSpeech test-clean:\n\n\nLong-Form Transcription\n-----------------------\n\n\nThe Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking\nalgorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers\n'pipeline'\nmethod. Chunking is enabled by setting 'chunk\\_length\\_s=30' when instantiating the pipeline. With chunking enabled, the pipeline\ncan be run with batched inference. It can also be extended to predict sequence level timestamps by passing 'return\\_timestamps=True':\n\n\nRefer to the blog post ASR Chunking for more details on the chunking algorithm.\n\n\nFine-Tuning\n-----------\n\n\nThe pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However,\nits predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog\npost Fine-Tune Whisper with Transformers provides a step-by-step\nguide to fine-tuning the Whisper model with as little as 5 hours of labelled data.", "### Evaluated Use\n\n\nThe primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research.\n\n\nThe models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them.\n\n\nIn particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes.\n\n\nTraining Data\n-------------\n\n\nThe models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages.\n\n\nAs discussed in the accompanying paper, we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language.\n\n\nPerformance and Limitations\n---------------------------\n\n\nOur studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level.\n\n\nHowever, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself.\n\n\nOur models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in the paper accompanying this release.\n\n\nIn addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in the paper. It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages.\n\n\nBroader Implications\n--------------------\n\n\nWe anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications.\n\n\nThere are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects.", "### BibTeX entry and citation info" ]	[ 289, 69, 49, 302, 1087, 11 ]	[ "passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #en #zh #de #es #ru #ko #fr #ja #pt #tr #pl #ca #nl #ar #sv #it #id #hi #fi #vi #he #uk #el #ms #cs #ro #da #hu #ta #no #th #ur #hr #bg #lt #la #mi #ml #cy #sk #te #fa #lv #bn #sr #az #sl #kn #et #mk #br #eu #is #hy #ne #mn #bs #kk #sq #sw #gl #mr #pa #si #km #sn #yo #so #af #oc #ka #be #tg #sd #gu #am #yi #lo #uz #fo #ht #ps #tk #nn #mt #sa #lb #my #bo #tl #mg #as #tt #haw #ln #ha #ba #jw #su #arxiv-2212.04356 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### English to English\n\n\nIn this example, the context tokens are 'unforced', meaning the model automatically predicts the output language\n(English) and task (transcribe).\n\n\nThe context tokens can be removed from the start of the transcription by setting 'skip\\_special\\_tokens=True'.### French to French\n\n\nThe following example demonstrates French to French transcription by setting the decoder ids appropriately.\n\n\nTranslation\n-----------\n\n\nSetting the task to \"translate\" forces the Whisper model to perform speech translation.", "passage: ### French to English\n\n\nEvaluation\n----------\n\n\nThis code snippet shows how to evaluate Whisper Small on LibriSpeech test-clean:\n\n\nLong-Form Transcription\n-----------------------\n\n\nThe Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking\nalgorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers\n'pipeline'\nmethod. Chunking is enabled by setting 'chunk\\_length\\_s=30' when instantiating the pipeline. With chunking enabled, the pipeline\ncan be run with batched inference. It can also be extended to predict sequence level timestamps by passing 'return\\_timestamps=True':\n\n\nRefer to the blog post ASR Chunking for more details on the chunking algorithm.\n\n\nFine-Tuning\n-----------\n\n\nThe pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However,\nits predictive capabilities can be improved further for certain languages and tasks through fine-tuning. 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null	null	transformers	# Uploaded model - Developed by: BarraHome - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "datasets": ["yahma/alpaca-cleaned"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "pipeline_tag": "text-generation"}	text-generation	BarraHome/Lucie-7b	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "dataset:yahma/alpaca-cleaned", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-12T16:06:04+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #conversational #en #dataset-yahma/alpaca-cleaned #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# Uploaded model - Developed by: BarraHome - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>	[ "# Uploaded model\n\n- Developed by: BarraHome\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #conversational #en #dataset-yahma/alpaca-cleaned #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: BarraHome\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]	[ 105, 84 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #text-generation-inference #unsloth #trl #conversational #en #dataset-yahma/alpaca-cleaned #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: BarraHome\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]	[ -0.0875626802444458, 0.03114994242787361, -0.004871149081736803, 0.07690940797328949, 0.059270285069942474, 0.02412272058427334, 0.12355786561965942, 0.0704214796423912, 0.06011175364255905, 0.00019747443730011582, 0.07619049400091171, 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null	null	transformers	# Whisper Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper). Disclaimer: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. ## Model details Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. The models were trained on either English-only data or multilingual data. The English-only models were trained on the task of speech recognition. The multilingual models were trained on both speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the same language as the audio. For speech translation, the model predicts transcriptions to a different language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The checkpoints are summarised in the following table with links to the models on the Hub: \| Size \| Parameters \| English-only \| Multilingual \| \|----------\|------------\|------------------------------------------------------\|-----------------------------------------------------\| \| tiny \| 39 M \| [✓](https://huggingface.co/openai/whisper-tiny.en) \| [✓](https://huggingface.co/openai/whisper-tiny) \| \| base \| 74 M \| [✓](https://huggingface.co/openai/whisper-base.en) \| [✓](https://huggingface.co/openai/whisper-base) \| \| small \| 244 M \| [✓](https://huggingface.co/openai/whisper-small.en) \| [✓](https://huggingface.co/openai/whisper-small) \| \| medium \| 769 M \| [✓](https://huggingface.co/openai/whisper-medium.en) \| [✓](https://huggingface.co/openai/whisper-medium) \| \| large \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large) \| \| large-v2 \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large-v2) \| # Usage To transcribe audio samples, the model has to be used alongside a [`WhisperProcessor`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor). The `WhisperProcessor` is used to: 1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) 2. Post-process the model outputs (converting them from tokens to text) The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order: 1. The transcription always starts with the `<\|startoftranscript\|>` token 2. The second token is the language token (e.g. `<\|en\|>` for English) 3. The third token is the "task token". It can take one of two values: `<\|transcribe\|>` for speech recognition or `<\|translate\|>` for speech translation 4. In addition, a `<\|notimestamps\|>` token is added if the model should not include timestamp prediction Thus, a typical sequence of context tokens might look as follows: ``` <\|startoftranscript\|> <\|en\|> <\|transcribe\|> <\|notimestamps\|> ``` Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps. These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, the Whisper model will automatically predict the output langauge and task itself. The context tokens can be set accordingly: ```python model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe") ``` Which forces the model to predict in English under the task of speech recognition. ## Transcription ### English to English In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language (English) and task (transcribe). ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") >>> model.config.forced_decoder_ids = None >>> # load dummy dataset and read audio files >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False) ['<\|startoftranscript\|><\|en\|><\|transcribe\|><\|notimestamps\|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<\|endoftext\|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.'] ``` The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`. ### French to French The following example demonstrates French to French transcription by setting the decoder ids appropriately. ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids) ['<\|startoftranscript\|><\|fr\|><\|transcribe\|><\|notimestamps\|> Un vrai travail intéressant va enfin être mené sur ce sujet.<\|endoftext\|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Un vrai travail intéressant va enfin être mené sur ce sujet.'] ``` ## Translation Setting the task to "translate" forces the Whisper model to perform speech translation. ### French to English ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="translate") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' A very interesting work, we will finally be given on this subject.'] ``` ## Evaluation This code snippet shows how to evaluate Whisper Tiny on [LibriSpeech test-clean](https://huggingface.co/datasets/librispeech_asr): ```python >>> from datasets import load_dataset >>> from transformers import WhisperForConditionalGeneration, WhisperProcessor >>> import torch >>> from evaluate import load >>> librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test") >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny").to("cuda") >>> def map_to_pred(batch): >>> audio = batch["audio"] >>> input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features >>> batch["reference"] = processor.tokenizer._normalize(batch['text']) >>> >>> with torch.no_grad(): >>> predicted_ids = model.generate(input_features.to("cuda"))[0] >>> transcription = processor.decode(predicted_ids) >>> batch["prediction"] = processor.tokenizer._normalize(transcription) >>> return batch >>> result = librispeech_test_clean.map(map_to_pred) >>> wer = load("wer") >>> print(100 * wer.compute(references=result["reference"], predictions=result["prediction"])) 7.547098647858638 ``` ## Long-Form Transcription The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline can be run with batched inference. It can also be extended to predict sequence level timestamps by passing `return_timestamps=True`: ```python >>> import torch >>> from transformers import pipeline >>> from datasets import load_dataset >>> device = "cuda:0" if torch.cuda.is_available() else "cpu" >>> pipe = pipeline( >>> "automatic-speech-recognition", >>> model="openai/whisper-tiny", >>> chunk_length_s=30, >>> device=device, >>> ) >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> prediction = pipe(sample.copy(), batch_size=8)["text"] " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel." >>> # we can also return timestamps for the predictions >>> prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"] [{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.', 'timestamp': (0.0, 5.44)}] ``` Refer to the blog post [ASR Chunking](https://huggingface.co/blog/asr-chunking) for more details on the chunking algorithm. ## Fine-Tuning The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step guide to fine-tuning the Whisper model with as little as 5 hours of labelled data. ### Evaluated Use The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. ## Training Data The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. As discussed in [the accompanying paper](https://cdn.openai.com/papers/whisper.pdf), we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language. ## Performance and Limitations Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf). In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. ## Broader Implications We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. ### BibTeX entry and citation info ```bibtex @misc{radford2022whisper, doi = {10.48550/ARXIV.2212.04356}, url = {https://arxiv.org/abs/2212.04356}, author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya}, title = {Robust Speech Recognition via Large-Scale Weak Supervision}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```	{"language": ["en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", false, "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "src": "https://cdn-media.huggingface.co/speech_samples/sample2.flac"}], "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "whisper-tiny", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "LibriSpeech (clean)", "type": "librispeech_asr", "config": "clean", "split": "test", "args": {"language": "en"}}, "metrics": [{"type": "wer", "value": 7.54, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "LibriSpeech (other)", "type": "librispeech_asr", "config": "other", "split": "test", "args": {"language": "en"}}, "metrics": [{"type": "wer", "value": 17.15, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "hi", "split": "test", "args": {"language": "hi"}}, "metrics": [{"type": "wer", "value": 141, "name": "Test WER"}]}]}]}	automatic-speech-recognition	rishabhjain16/whisper-tiny	[ "transformers", "pytorch", "tf", "jax", "safetensors", "whisper", "automatic-speech-recognition", "audio", "hf-asr-leaderboard", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "arxiv:2212.04356", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2024-02-12T16:06:47+00:00	[ "2212.04356" ]	[ "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su" ]	TAGS #transformers #pytorch #tf #jax #safetensors #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #en #zh #de #es #ru #ko #fr #ja #pt #tr #pl #ca #nl #ar #sv #it #id #hi #fi #vi #he #uk #el #ms #cs #ro #da #hu #ta #no #th #ur #hr #bg #lt #la #mi #ml #cy #sk #te #fa #lv #bn #sr #az #sl #kn #et #mk #br #eu #is #hy #ne #mn #bs #kk #sq #sw #gl #mr #pa #si #km #sn #yo #so #af #oc #ka #be #tg #sd #gu #am #yi #lo #uz #fo #ht #ps #tk #nn #mt #sa #lb #my #bo #tl #mg #as #tt #haw #ln #ha #ba #jw #su #arxiv-2212.04356 #license-apache-2.0 #model-index #endpoints_compatible #region-us	Whisper ======= Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al from OpenAI. The original code repository can be found here. Disclaimer: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. Model details ------------- Whisper is a Transformer based encoder-decoder model, also referred to as a sequence-to-sequence model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. The models were trained on either English-only data or multilingual data. The English-only models were trained on the task of speech recognition. The multilingual models were trained on both speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the same language as the audio. For speech translation, the model predicts transcriptions to a different language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the Hugging Face Hub. The checkpoints are summarised in the following table with links to the models on the Hub: Usage ===== To transcribe audio samples, the model has to be used alongside a 'WhisperProcessor'. The 'WhisperProcessor' is used to: 1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) 2. Post-process the model outputs (converting them from tokens to text) The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order: 1. The transcription always starts with the '<\|startoftranscript\|>' token 2. The second token is the language token (e.g. '<\|en\|>' for English) 3. The third token is the "task token". It can take one of two values: '<\|transcribe\|>' for speech recognition or '<\|translate\|>' for speech translation 4. In addition, a '<\|notimestamps\|>' token is added if the model should not include timestamp prediction Thus, a typical sequence of context tokens might look as follows: Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps. These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, the Whisper model will automatically predict the output langauge and task itself. The context tokens can be set accordingly: Which forces the model to predict in English under the task of speech recognition. Transcription ------------- ### English to English In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language (English) and task (transcribe). The context tokens can be removed from the start of the transcription by setting 'skip\_special\_tokens=True'. ### French to French The following example demonstrates French to French transcription by setting the decoder ids appropriately. Translation ----------- Setting the task to "translate" forces the Whisper model to perform speech translation. ### French to English Evaluation ---------- This code snippet shows how to evaluate Whisper Tiny on LibriSpeech test-clean: Long-Form Transcription ----------------------- The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers 'pipeline' method. Chunking is enabled by setting 'chunk\_length\_s=30' when instantiating the pipeline. With chunking enabled, the pipeline can be run with batched inference. It can also be extended to predict sequence level timestamps by passing 'return\_timestamps=True': Refer to the blog post ASR Chunking for more details on the chunking algorithm. Fine-Tuning ----------- The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog post Fine-Tune Whisper with Transformers provides a step-by-step guide to fine-tuning the Whisper model with as little as 5 hours of labelled data. ### Evaluated Use The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. Training Data ------------- The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. As discussed in the accompanying paper, we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language. Performance and Limitations --------------------------- Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in the paper accompanying this release. In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in the paper. It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. Broader Implications -------------------- We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. ### BibTeX entry and citation info	[ "### English to English\n\n\nIn this example, the context tokens are 'unforced', meaning the model automatically predicts the output language\n(English) and task (transcribe).\n\n\nThe context tokens can be removed from the start of the transcription by setting 'skip\\_special\\_tokens=True'.", "### French to French\n\n\nThe following example demonstrates French to French transcription by setting the decoder ids appropriately.\n\n\nTranslation\n-----------\n\n\nSetting the task to \"translate\" forces the Whisper model to perform speech translation.", "### French to English\n\n\nEvaluation\n----------\n\n\nThis code snippet shows how to evaluate Whisper Tiny on LibriSpeech test-clean:\n\n\nLong-Form Transcription\n-----------------------\n\n\nThe Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking\nalgorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers\n'pipeline'\nmethod. Chunking is enabled by setting 'chunk\\_length\\_s=30' when instantiating the pipeline. With chunking enabled, the pipeline\ncan be run with batched inference. It can also be extended to predict sequence level timestamps by passing 'return\\_timestamps=True':\n\n\nRefer to the blog post ASR Chunking for more details on the chunking algorithm.\n\n\nFine-Tuning\n-----------\n\n\nThe pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However,\nits predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog\npost Fine-Tune Whisper with Transformers provides a step-by-step\nguide to fine-tuning the Whisper model with as little as 5 hours of labelled data.", "### Evaluated Use\n\n\nThe primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research.\n\n\nThe models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them.\n\n\nIn particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes.\n\n\nTraining Data\n-------------\n\n\nThe models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages.\n\n\nAs discussed in the accompanying paper, we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language.\n\n\nPerformance and Limitations\n---------------------------\n\n\nOur studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level.\n\n\nHowever, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself.\n\n\nOur models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in the paper accompanying this release.\n\n\nIn addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in the paper. It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages.\n\n\nBroader Implications\n--------------------\n\n\nWe anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications.\n\n\nThere are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects.", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #en #zh #de #es #ru #ko #fr #ja #pt #tr #pl #ca #nl #ar #sv #it #id #hi #fi #vi #he #uk #el #ms #cs #ro #da #hu #ta #no #th #ur #hr #bg #lt #la #mi #ml #cy #sk #te #fa #lv #bn #sr #az #sl #kn #et #mk #br #eu #is #hy #ne #mn #bs #kk #sq #sw #gl #mr #pa #si #km #sn #yo #so #af #oc #ka #be #tg #sd #gu #am #yi #lo #uz #fo #ht #ps #tk #nn #mt #sa #lb #my #bo #tl #mg #as #tt #haw #ln #ha #ba #jw #su #arxiv-2212.04356 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### English to English\n\n\nIn this example, the context tokens are 'unforced', meaning the model automatically predicts the output language\n(English) and task (transcribe).\n\n\nThe context tokens can be removed from the start of the transcription by setting 'skip\\_special\\_tokens=True'.", "### French to French\n\n\nThe following example demonstrates French to French transcription by setting the decoder ids appropriately.\n\n\nTranslation\n-----------\n\n\nSetting the task to \"translate\" forces the Whisper model to perform speech translation.", "### French to English\n\n\nEvaluation\n----------\n\n\nThis code snippet shows how to evaluate Whisper Tiny on LibriSpeech test-clean:\n\n\nLong-Form Transcription\n-----------------------\n\n\nThe Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking\nalgorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers\n'pipeline'\nmethod. Chunking is enabled by setting 'chunk\\_length\\_s=30' when instantiating the pipeline. With chunking enabled, the pipeline\ncan be run with batched inference. It can also be extended to predict sequence level timestamps by passing 'return\\_timestamps=True':\n\n\nRefer to the blog post ASR Chunking for more details on the chunking algorithm.\n\n\nFine-Tuning\n-----------\n\n\nThe pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However,\nits predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog\npost Fine-Tune Whisper with Transformers provides a step-by-step\nguide to fine-tuning the Whisper model with as little as 5 hours of labelled data.", "### Evaluated Use\n\n\nThe primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research.\n\n\nThe models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them.\n\n\nIn particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes.\n\n\nTraining Data\n-------------\n\n\nThe models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages.\n\n\nAs discussed in the accompanying paper, we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language.\n\n\nPerformance and Limitations\n---------------------------\n\n\nOur studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level.\n\n\nHowever, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself.\n\n\nOur models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in the paper accompanying this release.\n\n\nIn addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in the paper. It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages.\n\n\nBroader Implications\n--------------------\n\n\nWe anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications.\n\n\nThere are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects.", "### BibTeX entry and citation info" ]	[ 289, 69, 49, 303, 1087, 11 ]	[ "passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #en #zh #de #es #ru #ko #fr #ja #pt #tr #pl #ca #nl #ar #sv #it #id #hi #fi #vi #he #uk #el #ms #cs #ro #da #hu #ta #no #th #ur #hr #bg #lt #la #mi #ml #cy #sk #te #fa #lv #bn #sr #az #sl #kn #et #mk #br #eu #is #hy #ne #mn #bs #kk #sq #sw #gl #mr #pa #si #km #sn #yo #so #af #oc #ka #be #tg #sd #gu #am #yi #lo #uz #fo #ht #ps #tk #nn #mt #sa #lb #my #bo #tl #mg #as #tt #haw #ln #ha #ba #jw #su #arxiv-2212.04356 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### English to English\n\n\nIn this example, the context tokens are 'unforced', meaning the model automatically predicts the output language\n(English) and task (transcribe).\n\n\nThe context tokens can be removed from the start of the transcription by setting 'skip\\_special\\_tokens=True'.### French to French\n\n\nThe following example demonstrates French to French transcription by setting the decoder ids appropriately.\n\n\nTranslation\n-----------\n\n\nSetting the task to \"translate\" forces the Whisper model to perform speech translation.", "passage: ### French to English\n\n\nEvaluation\n----------\n\n\nThis code snippet shows how to evaluate Whisper Tiny on LibriSpeech test-clean:\n\n\nLong-Form Transcription\n-----------------------\n\n\nThe Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking\nalgorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers\n'pipeline'\nmethod. Chunking is enabled by setting 'chunk\\_length\\_s=30' when instantiating the pipeline. With chunking enabled, the pipeline\ncan be run with batched inference. It can also be extended to predict sequence level timestamps by passing 'return\\_timestamps=True':\n\n\nRefer to the blog post ASR Chunking for more details on the chunking algorithm.\n\n\nFine-Tuning\n-----------\n\n\nThe pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However,\nits predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog\npost Fine-Tune Whisper with Transformers provides a step-by-step\nguide to fine-tuning the Whisper model with as little as 5 hours of labelled data." ]	[ -0.08027037233114243, 0.05652396380901337, -0.0040448447689414024, 0.029819060117006302, 0.018467973917722702, -0.03489754721522331, 0.0959380492568016, 0.04071760177612305, -0.028072815388441086, 0.07259296625852585, 0.06789256632328033, -0.013807404786348343, -0.002247292548418045, 0.11588896811008453, 0.07944761961698532, -0.3025968670845032, 0.06822321563959122, -0.09506497532129288, 0.0390934944152832, 0.0670681893825531, 0.0740831196308136, -0.032412152737379074, 0.02992483228445053, -0.014481586404144764, 0.07725343108177185, 0.03220391646027565, 0.019231634214520454, -0.059644851833581924, 0.07935847342014313, 0.07698657363653183, -0.02454228699207306, 0.03819994255900383, 0.03882046788930893, -0.21749675273895264, 0.014577940106391907, 0.008717622607946396, 0.02560705877840519, 0.010700867511332035, 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null	null	transformers	# Whisper Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper). Disclaimer: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. ## Model details Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. The models were trained on either English-only data or multilingual data. The English-only models were trained on the task of speech recognition. The multilingual models were trained on both speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the same language as the audio. For speech translation, the model predicts transcriptions to a different language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The checkpoints are summarised in the following table with links to the models on the Hub: \| Size \| Parameters \| English-only \| Multilingual \| \|----------\|------------\|------------------------------------------------------\|-----------------------------------------------------\| \| tiny \| 39 M \| [✓](https://huggingface.co/openai/whisper-tiny.en) \| [✓](https://huggingface.co/openai/whisper-tiny) \| \| base \| 74 M \| [✓](https://huggingface.co/openai/whisper-base.en) \| [✓](https://huggingface.co/openai/whisper-base) \| \| small \| 244 M \| [✓](https://huggingface.co/openai/whisper-small.en) \| [✓](https://huggingface.co/openai/whisper-small) \| \| medium \| 769 M \| [✓](https://huggingface.co/openai/whisper-medium.en) \| [✓](https://huggingface.co/openai/whisper-medium) \| \| large \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large) \| \| large-v2 \| 1550 M \| x \| [✓](https://huggingface.co/openai/whisper-large-v2) \| # Usage To transcribe audio samples, the model has to be used alongside a [`WhisperProcessor`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor). The `WhisperProcessor` is used to: 1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) 2. Post-process the model outputs (converting them from tokens to text) The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order: 1. The transcription always starts with the `<\|startoftranscript\|>` token 2. The second token is the language token (e.g. `<\|en\|>` for English) 3. The third token is the "task token". It can take one of two values: `<\|transcribe\|>` for speech recognition or `<\|translate\|>` for speech translation 4. In addition, a `<\|notimestamps\|>` token is added if the model should not include timestamp prediction Thus, a typical sequence of context tokens might look as follows: ``` <\|startoftranscript\|> <\|en\|> <\|transcribe\|> <\|notimestamps\|> ``` Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps. These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, the Whisper model will automatically predict the output langauge and task itself. The context tokens can be set accordingly: ```python model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe") ``` Which forces the model to predict in English under the task of speech recognition. ## Transcription ### English to English In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language (English) and task (transcribe). ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-medium") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium") >>> model.config.forced_decoder_ids = None >>> # load dummy dataset and read audio files >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False) ['<\|startoftranscript\|><\|en\|><\|transcribe\|><\|notimestamps\|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<\|endoftext\|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.'] ``` The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`. ### French to French The following example demonstrates French to French transcription by setting the decoder ids appropriately. ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-medium") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids) ['<\|startoftranscript\|><\|fr\|><\|transcribe\|><\|notimestamps\|> Un vrai travail intéressant va enfin être mené sur ce sujet.<\|endoftext\|>'] >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' Un vrai travail intéressant va enfin être mené sur ce sujet.'] ``` ## Translation Setting the task to "translate" forces the Whisper model to perform speech translation. ### French to English ```python >>> from transformers import WhisperProcessor, WhisperForConditionalGeneration >>> from datasets import Audio, load_dataset >>> # load model and processor >>> processor = WhisperProcessor.from_pretrained("openai/whisper-medium") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium") >>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="translate") >>> # load streaming dataset and read first audio sample >>> ds = load_dataset("common_voice", "fr", split="test", streaming=True) >>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) >>> input_speech = next(iter(ds))["audio"] >>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features >>> # generate token ids >>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids) >>> # decode token ids to text >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) [' A very interesting work, we will finally be given on this subject.'] ``` ## Evaluation This code snippet shows how to evaluate Whisper Medium on [LibriSpeech test-clean](https://huggingface.co/datasets/librispeech_asr): ```python >>> from datasets import load_dataset >>> from transformers import WhisperForConditionalGeneration, WhisperProcessor >>> import torch >>> from evaluate import load >>> librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test") >>> processor = WhisperProcessor.from_pretrained("openai/whisper-medium") >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-medium").to("cuda") >>> def map_to_pred(batch): >>> audio = batch["audio"] >>> input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features >>> batch["reference"] = processor.tokenizer._normalize(batch['text']) >>> >>> with torch.no_grad(): >>> predicted_ids = model.generate(input_features.to("cuda"))[0] >>> transcription = processor.decode(predicted_ids) >>> batch["prediction"] = processor.tokenizer._normalize(transcription) >>> return batch >>> result = librispeech_test_clean.map(map_to_pred) >>> wer = load("wer") >>> print(100 * wer.compute(references=result["reference"], predictions=result["prediction"])) 2.900409225488902 ``` ## Long-Form Transcription The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline can be run with batched inference. It can also be extended to predict sequence level timestamps by passing `return_timestamps=True`: ```python >>> import torch >>> from transformers import pipeline >>> from datasets import load_dataset >>> device = "cuda:0" if torch.cuda.is_available() else "cpu" >>> pipe = pipeline( >>> "automatic-speech-recognition", >>> model="openai/whisper-medium", >>> chunk_length_s=30, >>> device=device, >>> ) >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") >>> sample = ds[0]["audio"] >>> prediction = pipe(sample.copy(), batch_size=8)["text"] " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel." >>> # we can also return timestamps for the predictions >>> prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"] [{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.', 'timestamp': (0.0, 5.44)}] ``` Refer to the blog post [ASR Chunking](https://huggingface.co/blog/asr-chunking) for more details on the chunking algorithm. ## Fine-Tuning The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step guide to fine-tuning the Whisper model with as little as 5 hours of labelled data. ### Evaluated Use The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. ## Training Data The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. As discussed in [the accompanying paper](https://cdn.openai.com/papers/whisper.pdf), we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language. ## Performance and Limitations Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf). In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. ## Broader Implications We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. ### BibTeX entry and citation info ```bibtex @misc{radford2022whisper, doi = {10.48550/ARXIV.2212.04356}, url = {https://arxiv.org/abs/2212.04356}, author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya}, title = {Robust Speech Recognition via Large-Scale Weak Supervision}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ```	{"language": ["en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", false, "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech sample 2", "src": "https://cdn-media.huggingface.co/speech_samples/sample2.flac"}], "pipeline_tag": "automatic-speech-recognition", "model-index": [{"name": "whisper-medium", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "LibriSpeech (clean)", "type": "librispeech_asr", "config": "clean", "split": "test", "args": {"language": "en"}}, "metrics": [{"type": "wer", "value": 2.9, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "LibriSpeech (other)", "type": "librispeech_asr", "config": "other", "split": "test", "args": {"language": "en"}}, "metrics": [{"type": "wer", "value": 5.9, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "hi", "split": "test", "args": {"language": "hi"}}, "metrics": [{"type": "wer", "value": 53.87, "name": "Test WER"}]}]}]}	automatic-speech-recognition	rishabhjain16/whisper-medium	[ "transformers", "pytorch", "tf", "jax", "safetensors", "whisper", "automatic-speech-recognition", "audio", "hf-asr-leaderboard", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "arxiv:2212.04356", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2024-02-12T16:07:00+00:00	[ "2212.04356" ]	[ "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su" ]	TAGS #transformers #pytorch #tf #jax #safetensors #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #en #zh #de #es #ru #ko #fr #ja #pt #tr #pl #ca #nl #ar #sv #it #id #hi #fi #vi #he #uk #el #ms #cs #ro #da #hu #ta #no #th #ur #hr #bg #lt #la #mi #ml #cy #sk #te #fa #lv #bn #sr #az #sl #kn #et #mk #br #eu #is #hy #ne #mn #bs #kk #sq #sw #gl #mr #pa #si #km #sn #yo #so #af #oc #ka #be #tg #sd #gu #am #yi #lo #uz #fo #ht #ps #tk #nn #mt #sa #lb #my #bo #tl #mg #as #tt #haw #ln #ha #ba #jw #su #arxiv-2212.04356 #license-apache-2.0 #model-index #endpoints_compatible #region-us	Whisper ======= Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al from OpenAI. The original code repository can be found here. Disclaimer: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. Model details ------------- Whisper is a Transformer based encoder-decoder model, also referred to as a sequence-to-sequence model. It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. The models were trained on either English-only data or multilingual data. The English-only models were trained on the task of speech recognition. The multilingual models were trained on both speech recognition and speech translation. For speech recognition, the model predicts transcriptions in the same language as the audio. For speech translation, the model predicts transcriptions to a different language to the audio. Whisper checkpoints come in five configurations of varying model sizes. The smallest four are trained on either English-only or multilingual data. The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints are available on the Hugging Face Hub. The checkpoints are summarised in the following table with links to the models on the Hub: Usage ===== To transcribe audio samples, the model has to be used alongside a 'WhisperProcessor'. The 'WhisperProcessor' is used to: 1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model) 2. Post-process the model outputs (converting them from tokens to text) The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order: 1. The transcription always starts with the '<\|startoftranscript\|>' token 2. The second token is the language token (e.g. '<\|en\|>' for English) 3. The third token is the "task token". It can take one of two values: '<\|transcribe\|>' for speech recognition or '<\|translate\|>' for speech translation 4. In addition, a '<\|notimestamps\|>' token is added if the model should not include timestamp prediction Thus, a typical sequence of context tokens might look as follows: Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps. These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, the Whisper model will automatically predict the output langauge and task itself. The context tokens can be set accordingly: Which forces the model to predict in English under the task of speech recognition. Transcription ------------- ### English to English In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language (English) and task (transcribe). The context tokens can be removed from the start of the transcription by setting 'skip\_special\_tokens=True'. ### French to French The following example demonstrates French to French transcription by setting the decoder ids appropriately. Translation ----------- Setting the task to "translate" forces the Whisper model to perform speech translation. ### French to English Evaluation ---------- This code snippet shows how to evaluate Whisper Medium on LibriSpeech test-clean: Long-Form Transcription ----------------------- The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers 'pipeline' method. Chunking is enabled by setting 'chunk\_length\_s=30' when instantiating the pipeline. With chunking enabled, the pipeline can be run with batched inference. It can also be extended to predict sequence level timestamps by passing 'return\_timestamps=True': Refer to the blog post ASR Chunking for more details on the chunking algorithm. Fine-Tuning ----------- The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, its predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog post Fine-Tune Whisper with Transformers provides a step-by-step guide to fine-tuning the Whisper model with as little as 5 hours of labelled data. ### Evaluated Use The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. Training Data ------------- The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. As discussed in the accompanying paper, we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language. Performance and Limitations --------------------------- Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in the paper accompanying this release. In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in the paper. It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. Broader Implications -------------------- We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. ### BibTeX entry and citation info	[ "### English to English\n\n\nIn this example, the context tokens are 'unforced', meaning the model automatically predicts the output language\n(English) and task (transcribe).\n\n\nThe context tokens can be removed from the start of the transcription by setting 'skip\\_special\\_tokens=True'.", "### French to French\n\n\nThe following example demonstrates French to French transcription by setting the decoder ids appropriately.\n\n\nTranslation\n-----------\n\n\nSetting the task to \"translate\" forces the Whisper model to perform speech translation.", "### French to English\n\n\nEvaluation\n----------\n\n\nThis code snippet shows how to evaluate Whisper Medium on LibriSpeech test-clean:\n\n\nLong-Form Transcription\n-----------------------\n\n\nThe Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking\nalgorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers\n'pipeline'\nmethod. Chunking is enabled by setting 'chunk\\_length\\_s=30' when instantiating the pipeline. With chunking enabled, the pipeline\ncan be run with batched inference. It can also be extended to predict sequence level timestamps by passing 'return\\_timestamps=True':\n\n\nRefer to the blog post ASR Chunking for more details on the chunking algorithm.\n\n\nFine-Tuning\n-----------\n\n\nThe pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However,\nits predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog\npost Fine-Tune Whisper with Transformers provides a step-by-step\nguide to fine-tuning the Whisper model with as little as 5 hours of labelled data.", "### Evaluated Use\n\n\nThe primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research.\n\n\nThe models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them.\n\n\nIn particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes.\n\n\nTraining Data\n-------------\n\n\nThe models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages.\n\n\nAs discussed in the accompanying paper, we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language.\n\n\nPerformance and Limitations\n---------------------------\n\n\nOur studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level.\n\n\nHowever, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself.\n\n\nOur models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in the paper accompanying this release.\n\n\nIn addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in the paper. It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages.\n\n\nBroader Implications\n--------------------\n\n\nWe anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications.\n\n\nThere are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects.", "### BibTeX entry and citation info" ]	[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #en #zh #de #es #ru #ko #fr #ja #pt #tr #pl #ca #nl #ar #sv #it #id #hi #fi #vi #he #uk #el #ms #cs #ro #da #hu #ta #no #th #ur #hr #bg #lt #la #mi #ml #cy #sk #te #fa #lv #bn #sr #az #sl #kn #et #mk #br #eu #is #hy #ne #mn #bs #kk #sq #sw #gl #mr #pa #si #km #sn #yo #so #af #oc #ka #be #tg #sd #gu #am #yi #lo #uz #fo #ht #ps #tk #nn #mt #sa #lb #my #bo #tl #mg #as #tt #haw #ln #ha #ba #jw #su #arxiv-2212.04356 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### English to English\n\n\nIn this example, the context tokens are 'unforced', meaning the model automatically predicts the output language\n(English) and task (transcribe).\n\n\nThe context tokens can be removed from the start of the transcription by setting 'skip\\_special\\_tokens=True'.", "### French to French\n\n\nThe following example demonstrates French to French transcription by setting the decoder ids appropriately.\n\n\nTranslation\n-----------\n\n\nSetting the task to \"translate\" forces the Whisper model to perform speech translation.", "### French to English\n\n\nEvaluation\n----------\n\n\nThis code snippet shows how to evaluate Whisper Medium on LibriSpeech test-clean:\n\n\nLong-Form Transcription\n-----------------------\n\n\nThe Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking\nalgorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers\n'pipeline'\nmethod. Chunking is enabled by setting 'chunk\\_length\\_s=30' when instantiating the pipeline. With chunking enabled, the pipeline\ncan be run with batched inference. It can also be extended to predict sequence level timestamps by passing 'return\\_timestamps=True':\n\n\nRefer to the blog post ASR Chunking for more details on the chunking algorithm.\n\n\nFine-Tuning\n-----------\n\n\nThe pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However,\nits predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog\npost Fine-Tune Whisper with Transformers provides a step-by-step\nguide to fine-tuning the Whisper model with as little as 5 hours of labelled data.", "### Evaluated Use\n\n\nThe primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research.\n\n\nThe models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them.\n\n\nIn particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes.\n\n\nTraining Data\n-------------\n\n\nThe models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages.\n\n\nAs discussed in the accompanying paper, we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language.\n\n\nPerformance and Limitations\n---------------------------\n\n\nOur studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level.\n\n\nHowever, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself.\n\n\nOur models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in the paper accompanying this release.\n\n\nIn addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in the paper. It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages.\n\n\nBroader Implications\n--------------------\n\n\nWe anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications.\n\n\nThere are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects.", "### BibTeX entry and citation info" ]	[ 289, 69, 49, 302, 1087, 11 ]	[ "passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #en #zh #de #es #ru #ko #fr #ja #pt #tr #pl #ca #nl #ar #sv #it #id #hi #fi #vi #he #uk #el #ms #cs #ro #da #hu #ta #no #th #ur #hr #bg #lt #la #mi #ml #cy #sk #te #fa #lv #bn #sr #az #sl #kn #et #mk #br #eu #is #hy #ne #mn #bs #kk #sq #sw #gl #mr #pa #si #km #sn #yo #so #af #oc #ka #be #tg #sd #gu #am #yi #lo #uz #fo #ht #ps #tk #nn #mt #sa #lb #my #bo #tl #mg #as #tt #haw #ln #ha #ba #jw #su #arxiv-2212.04356 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### English to English\n\n\nIn this example, the context tokens are 'unforced', meaning the model automatically predicts the output language\n(English) and task (transcribe).\n\n\nThe context tokens can be removed from the start of the transcription by setting 'skip\\_special\\_tokens=True'.### French to French\n\n\nThe following example demonstrates French to French transcription by setting the decoder ids appropriately.\n\n\nTranslation\n-----------\n\n\nSetting the task to \"translate\" forces the Whisper model to perform speech translation.", "passage: ### French to English\n\n\nEvaluation\n----------\n\n\nThis code snippet shows how to evaluate Whisper Medium on LibriSpeech test-clean:\n\n\nLong-Form Transcription\n-----------------------\n\n\nThe Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking\nalgorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers\n'pipeline'\nmethod. Chunking is enabled by setting 'chunk\\_length\\_s=30' when instantiating the pipeline. With chunking enabled, the pipeline\ncan be run with batched inference. It can also be extended to predict sequence level timestamps by passing 'return\\_timestamps=True':\n\n\nRefer to the blog post ASR Chunking for more details on the chunking algorithm.\n\n\nFine-Tuning\n-----------\n\n\nThe pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However,\nits predictive capabilities can be improved further for certain languages and tasks through fine-tuning. The blog\npost Fine-Tune Whisper with Transformers provides a step-by-step\nguide to fine-tuning the Whisper model with as little as 5 hours of labelled data." ]	[ -0.08244045078754425, 0.05185613036155701, -0.0040265764109790325, 0.028276776894927025, 0.022008074447512627, -0.032525453716516495, 0.09311836957931519, 0.038098569959402084, -0.026309549808502197, 0.07443810254335403, 0.06922861933708191, -0.01174730435013771, -0.0014282651245594025, 0.11867998540401459, 0.07690242677927017, -0.3081640601158142, 0.066000796854496, -0.09365248680114746, 0.03706122934818268, 0.07005143910646439, 0.07289206981658936, -0.03404225409030914, 0.030839256942272186, -0.018143337219953537, 0.0762539803981781, 0.0325135737657547, 0.016918422654271126, -0.06006677821278572, 0.07958053797483444, 0.07837831228971481, -0.022172961384058, 0.03926726430654526, 0.03873050957918167, -0.21647530794143677, 0.015714040026068687, 0.009764972142875195, 0.02528519742190838, 0.011241721920669079, 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