Datasets:

---

ftopal

/

huggingface-models-processed

like 0

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

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[]

[ "### 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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 8.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 8.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 142, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 8.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 8.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 8.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 126, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 8.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 15.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 15.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 126, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 15.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 12.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 12.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 126, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 12.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 24.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 24.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 126, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 24.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 12.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 12.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 126, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 12.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 18.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 18.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 126, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 18.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 12.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 12.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 126, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 12.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 12.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 12.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 126, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 12.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 126, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #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: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 3.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 15.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 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: 15.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 103, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 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: 15.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 103, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 103, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 103, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\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: 30.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 32\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: 30.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 103, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 32\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: 30.0### Training results### Framework versions\n\n\n* Transformers 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 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 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ 37, 103, 5, 43 ]

[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #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: 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 4.13.0.dev0\n* Pytorch 1.10.0\n* Datasets 1.16.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "bert", "text-classification", "autonlp", "unk", "dataset:Jeska/autonlp-data-vaccinfaq", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "unk" ]

[ "# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 22144706\n- CO2 Emissions (in grams): 27.135492487925884", "## Validation Metrics\n\n- Loss: 1.81697416305542\n- Accuracy: 0.6377269139700079\n- Macro F1: 0.5181293370145044\n- Micro F1: 0.6377269139700079\n- Weighted F1: 0.631117826235572\n- Macro Precision: 0.5371452512845428\n- Micro Precision: 0.6377269139700079\n- Weighted Precision: 0.6655055695465463\n- Macro Recall: 0.5609328178925124\n- Micro Recall: 0.6377269139700079\n- Weighted Recall: 0.6377269139700079", "## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]

[ "TAGS\n#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-Jeska/autonlp-data-vaccinfaq #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 22144706\n- CO2 Emissions (in grams): 27.135492487925884", "## Validation Metrics\n\n- Loss: 1.81697416305542\n- Accuracy: 0.6377269139700079\n- Macro F1: 0.5181293370145044\n- Micro F1: 0.6377269139700079\n- Weighted F1: 0.631117826235572\n- Macro Precision: 0.5371452512845428\n- Micro Precision: 0.6377269139700079\n- Weighted Precision: 0.6655055695465463\n- Macro Recall: 0.5609328178925124\n- Micro Recall: 0.6377269139700079\n- Weighted Recall: 0.6377269139700079", "## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]

[ 61, 44, 180, 16 ]

[ "TAGS\n#transformers #pytorch #bert #text-classification #autonlp #unk #dataset-Jeska/autonlp-data-vaccinfaq #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 22144706\n- CO2 Emissions (in grams): 27.135492487925884## Validation Metrics\n\n- Loss: 1.81697416305542\n- Accuracy: 0.6377269139700079\n- Macro F1: 0.5181293370145044\n- Micro F1: 0.6377269139700079\n- Weighted F1: 0.631117826235572\n- Macro Precision: 0.5371452512845428\n- Micro Precision: 0.6377269139700079\n- Weighted Precision: 0.6655055695465463\n- Macro Recall: 0.5609328178925124\n- Micro Recall: 0.6377269139700079\n- Weighted Recall: 0.6377269139700079## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]

[ "arxiv:2012.13577", "region:us" ]

[ "2012.13577" ]

[]

[]

[ "TAGS\n#arxiv-2012.13577 #region-us \n" ]

[ 15 ]

[ "TAGS\n#arxiv-2012.13577 #region-us \n" ]

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:klue", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #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: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ 41, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #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: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# testing\n\nThis model is a fine-tuned version of distilbert-base-uncased on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6644\n- Accuracy: 0.6814\n- F1: 0.8105\n- Combined Score: 0.7459", "## 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: 1\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- training_steps: 10", "### Training results", "### Framework versions\n\n- Transformers 4.11.0.dev0\n- Pytorch 1.9.0+cu111\n- Datasets 1.11.0\n- Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "# testing\n\nThis model is a fine-tuned version of distilbert-base-uncased on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6644\n- Accuracy: 0.6814\n- F1: 0.8105\n- Combined Score: 0.7459", "## 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: 1\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- training_steps: 10", "### Training results", "### Framework versions\n\n- Transformers 4.11.0.dev0\n- Pytorch 1.9.0+cu111\n- Datasets 1.11.0\n- Tokenizers 0.10.3" ]

[ 58, 68, 7, 9, 9, 4, 91, 5, 47 ]

[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n# testing\n\nThis model is a fine-tuned version of distilbert-base-uncased on the GLUE MRPC dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6644\n- Accuracy: 0.6814\n- F1: 0.8105\n- Combined Score: 0.7459## 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: 1\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- training_steps: 10### Training results### Framework versions\n\n- Transformers 4.11.0.dev0\n- Pytorch 1.9.0+cu111\n- Datasets 1.11.0\n- Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Jimmy's character DialoGPT model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Jimmy's character DialoGPT model" ]

[ 39, 9 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Jimmy's character DialoGPT model" ]

[ "transformers", "pytorch", "electra", "pretraining", "korean", "ko", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "ko" ]

[ "# KrELECTRA-base-mecab\nKorean-based Pre-trained ELECTRA Language Model using Mecab (Morphological Analyzer)", "## Usage", "### Load model and tokenizer", "### Tokenizer example\n\n'''python\n>>> from transformers import AutoTokenizer\n>>> tokenizer = AutoTokenizer.from_pretrained(\"Jinhwan/krelectra-base-mecab\")\n>>> tokenizer.tokenize(\"[CLS] 한국어 ELECTRA를 공유합니다. [SEP]\")\n['[CLS]', '한국어', 'EL', '##ECT', '##RA', '##를', '공유', '##합', '##니다', '.', '[SEP]']\n>>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'EL', '##ECT', '##RA', '##를', '공유', '##합', '##니다', '.', '[SEP]'])\n[2, 7214, 24023, 24663, 26580, 3195, 7086, 3746, 5500, 17, 3]" ]

[ "TAGS\n#transformers #pytorch #electra #pretraining #korean #ko #license-apache-2.0 #endpoints_compatible #region-us \n", "# KrELECTRA-base-mecab\nKorean-based Pre-trained ELECTRA Language Model using Mecab (Morphological Analyzer)", "## Usage", "### Load model and tokenizer", "### Tokenizer example\n\n'''python\n>>> from transformers import AutoTokenizer\n>>> tokenizer = AutoTokenizer.from_pretrained(\"Jinhwan/krelectra-base-mecab\")\n>>> tokenizer.tokenize(\"[CLS] 한국어 ELECTRA를 공유합니다. [SEP]\")\n['[CLS]', '한국어', 'EL', '##ECT', '##RA', '##를', '공유', '##합', '##니다', '.', '[SEP]']\n>>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'EL', '##ECT', '##RA', '##를', '공유', '##합', '##니다', '.', '[SEP]'])\n[2, 7214, 24023, 24663, 26580, 3195, 7086, 3746, 5500, 17, 3]" ]

[ 36, 30, 3, 8, 291 ]

[ "TAGS\n#transformers #pytorch #electra #pretraining #korean #ko #license-apache-2.0 #endpoints_compatible #region-us \n# KrELECTRA-base-mecab\nKorean-based Pre-trained ELECTRA Language Model using Mecab (Morphological Analyzer)## Usage### Load model and tokenizer### Tokenizer example\n\n'''python\n>>> from transformers import AutoTokenizer\n>>> tokenizer = AutoTokenizer.from_pretrained(\"Jinhwan/krelectra-base-mecab\")\n>>> tokenizer.tokenize(\"[CLS] 한국어 ELECTRA를 공유합니다. [SEP]\")\n['[CLS]', '한국어', 'EL', '##ECT', '##RA', '##를', '공유', '##합', '##니다', '.', '[SEP]']\n>>> tokenizer.convert_tokens_to_ids(['[CLS]', '한국어', 'EL', '##ECT', '##RA', '##를', '공유', '##합', '##니다', '.', '[SEP]'])\n[2, 7214, 24023, 24663, 26580, 3195, 7086, 3746, 5500, 17, 3]" ]

[ "license:afl-3.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#license-afl-3.0 #region-us \n" ]

[ 13 ]

[ "TAGS\n#license-afl-3.0 #region-us \n" ]

[ "transformers", "pytorch", "safetensors", "xlm-roberta", "text-classification", "tensorflow", "zero-shot-classification", "it", "dataset:multi_nli", "dataset:glue", "arxiv:1911.02116", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[ "1911.02116" ]

[ "it" ]

[ "#### With the zero-shot classification pipeline\n\n\nThe model can be loaded with the 'zero-shot-classification' pipeline like so:\n\n\nYou can then classify in any of the above languages. You can even pass the labels in one language and the sequence to\nclassify in another:\n\n\nThe default hypothesis template is the English, 'This text is {}'. With this model better results are achieving when providing a translated template:", "#### With manual PyTorch\n\n\nTraining\n--------\n\n\nVersion 0.1\n-----------\n\n\nThe model has been now retrained on the full training set. Around 1000 sentences pairs have been removed from the set because their translation was botched by the translation model.\n\n\n\nVersion 0.0\n-----------\n\n\nThis model was pre-trained on set of 100 languages, as described in\nthe original paper. It was then fine-tuned on the task of NLI on an Italian translation of the MNLI dataset (85% of the train set only so far). The model used for translating the texts is Helsinki-NLP/opus-mt-en-it, with a max output sequence lenght of 120. The model has been trained for 1 epoch with learning rate 4e-6 and batch size 80, currently it scores 82 acc. on the remaining 15% of the training." ]

[ "TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #tensorflow #zero-shot-classification #it #dataset-multi_nli #dataset-glue #arxiv-1911.02116 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "#### With the zero-shot classification pipeline\n\n\nThe model can be loaded with the 'zero-shot-classification' pipeline like so:\n\n\nYou can then classify in any of the above languages. You can even pass the labels in one language and the sequence to\nclassify in another:\n\n\nThe default hypothesis template is the English, 'This text is {}'. With this model better results are achieving when providing a translated template:", "#### With manual PyTorch\n\n\nTraining\n--------\n\n\nVersion 0.1\n-----------\n\n\nThe model has been now retrained on the full training set. Around 1000 sentences pairs have been removed from the set because their translation was botched by the translation model.\n\n\n\nVersion 0.0\n-----------\n\n\nThis model was pre-trained on set of 100 languages, as described in\nthe original paper. It was then fine-tuned on the task of NLI on an Italian translation of the MNLI dataset (85% of the train set only so far). The model used for translating the texts is Helsinki-NLP/opus-mt-en-it, with a max output sequence lenght of 120. The model has been trained for 1 epoch with learning rate 4e-6 and batch size 80, currently it scores 82 acc. on the remaining 15% of the training." ]

[ 81, 86, 203 ]

[ "TAGS\n#transformers #pytorch #safetensors #xlm-roberta #text-classification #tensorflow #zero-shot-classification #it #dataset-multi_nli #dataset-glue #arxiv-1911.02116 #license-mit #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n#### With the zero-shot classification pipeline\n\n\nThe model can be loaded with the 'zero-shot-classification' pipeline like so:\n\n\nYou can then classify in any of the above languages. You can even pass the labels in one language and the sequence to\nclassify in another:\n\n\nThe default hypothesis template is the English, 'This text is {}'. With this model better results are achieving when providing a translated template:#### With manual PyTorch\n\n\nTraining\n--------\n\n\nVersion 0.1\n-----------\n\n\nThe model has been now retrained on the full training set. Around 1000 sentences pairs have been removed from the set because their translation was botched by the translation model.\n\n\n\nVersion 0.0\n-----------\n\n\nThis model was pre-trained on set of 100 languages, as described in\nthe original paper. It was then fine-tuned on the task of NLI on an Italian translation of the MNLI dataset (85% of the train set only so far). The model used for translating the texts is Helsinki-NLP/opus-mt-en-it, with a max output sequence lenght of 120. The model has been trained for 1 epoch with learning rate 4e-6 and batch size 80, currently it scores 82 acc. on the remaining 15% of the training." ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# My Awesome Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# My Awesome Model" ]

[ 39, 4 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# My Awesome Model" ]

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "spanish", "es", "dataset:catalonia_independence", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "es" ]

[ "### 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: 5", "### Training results", "### Model in action\n\n\nFast usage with pipelines:\n\n\n![Open In Colab](URL", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3\n\n\nThx to URL & @lewtun for Dataset ;)\n\n\n\n> \n> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.\n> \n> \n> \n\n\n\n> \n> Created by Jonatan Luna | LinkedIn\n> \n> \n>" ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #spanish #es #dataset-catalonia_independence #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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: 5", "### Training results", "### Model in action\n\n\nFast usage with pipelines:\n\n\n![Open In Colab](URL", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3\n\n\nThx to URL & @lewtun for Dataset ;)\n\n\n\n> \n> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.\n> \n> \n> \n\n\n\n> \n> Created by Jonatan Luna | LinkedIn\n> \n> \n>" ]

[ 58, 101, 5, 22, 99 ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #spanish #es #dataset-catalonia_independence #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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: 5### Training results### Model in action\n\n\nFast usage with pipelines:\n\n\n\n\n\n\n> \n> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.\n> \n> \n> \n\n\n\n> \n> Created by Jonatan Luna | LinkedIn\n> \n> \n>" ]

[ "transformers", "pytorch", "tensorboard", "safetensors", "roberta", "text-classification", "spanish", "es", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "es" ]

[ "### 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: 4", "### Training results", "### Model in action\n\n\nFast usage with pipelines:\n\n\n![Open In Colab](URL", "### Framework versions\n\n\n* Transformers 4.10.3\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3\n\n\n\n> \n> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.\n> \n> \n> \n\n\n\n> \n> Created by Jonatan Luna | LinkedIn\n> \n> \n>" ]

[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #spanish #es #autotrain_compatible #endpoints_compatible #has_space #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: 4", "### Training results", "### Model in action\n\n\nFast usage with pipelines:\n\n\n![Open In Colab](URL", "### Framework versions\n\n\n* Transformers 4.10.3\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3\n\n\n\n> \n> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.\n> \n> \n> \n\n\n\n> \n> Created by Jonatan Luna | LinkedIn\n> \n> \n>" ]

[ 43, 101, 5, 22, 84 ]

[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #spanish #es #autotrain_compatible #endpoints_compatible #has_space #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: 4### Training results### Model in action\n\n\nFast usage with pipelines:\n\n\n![Open In Colab](URL### Framework versions\n\n\n* Transformers 4.10.3\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3\n\n\n\n> \n> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.\n> \n> \n> \n\n\n\n> \n> Created by Jonatan Luna | LinkedIn\n> \n> \n>" ]

[ "transformers", "pytorch", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #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: 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ 49, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #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: 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: 5### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "roberta", "question-answering", "generated_from_trainer", "dataset:sqac", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-sqac #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: 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: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]

[ 46, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #dataset-sqac #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: 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: 3### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "catalan", "ca", "dataset:catalonia_independence", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "ca" ]

[ "### 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: 5", "### Training results", "### Model in action\n\n\nFast usage with pipelines:\n\n\n![Open In Colab](URL", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3\n\n\nThx to URL & @lewtun for Dataset ;)\n\n\n\n> \n> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.\n> \n> \n> \n\n\n\n> \n> Created by Jonatan Luna | LinkedIn\n> \n> \n>" ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #catalan #ca #dataset-catalonia_independence #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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: 5", "### Training results", "### Model in action\n\n\nFast usage with pipelines:\n\n\n![Open In Colab](URL", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3\n\n\nThx to URL & @lewtun for Dataset ;)\n\n\n\n> \n> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.\n> \n> \n> \n\n\n\n> \n> Created by Jonatan Luna | LinkedIn\n> \n> \n>" ]

[ 58, 101, 5, 22, 99 ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #catalan #ca #dataset-catalonia_independence #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #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: 5### Training results### Model in action\n\n\nFast usage with pipelines:\n\n\n\n\n\n\n> \n> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.\n> \n> \n> \n\n\n\n> \n> Created by Jonatan Luna | LinkedIn\n> \n> \n>" ]

[ "transformers", "pytorch", "roberta", "text-classification", "catalan", "ca", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "ca" ]

[ "# roberta-base-ca-finetuned-cyberbullying-catalan\n\nThis model is a fine-tuned version of BSC-TeMU/roberta-base-ca on the dataset generated scrapping all social networks (Twitter, Youtube ...) to detect cyberbullying on Catalan.\n\nIt achieves the following results on the evaluation set:\n- Loss: 0.1508\n- Accuracy: 0.9665", "## Training and evaluation data\n\nI use the concatenation from multiple datasets generated scrapping social networks (Twitter,Youtube,Discord...) to fine-tune this model. The total number of sentence pairs is above 410k sentences. Trained similar method at roberta-base-bne-finetuned-cyberbullying-spanish", "## Training procedure\n\n<details>", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\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: 4\n\n</details>", "### Model in action \n\nFast usage with pipelines:\n\n\n\n![Open In Colab](URL", "### Framework versions\n- Transformers 4.10.3\n- Pytorch 1.9.0+cu102\n- Datasets 1.12.1\n- Tokenizers 0.10.3\n\n> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.\n\n> Created by Jonatan Luna | LinkedIn" ]

[ "TAGS\n#transformers #pytorch #roberta #text-classification #catalan #ca #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# roberta-base-ca-finetuned-cyberbullying-catalan\n\nThis model is a fine-tuned version of BSC-TeMU/roberta-base-ca on the dataset generated scrapping all social networks (Twitter, Youtube ...) to detect cyberbullying on Catalan.\n\nIt achieves the following results on the evaluation set:\n- Loss: 0.1508\n- Accuracy: 0.9665", "## Training and evaluation data\n\nI use the concatenation from multiple datasets generated scrapping social networks (Twitter,Youtube,Discord...) to fine-tune this model. The total number of sentence pairs is above 410k sentences. Trained similar method at roberta-base-bne-finetuned-cyberbullying-spanish", "## Training procedure\n\n<details>", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\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: 4\n\n</details>", "### Model in action \n\nFast usage with pipelines:\n\n\n\n![Open In Colab](URL", "### Framework versions\n- Transformers 4.10.3\n- Pytorch 1.9.0+cu102\n- Datasets 1.12.1\n- Tokenizers 0.10.3\n\n> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.\n\n> Created by Jonatan Luna | LinkedIn" ]

[ 36, 88, 73, 7, 97, 22, 76 ]

[ "TAGS\n#transformers #pytorch #roberta #text-classification #catalan #ca #autotrain_compatible #endpoints_compatible #has_space #region-us \n# roberta-base-ca-finetuned-cyberbullying-catalan\n\nThis model is a fine-tuned version of BSC-TeMU/roberta-base-ca on the dataset generated scrapping all social networks (Twitter, Youtube ...) to detect cyberbullying on Catalan.\n\nIt achieves the following results on the evaluation set:\n- Loss: 0.1508\n- Accuracy: 0.9665## Training and evaluation data\n\nI use the concatenation from multiple datasets generated scrapping social networks (Twitter,Youtube,Discord...) to fine-tune this model. The total number of sentence pairs is above 410k sentences. Trained similar method at roberta-base-bne-finetuned-cyberbullying-spanish## Training procedure\n\n<details>### Training hyperparameters\n\nThe following hyperparameters were used during training:\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: 4\n\n</details>### Model in action \n\nFast usage with pipelines:\n\n\n\n![Open In Colab](URL### Framework versions\n- Transformers 4.10.3\n- Pytorch 1.9.0+cu102\n- Datasets 1.12.1\n- Tokenizers 0.10.3\n\n> Special thx to Manuel Romero/@mrm8488 as my mentor & R.C.\n\n> Created by Jonatan Luna | LinkedIn" ]

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #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: 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ 45, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #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: 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: 5### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:tecla", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-tecla #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: 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ 56, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-tecla #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: 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: 5### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ 5 ]

[ "TAGS\n#region-us \n" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Barney Calhoun DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Barney Calhoun DialoGPT Model" ]

[ 39, 7 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Barney Calhoun DialoGPT Model" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Family Guy DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Family Guy DialoGPT Model" ]

[ 39, 7 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Family Guy DialoGPT Model" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Johnny Test DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Johnny Test DialoGPT Model" ]

[ 39, 7 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Johnny Test DialoGPT Model" ]

[ "transformers", "pytorch", "mt5", "text2text-generation", "es", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "es" ]

[]

[ "TAGS\n#transformers #pytorch #mt5 #text2text-generation #es #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ 39 ]

[ "TAGS\n#transformers #pytorch #mt5 #text2text-generation #es #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ "transformers", "pytorch", "mt5", "text2text-generation", "es", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "es" ]

[]

[ "TAGS\n#transformers #pytorch #mt5 #text2text-generation #es #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ 39 ]

[ "TAGS\n#transformers #pytorch #mt5 #text2text-generation #es #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ "transformers", "pytorch", "t5", "text2text-generation", "es", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "es" ]

[]

[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #es #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ 39 ]

[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #es #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ "transformers", "pytorch", "albert", "token-classification", "generated_from_trainer", "en", "dataset:conll2003", "base_model:albert-base-v2", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases.", "#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n------------------", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #albert #token-classification #generated_from_trainer #en #dataset-conll2003 #base_model-albert-base-v2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases.", "#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n------------------", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ 67, 74, 76, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #albert #token-classification #generated_from_trainer #en #dataset-conll2003 #base_model-albert-base-v2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases.#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining procedure\n------------------### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "sagemaker", "bert-base-uncased", "text classification", "en", "dataset:adecorpusv2", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sagemaker #bert-base-uncased #text classification #en #dataset-adecorpusv2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 69 ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #text-classification #sagemaker #bert-base-uncased #text classification #en #dataset-adecorpusv2 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "generated_from_trainer", "en", "base_model:bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "#### Limitations and bias\n\n\nThis model is limited by its training dataset of survey results for a particular customer service domain. This may not generalize well for all use cases in different domains.", "#### How to use\n\n\nYou can use this model with Transformers *pipeline* for Text Classification.\n\n\nTraining and evaluation data\n----------------------------\n\n\nCustom survey dataset.\n\n\nTraining procedure\n------------------\n\n\nSageMaker notebook instance.", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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: 100\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #en #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "#### Limitations and bias\n\n\nThis model is limited by its training dataset of survey results for a particular customer service domain. This may not generalize well for all use cases in different domains.", "#### How to use\n\n\nYou can use this model with Transformers *pipeline* for Text Classification.\n\n\nTraining and evaluation data\n----------------------------\n\n\nCustom survey dataset.\n\n\nTraining procedure\n------------------\n\n\nSageMaker notebook instance.", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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: 100\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ 59, 40, 83, 128, 5, 44 ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #en #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n#### Limitations and bias\n\n\nThis model is limited by its training dataset of survey results for a particular customer service domain. This may not generalize well for all use cases in different domains.#### How to use\n\n\nYou can use this model with Transformers *pipeline* for Text Classification.\n\n\nTraining and evaluation data\n----------------------------\n\n\nCustom survey dataset.\n\n\nTraining procedure\n------------------\n\n\nSageMaker notebook instance.### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-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: 100\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "generated_from_trainer", "en", "dataset:conll2003", "base_model:bert-large-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases.", "#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.\n\n\nTraining procedure\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: 64\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 4.16.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #token-classification #generated_from_trainer #en #dataset-conll2003 #base_model-bert-large-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases.", "#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.\n\n\nTraining procedure\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: 64\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 4.16.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ 71, 74, 41, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #token-classification #generated_from_trainer #en #dataset-conll2003 #base_model-bert-large-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n#### Limitations and bias\n\n\nThis model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases.#### How to use\n\n\nYou can use this model with Transformers *pipeline* for NER.\n\n\nTraining procedure\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: 64\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 4.16.2\n* Pytorch 1.8.1+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0" ]

[ "transformers", "pytorch", "safetensors", "roberta", "text-classification", "sagemaker", "roberta-base", "text classification", "en", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #sagemaker #roberta-base #text classification #en #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 61 ]

[ "TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #sagemaker #roberta-base #text classification #en #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "asteroid", "pytorch", "audio", "ConvTasNet", "audio-to-audio", "dataset:Libri1Mix", "dataset:enh_single", "license:cc-by-sa-4.0", "has_space", "region:us" ]

[]

[]

[ "## Asteroid model 'JorisCos/ConvTasNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"ConvTasNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n", "## Asteroid model 'JorisCos/ConvTasNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"ConvTasNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ 58, 215 ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"ConvTasNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "asteroid", "pytorch", "audio", "ConvTasNet", "audio-to-audio", "dataset:Libri2Mix", "dataset:sep_clean", "license:cc-by-sa-4.0", "has_space", "region:us" ]

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[ "## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n\nResults :\n\nOn Libri2Mix min test set :\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri2Mix_sepclean_16k\" \nis a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0. \"ConvTasNet_Libri2Mix_sepclean_16k\" \nis licensed under Attribution-ShareAlike 3.0 Unported by Cosentino Joris." ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_clean #license-cc-by-sa-4.0 #has_space #region-us \n", "## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n\nResults :\n\nOn Libri2Mix min test set :\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri2Mix_sepclean_16k\" \nis a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0. \"ConvTasNet_Libri2Mix_sepclean_16k\" \nis licensed under Attribution-ShareAlike 3.0 Unported by Cosentino Joris." ]

[ 57, 181 ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_clean #license-cc-by-sa-4.0 #has_space #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n\nResults :\n\nOn Libri2Mix min test set :\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri2Mix_sepclean_16k\" \nis a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0. \"ConvTasNet_Libri2Mix_sepclean_16k\" \nis licensed under Attribution-ShareAlike 3.0 Unported by Cosentino Joris." ]

[ "asteroid", "pytorch", "audio", "ConvTasNet", "audio-to-audio", "dataset:Libri2Mix", "dataset:sep_clean", "license:cc-by-sa-4.0", "region:us" ]

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[ "## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_8k'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n\nResults :\n\nOn Libri2Mix min test set :\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri2Mix_sepclean_8k\" \nis a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0. \"ConvTasNet_Libri2Mix_sepclean_8k\" \nis licensed under Attribution-ShareAlike 3.0 Unported by Cosentino Joris." ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n", "## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_8k'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n\nResults :\n\nOn Libri2Mix min test set :\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri2Mix_sepclean_8k\" \nis a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0. \"ConvTasNet_Libri2Mix_sepclean_8k\" \nis licensed under Attribution-ShareAlike 3.0 Unported by Cosentino Joris." ]

[ 53, 186 ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepclean_8k'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n\nResults :\n\nOn Libri2Mix min test set :\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri2Mix_sepclean_8k\" \nis a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0. \"ConvTasNet_Libri2Mix_sepclean_8k\" \nis licensed under Attribution-ShareAlike 3.0 Unported by Cosentino Joris." ]

[ "asteroid", "pytorch", "audio", "ConvTasNet", "audio-to-audio", "dataset:Libri2Mix", "dataset:sep_noisy", "license:cc-by-sa-4.0", "has_space", "region:us" ]

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[ "## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\n\nOn Libri2Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri2Mix_sepnoisy_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused underCC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"ConvTasNet_Libri2Mix_sepnoisy_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #has_space #region-us \n", "## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\n\nOn Libri2Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri2Mix_sepnoisy_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused underCC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"ConvTasNet_Libri2Mix_sepnoisy_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ 57, 214 ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #has_space #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\n\nOn Libri2Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri2Mix_sepnoisy_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused underCC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"ConvTasNet_Libri2Mix_sepnoisy_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "asteroid", "pytorch", "audio", "ConvTasNet", "audio-to-audio", "dataset:Libri2Mix", "dataset:sep_noisy", "license:cc-by-sa-4.0", "region:us" ]

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[ "## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_8k'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri2Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri2Mix_sepnoisy_8k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"ConvTasNet_Libri2Mix_sepnoisy_8k\" is licensed under AAttribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us \n", "## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_8k'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri2Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri2Mix_sepnoisy_8k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"ConvTasNet_Libri2Mix_sepnoisy_8k\" is licensed under AAttribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ 53, 220 ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri2Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri2Mix_sepnoisy_8k'\nImported from Zenodo\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri2Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri2Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri2Mix_sepnoisy_8k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"ConvTasNet_Libri2Mix_sepnoisy_8k\" is licensed under AAttribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "asteroid", "pytorch", "audio", "ConvTasNet", "audio-to-audio", "dataset:Libri3Mix", "dataset:sep_clean", "license:cc-by-sa-4.0", "region:us" ]

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[ "## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri3Mix dataset.\n\nTraining config:\n\n\n\nResults :\n\nOn Libri3Mix min test set :\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri3Mix_sepclean_16k\" \nis a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0. \"ConvTasNet_Libri3Mix_sepclean_16k\" \nis licensed under Attribution-ShareAlike 3.0 Unported by Cosentino Joris." ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n", "## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri3Mix dataset.\n\nTraining config:\n\n\n\nResults :\n\nOn Libri3Mix min test set :\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri3Mix_sepclean_16k\" \nis a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0. \"ConvTasNet_Libri3Mix_sepclean_16k\" \nis licensed under Attribution-ShareAlike 3.0 Unported by Cosentino Joris." ]

[ 53, 181 ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri3Mix dataset.\n\nTraining config:\n\n\n\nResults :\n\nOn Libri3Mix min test set :\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri3Mix_sepclean_16k\" \nis a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0. \"ConvTasNet_Libri3Mix_sepclean_16k\" \nis licensed under Attribution-ShareAlike 3.0 Unported by Cosentino Joris." ]

[ "asteroid", "pytorch", "audio", "ConvTasNet", "audio-to-audio", "dataset:Libri3Mix", "dataset:sep_clean", "license:cc-by-sa-4.0", "region:us" ]

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[ "## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_8k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri3Mix dataset.\n\nTraining config:\n\n\nResults :\n\nOn Libri3Mix min test set :\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri3Mix_sepclean_8k\" \nis a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0. \"ConvTasNet_Libri3Mix_sepclean_8k\" \nis licensed under Attribution-ShareAlike 3.0 Unported by Cosentino Joris." ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n", "## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_8k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri3Mix dataset.\n\nTraining config:\n\n\nResults :\n\nOn Libri3Mix min test set :\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri3Mix_sepclean_8k\" \nis a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0. \"ConvTasNet_Libri3Mix_sepclean_8k\" \nis licensed under Attribution-ShareAlike 3.0 Unported by Cosentino Joris." ]

[ 53, 181 ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepclean_8k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid. \nIt was trained on the 'sep_clean' task of the Libri3Mix dataset.\n\nTraining config:\n\n\nResults :\n\nOn Libri3Mix min test set :\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri3Mix_sepclean_8k\" \nis a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0. \"ConvTasNet_Libri3Mix_sepclean_8k\" \nis licensed under Attribution-ShareAlike 3.0 Unported by Cosentino Joris." ]

[ "asteroid", "pytorch", "audio", "ConvTasNet", "audio-to-audio", "dataset:Libri3Mix", "dataset:sep_noisy", "license:cc-by-sa-4.0", "region:us" ]

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[ "## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri3Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri3Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri3Mix_sepnoisy_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0. \n\"ConvTasNet_Libri3Mix_sepnoisy_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us \n", "## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri3Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri3Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri3Mix_sepnoisy_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0. \n\"ConvTasNet_Libri3Mix_sepnoisy_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ 53, 210 ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri3Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri3Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri3Mix_sepnoisy_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0. \n\"ConvTasNet_Libri3Mix_sepnoisy_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "asteroid", "pytorch", "audio", "ConvTasNet", "audio-to-audio", "dataset:Libri3Mix", "dataset:sep_noisy", "license:cc-by-sa-4.0", "region:us" ]

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[ "## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_8k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri3Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri3Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri3Mix_sepnoisy_8k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"ConvTasNet_Libri3Mix_sepnoisy_8k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us \n", "## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_8k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri3Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri3Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri3Mix_sepnoisy_8k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"ConvTasNet_Libri3Mix_sepnoisy_8k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ 53, 214 ]

[ "TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri3Mix #dataset-sep_noisy #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/ConvTasNet_Libri3Mix_sepnoisy_8k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'sep_noisy' task of the Libri3Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri3Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"ConvTasNet_Libri3Mix_sepnoisy_8k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"ConvTasNet_Libri3Mix_sepnoisy_8k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "asteroid", "pytorch", "audio", "DCCRNet", "audio-to-audio", "speech-enhancement", "dataset:Libri1Mix", "dataset:enh_single", "license:cc-by-sa-4.0", "has_space", "region:us" ]

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[ "## Asteroid model 'JorisCos/DCCRNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"DCCRNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"DCCRNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "TAGS\n#asteroid #pytorch #audio #DCCRNet #audio-to-audio #speech-enhancement #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n", "## Asteroid model 'JorisCos/DCCRNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"DCCRNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"DCCRNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ 61, 212 ]

[ "TAGS\n#asteroid #pytorch #audio #DCCRNet #audio-to-audio #speech-enhancement #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n## Asteroid model 'JorisCos/DCCRNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"DCCRNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"DCCRNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "asteroid", "pytorch", "audio", "DCUNet", "audio-to-audio", "dataset:Libri1Mix", "dataset:enh_single", "license:cc-by-sa-4.0", "has_space", "region:us" ]

[]

[]

[ "## Asteroid model 'JorisCos/DCUNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"DCUNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"DCUNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "TAGS\n#asteroid #pytorch #audio #DCUNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n", "## Asteroid model 'JorisCos/DCUNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"DCUNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"DCUNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ 57, 212 ]

[ "TAGS\n#asteroid #pytorch #audio #DCUNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n## Asteroid model 'JorisCos/DCUNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"DCUNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"DCUNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "asteroid", "pytorch", "audio", "DPRNNTasNet", "audio-to-audio", "dataset:Libri1Mix", "dataset:enh_single", "license:cc-by-sa-4.0", "region:us" ]

[]

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[ "## Asteroid model 'JorisCos/DPRNNTasNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"DPRNNTasNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"DPRNNTasNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "TAGS\n#asteroid #pytorch #audio #DPRNNTasNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #region-us \n", "## Asteroid model 'JorisCos/DPRNNTasNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"DPRNNTasNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"DPRNNTasNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ 55, 218 ]

[ "TAGS\n#asteroid #pytorch #audio #DPRNNTasNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/DPRNNTasNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"DPRNNTasNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"DPRNNTasNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "asteroid", "pytorch", "audio", "DPTNet", "audio-to-audio", "dataset:Libri1Mix", "dataset:enh_single", "license:cc-by-sa-4.0", "has_space", "region:us" ]

[]

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[ "## Asteroid model 'JorisCos/DPTNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"DPTNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"DPTNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "TAGS\n#asteroid #pytorch #audio #DPTNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n", "## Asteroid model 'JorisCos/DPTNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"DPTNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"DPTNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ 57, 212 ]

[ "TAGS\n#asteroid #pytorch #audio #DPTNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n## Asteroid model 'JorisCos/DPTNet_Libri1Mix_enhsignle_16k'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn Libri1Mix min test set :\n\n\n\nLicense notice:\n\nThis work \"DPTNet_Libri1Mix_enhsignle_16k\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The WSJ0 Hipster Ambient Mixtures \ndataset by URL, used under CC BY-NC 4.0 (Research only). \n\"DPTNet_Libri1Mix_enhsignle_16k\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "asteroid", "pytorch", "audio", "VADNet", "VAD", "Voice Activity Detection", "dataset:LibriVAD", "license:cc-by-sa-4.0", "region:us" ]

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[ "## Asteroid model 'JorisCos/VAD_Net'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn LibriVAD min test set :\n\n\n\nLicense notice:\n\nThis work \"VAD_Net\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The DNS challenge noises, Attribution-ShareAlike 3.0 Unported.\n\"VAD_Net\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "TAGS\n#asteroid #pytorch #audio #VADNet #VAD #Voice Activity Detection #dataset-LibriVAD #license-cc-by-sa-4.0 #region-us \n", "## Asteroid model 'JorisCos/VAD_Net'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn LibriVAD min test set :\n\n\n\nLicense notice:\n\nThis work \"VAD_Net\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The DNS challenge noises, Attribution-ShareAlike 3.0 Unported.\n\"VAD_Net\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ 45, 162 ]

[ "TAGS\n#asteroid #pytorch #audio #VADNet #VAD #Voice Activity Detection #dataset-LibriVAD #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'JorisCos/VAD_Net'\n\nDescription:\n\nThis model was trained by Joris Cosentino using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn LibriVAD min test set :\n\n\n\nLicense notice:\n\nThis work \"VAD_Net\" is a derivative of LibriSpeech ASR corpus by Vassil Panayotov,\nused under CC BY 4.0; of The DNS challenge noises, Attribution-ShareAlike 3.0 Unported.\n\"VAD_Net\" is licensed under Attribution-ShareAlike 3.0 Unported by Joris Cosentino" ]

[ "transformers", "pytorch", "bart", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "# BART_Finetuned_CNN_dailymail\nThe following repo contains a bart-base model that was finetuned using the dataset cnn_dailymail" ]

[ "TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n", "# BART_Finetuned_CNN_dailymail\nThe following repo contains a bart-base model that was finetuned using the dataset cnn_dailymail" ]

[ 30, 34 ]

[ "TAGS\n#transformers #pytorch #bart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n# BART_Finetuned_CNN_dailymail\nThe following repo contains a bart-base model that was finetuned using the dataset cnn_dailymail" ]

[ "transformers", "pytorch", "tensorboard", "m2m_100", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

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[]

[ "# m2m100_418M-fr\n\nThis model is a fine-tuned version of facebook/m2m100_418M on the kde4 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: 32\n- eval_batch_size: 64\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", "### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.9.0+cpu\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #m2m_100 #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# m2m100_418M-fr\n\nThis model is a fine-tuned version of facebook/m2m100_418M on the kde4 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: 32\n- eval_batch_size: 64\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", "### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.9.0+cpu\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]

[ 55, 36, 7, 9, 9, 4, 93, 42 ]

[ "TAGS\n#transformers #pytorch #tensorboard #m2m_100 #text2text-generation #translation #generated_from_trainer #dataset-kde4 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# m2m100_418M-fr\n\nThis model is a fine-tuned version of facebook/m2m100_418M on the kde4 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: 32\n- eval_batch_size: 64\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### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.9.0+cpu\n- Datasets 1.16.1\n- Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Morty DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Morty DialoGPT Model" ]

[ 39, 7 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Morty DialoGPT Model" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Rick Sanchez DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Rick Sanchez DialoGPT Model" ]

[ 39, 7 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rick Sanchez DialoGPT Model" ]

[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "## Model description\nThis model was trained on the XED dataset and achieved \nvalidation loss: 0.5995 \nvalidation acc: 84.28% (ROC-AUC) \n\nLabels are based on Plutchik's model of emotions and may be combined:\n!image", "### Framework versions\n- Transformers 4.6.1\n- Pytorch 1.8.1+cu101\n- Datasets 1.8.0\n- Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n", "## Model description\nThis model was trained on the XED dataset and achieved \nvalidation loss: 0.5995 \nvalidation acc: 84.28% (ROC-AUC) \n\nLabels are based on Plutchik's model of emotions and may be combined:\n!image", "### Framework versions\n- Transformers 4.6.1\n- Pytorch 1.8.1+cu101\n- Datasets 1.8.0\n- Tokenizers 0.10.3" ]

[ 30, 57, 44 ]

[ "TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n## Model description\nThis model was trained on the XED dataset and achieved \nvalidation loss: 0.5995 \nvalidation acc: 84.28% (ROC-AUC) \n\nLabels are based on Plutchik's model of emotions and may be combined:\n!image### Framework versions\n- Transformers 4.6.1\n- Pytorch 1.8.1+cu101\n- Datasets 1.8.0\n- Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "distilbert", "text-classification", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\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 4.7.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.8.0\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #distilbert #text-classification #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: 32\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 4.7.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.8.0\n* Tokenizers 0.10.3" ]

[ 38, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #distilbert #text-classification #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: 32\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 4.7.0\n* Pytorch 1.9.0+cu102\n* Datasets 1.8.0\n* Tokenizers 0.10.3" ]

[ "transformers", "pytorch", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 30 ]

[ "TAGS\n#transformers #pytorch #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.2\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]

[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #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: 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.12.2\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]

[ 56, 101, 5, 44 ]

[ "TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #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: 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: 5### Training results### Framework versions\n\n\n* Transformers 4.12.2\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3" ]

[ "asteroid", "pytorch", "audio", "MultiDecoderDPRNN", "dataset:Wsj0MixVar", "dataset:sep_clean", "license:cc-by-sa-4.0", "region:us" ]

[]

[]

[ "## Asteroid model", "## Description:\n- Code: The code corresponding to this pretrained model can be found here.\n\n- Notebook: Colab Notebook with examples can be found here\n\n- Paper: \"Multi-Decoder DPRNN: High Accuracy Source Counting and Separation\", Junzhe Zhu, Raymond Yeh, Mark Hasegawa-Johnson. ICASSP(2021). \n\n- Summary: This model achieves SOTA on the problem of source separation with an unknown number of speakers. It uses multiple decoder heads(each tackling a distinct number of speakers), in addition to a classifier head that selects which decoder head to use.\n\n- Project Page\n\n- Original research repo\n\nThis model was trained by Joseph Zhu using the wsj0-mix-var/Multi-Decoder-DPRNN recipe in Asteroid. \nIt was trained on the 'sep_count' task of the Wsj0MixVar dataset.", "## Training config:", "## Results:", "### License notice:\nThis work \"MultiDecoderDPRNN\" is a derivative of CSR-I (WSJ0) Complete\nby LDC, used under LDC User Agreement for \nNon-Members (Research only). \n\"MultiDecoderDPRNN\" is licensed under Attribution-ShareAlike 3.0 Unported\nby Joseph Zhu." ]

[ "TAGS\n#asteroid #pytorch #audio #MultiDecoderDPRNN #dataset-Wsj0MixVar #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n", "## Asteroid model", "## Description:\n- Code: The code corresponding to this pretrained model can be found here.\n\n- Notebook: Colab Notebook with examples can be found here\n\n- Paper: \"Multi-Decoder DPRNN: High Accuracy Source Counting and Separation\", Junzhe Zhu, Raymond Yeh, Mark Hasegawa-Johnson. ICASSP(2021). \n\n- Summary: This model achieves SOTA on the problem of source separation with an unknown number of speakers. It uses multiple decoder heads(each tackling a distinct number of speakers), in addition to a classifier head that selects which decoder head to use.\n\n- Project Page\n\n- Original research repo\n\nThis model was trained by Joseph Zhu using the wsj0-mix-var/Multi-Decoder-DPRNN recipe in Asteroid. \nIt was trained on the 'sep_count' task of the Wsj0MixVar dataset.", "## Training config:", "## Results:", "### License notice:\nThis work \"MultiDecoderDPRNN\" is a derivative of CSR-I (WSJ0) Complete\nby LDC, used under LDC User Agreement for \nNon-Members (Research only). \n\"MultiDecoderDPRNN\" is licensed under Attribution-ShareAlike 3.0 Unported\nby Joseph Zhu." ]

[ 51, 4, 192, 7, 4, 78 ]

[ "TAGS\n#asteroid #pytorch #audio #MultiDecoderDPRNN #dataset-Wsj0MixVar #dataset-sep_clean #license-cc-by-sa-4.0 #region-us \n## Asteroid model## Description:\n- Code: The code corresponding to this pretrained model can be found here.\n\n- Notebook: Colab Notebook with examples can be found here\n\n- Paper: \"Multi-Decoder DPRNN: High Accuracy Source Counting and Separation\", Junzhe Zhu, Raymond Yeh, Mark Hasegawa-Johnson. ICASSP(2021). \n\n- Summary: This model achieves SOTA on the problem of source separation with an unknown number of speakers. It uses multiple decoder heads(each tackling a distinct number of speakers), in addition to a classifier head that selects which decoder head to use.\n\n- Project Page\n\n- Original research repo\n\nThis model was trained by Joseph Zhu using the wsj0-mix-var/Multi-Decoder-DPRNN recipe in Asteroid. \nIt was trained on the 'sep_count' task of the Wsj0MixVar dataset.## Training config:## Results:### License notice:\nThis work \"MultiDecoderDPRNN\" is a derivative of CSR-I (WSJ0) Complete\nby LDC, used under LDC User Agreement for \nNon-Members (Research only). \n\"MultiDecoderDPRNN\" is licensed under Attribution-ShareAlike 3.0 Unported\nby Joseph Zhu." ]

[ "transformers", "pytorch", "bert", "text-classification", "autonlp", "en", "dataset:Jush/autonlp-data-bp", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 29016523\n- CO2 Emissions (in grams): 3.273303707756322", "## Validation Metrics\n\n- Loss: 0.6093757748603821\n- Accuracy: 0.8333333333333334\n- Macro F1: 0.7937936978656889\n- Micro F1: 0.8333333333333334\n- Weighted F1: 0.8239843785760546\n- Macro Precision: 0.8988882462566673\n- Micro Precision: 0.8333333333333334\n- Weighted Precision: 0.8404982541824647\n- Macro Recall: 0.7805142534864643\n- Micro Recall: 0.8333333333333334\n- Weighted Recall: 0.8333333333333334", "## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]

[ "TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Jush/autonlp-data-bp #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 29016523\n- CO2 Emissions (in grams): 3.273303707756322", "## Validation Metrics\n\n- Loss: 0.6093757748603821\n- Accuracy: 0.8333333333333334\n- Macro F1: 0.7937936978656889\n- Micro F1: 0.8333333333333334\n- Weighted F1: 0.8239843785760546\n- Macro Precision: 0.8988882462566673\n- Micro Precision: 0.8333333333333334\n- Weighted Precision: 0.8404982541824647\n- Macro Recall: 0.7805142534864643\n- Micro Recall: 0.8333333333333334\n- Weighted Recall: 0.8333333333333334", "## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]

[ 56, 43, 168, 16 ]

[ "TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-Jush/autonlp-data-bp #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 29016523\n- CO2 Emissions (in grams): 3.273303707756322## Validation Metrics\n\n- Loss: 0.6093757748603821\n- Accuracy: 0.8333333333333334\n- Macro F1: 0.7937936978656889\n- Micro F1: 0.8333333333333334\n- Weighted F1: 0.8239843785760546\n- Macro Precision: 0.8988882462566673\n- Micro Precision: 0.8333333333333334\n- Weighted Precision: 0.8404982541824647\n- Macro Recall: 0.7805142534864643\n- Micro Recall: 0.8333333333333334\n- Weighted Recall: 0.8333333333333334## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:" ]

[ "transformers", "pytorch", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ 28 ]

[ "TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "mt5", "text2text-generation", "translation", "zh", "ja", "en", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]

[]

[ "zh", "ja", "en" ]

[]

[ "TAGS\n#transformers #pytorch #mt5 #text2text-generation #translation #zh #ja #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n" ]

[ 64 ]

[ "TAGS\n#transformers #pytorch #mt5 #text2text-generation #translation #zh #ja #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n" ]

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ 5 ]

[ "TAGS\n#region-us \n" ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ 39 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ "transformers", "pytorch", "albert", "sv", "endpoints_compatible", "region:us" ]

[]

[ "sv" ]

[ "### BERT Base Swedish\n\n\nA standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:", "### BERT base fine-tuned for Swedish NER\n\n\nThis model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline the model can be easily instantiated. For Transformer<2.4.1 it seems the tokenizer must be loaded separately to disable lower-casing of input strings:\n\n\nRunning the Python code above should produce in something like the result below. Entity types used are 'TME' for time, 'PRS' for personal names, 'LOC' for locations, 'EVN' for events and 'ORG' for organisations. These labels are subject to change.\n\n\nThe BERT tokenizer often splits words into multiple tokens, with the subparts starting with '##', for example the string 'Engelbert kör Volvo till Herrängens fotbollsklubb' gets tokenized as 'Engel ##bert kör Volvo till Herr ##ängens fotbolls ##klubb'. To glue parts back together one can use something like this:\n\n\nWhich should result in the following (though less cleanly formatted):", "### ALBERT base\n\n\nThe easiest way to do this is, again, using Huggingface Transformers:\n\n\nAcknowledgements ️\n------------------\n\n\n* Resources from Stockholms University, Umeå University and Swedish Language Bank at Gothenburg University were used when fine-tuning BERT for NER.\n* Model pretraining was made partly in-house at the KBLab and partly (for material without active copyright) with the support of Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).\n* Models are hosted on S3 by Huggingface" ]

[ "TAGS\n#transformers #pytorch #albert #sv #endpoints_compatible #region-us \n", "### BERT Base Swedish\n\n\nA standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:", "### BERT base fine-tuned for Swedish NER\n\n\nThis model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline the model can be easily instantiated. For Transformer<2.4.1 it seems the tokenizer must be loaded separately to disable lower-casing of input strings:\n\n\nRunning the Python code above should produce in something like the result below. Entity types used are 'TME' for time, 'PRS' for personal names, 'LOC' for locations, 'EVN' for events and 'ORG' for organisations. These labels are subject to change.\n\n\nThe BERT tokenizer often splits words into multiple tokens, with the subparts starting with '##', for example the string 'Engelbert kör Volvo till Herrängens fotbollsklubb' gets tokenized as 'Engel ##bert kör Volvo till Herr ##ängens fotbolls ##klubb'. To glue parts back together one can use something like this:\n\n\nWhich should result in the following (though less cleanly formatted):", "### ALBERT base\n\n\nThe easiest way to do this is, again, using Huggingface Transformers:\n\n\nAcknowledgements ️\n------------------\n\n\n* Resources from Stockholms University, Umeå University and Swedish Language Bank at Gothenburg University were used when fine-tuning BERT for NER.\n* Model pretraining was made partly in-house at the KBLab and partly (for material without active copyright) with the support of Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).\n* Models are hosted on S3 by Huggingface" ]

[ 21, 40, 233, 123 ]

[ "TAGS\n#transformers #pytorch #albert #sv #endpoints_compatible #region-us \n### BERT Base Swedish\n\n\nA standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:### BERT base fine-tuned for Swedish NER\n\n\nThis model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline the model can be easily instantiated. For Transformer<2.4.1 it seems the tokenizer must be loaded separately to disable lower-casing of input strings:\n\n\nRunning the Python code above should produce in something like the result below. Entity types used are 'TME' for time, 'PRS' for personal names, 'LOC' for locations, 'EVN' for events and 'ORG' for organisations. These labels are subject to change.\n\n\nThe BERT tokenizer often splits words into multiple tokens, with the subparts starting with '##', for example the string 'Engelbert kör Volvo till Herrängens fotbollsklubb' gets tokenized as 'Engel ##bert kör Volvo till Herr ##ängens fotbolls ##klubb'. To glue parts back together one can use something like this:\n\n\nWhich should result in the following (though less cleanly formatted):### ALBERT base\n\n\nThe easiest way to do this is, again, using Huggingface Transformers:\n\n\nAcknowledgements ️\n------------------\n\n\n* Resources from Stockholms University, Umeå University and Swedish Language Bank at Gothenburg University were used when fine-tuning BERT for NER.\n* Model pretraining was made partly in-house at the KBLab and partly (for material without active copyright) with the support of Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).\n* Models are hosted on S3 by Huggingface" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "token-classification", "sv", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[]

[ "sv" ]

[ "### BERT Base Swedish\n\n\nA standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:", "### BERT base fine-tuned for Swedish NER\n\n\nThis model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline the model can be easily instantiated. For Transformer<2.4.1 it seems the tokenizer must be loaded separately to disable lower-casing of input strings:\n\n\nRunning the Python code above should produce in something like the result below. Entity types used are 'TME' for time, 'PRS' for personal names, 'LOC' for locations, 'EVN' for events and 'ORG' for organisations. These labels are subject to change.\n\n\nThe BERT tokenizer often splits words into multiple tokens, with the subparts starting with '##', for example the string 'Engelbert kör Volvo till Herrängens fotbollsklubb' gets tokenized as 'Engel ##bert kör Volvo till Herr ##ängens fotbolls ##klubb'. To glue parts back together one can use something like this:\n\n\nWhich should result in the following (though less cleanly formatted):", "### ALBERT base\n\n\nThe easiest way to do this is, again, using Huggingface Transformers:\n\n\nAcknowledgements ️\n------------------\n\n\n* Resources from Stockholms University, Umeå University and Swedish Language Bank at Gothenburg University were used when fine-tuning BERT for NER.\n* Model pretraining was made partly in-house at the KBLab and partly (for material without active copyright) with the support of Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).\n* Models are hosted on S3 by Huggingface" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #token-classification #sv #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### BERT Base Swedish\n\n\nA standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:", "### BERT base fine-tuned for Swedish NER\n\n\nThis model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline the model can be easily instantiated. For Transformer<2.4.1 it seems the tokenizer must be loaded separately to disable lower-casing of input strings:\n\n\nRunning the Python code above should produce in something like the result below. Entity types used are 'TME' for time, 'PRS' for personal names, 'LOC' for locations, 'EVN' for events and 'ORG' for organisations. These labels are subject to change.\n\n\nThe BERT tokenizer often splits words into multiple tokens, with the subparts starting with '##', for example the string 'Engelbert kör Volvo till Herrängens fotbollsklubb' gets tokenized as 'Engel ##bert kör Volvo till Herr ##ängens fotbolls ##klubb'. To glue parts back together one can use something like this:\n\n\nWhich should result in the following (though less cleanly formatted):", "### ALBERT base\n\n\nThe easiest way to do this is, again, using Huggingface Transformers:\n\n\nAcknowledgements ️\n------------------\n\n\n* Resources from Stockholms University, Umeå University and Swedish Language Bank at Gothenburg University were used when fine-tuning BERT for NER.\n* Model pretraining was made partly in-house at the KBLab and partly (for material without active copyright) with the support of Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).\n* Models are hosted on S3 by Huggingface" ]

[ 43, 40, 233, 123 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #token-classification #sv #autotrain_compatible #endpoints_compatible #has_space #region-us \n### BERT Base Swedish\n\n\nA standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:### BERT base fine-tuned for Swedish NER\n\n\nThis model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline the model can be easily instantiated. For Transformer<2.4.1 it seems the tokenizer must be loaded separately to disable lower-casing of input strings:\n\n\nRunning the Python code above should produce in something like the result below. Entity types used are 'TME' for time, 'PRS' for personal names, 'LOC' for locations, 'EVN' for events and 'ORG' for organisations. These labels are subject to change.\n\n\nThe BERT tokenizer often splits words into multiple tokens, with the subparts starting with '##', for example the string 'Engelbert kör Volvo till Herrängens fotbollsklubb' gets tokenized as 'Engel ##bert kör Volvo till Herr ##ängens fotbolls ##klubb'. To glue parts back together one can use something like this:\n\n\nWhich should result in the following (though less cleanly formatted):### ALBERT base\n\n\nThe easiest way to do this is, again, using Huggingface Transformers:\n\n\nAcknowledgements ️\n------------------\n\n\n* Resources from Stockholms University, Umeå University and Swedish Language Bank at Gothenburg University were used when fine-tuning BERT for NER.\n* Model pretraining was made partly in-house at the KBLab and partly (for material without active copyright) with the support of Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).\n* Models are hosted on S3 by Huggingface" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "bert", "fill-mask", "sv", "arxiv:2007.01658", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "2007.01658" ]

[ "sv" ]

[ "### BERT Base Swedish\n\n\nA standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:", "### BERT base fine-tuned for Swedish NER\n\n\nThis model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline the model can be easily instantiated. For Transformer<2.4.1 it seems the tokenizer must be loaded separately to disable lower-casing of input strings:\n\n\nRunning the Python code above should produce in something like the result below. Entity types used are 'TME' for time, 'PRS' for personal names, 'LOC' for locations, 'EVN' for events and 'ORG' for organisations. These labels are subject to change.\n\n\nThe BERT tokenizer often splits words into multiple tokens, with the subparts starting with '##', for example the string 'Engelbert kör Volvo till Herrängens fotbollsklubb' gets tokenized as 'Engel ##bert kör Volvo till Herr ##ängens fotbolls ##klubb'. To glue parts back together one can use something like this:\n\n\nWhich should result in the following (though less cleanly formated):", "### ALBERT base\n\n\nThe easisest way to do this is, again, using Huggingface Transformers:\n\n\nAcknowledgements ️\n------------------\n\n\n* Resources from Stockholms University, Umeå University and Swedish Language Bank at Gothenburg University were used when fine-tuning BERT for NER.\n* Model pretraining was made partly in-house at the KBLab and partly (for material without active copyright) with the support of Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).\n* Models are hosted on S3 by Huggingface\n\n\nURL" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #sv #arxiv-2007.01658 #autotrain_compatible #endpoints_compatible #region-us \n", "### BERT Base Swedish\n\n\nA standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:", "### BERT base fine-tuned for Swedish NER\n\n\nThis model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline the model can be easily instantiated. For Transformer<2.4.1 it seems the tokenizer must be loaded separately to disable lower-casing of input strings:\n\n\nRunning the Python code above should produce in something like the result below. Entity types used are 'TME' for time, 'PRS' for personal names, 'LOC' for locations, 'EVN' for events and 'ORG' for organisations. These labels are subject to change.\n\n\nThe BERT tokenizer often splits words into multiple tokens, with the subparts starting with '##', for example the string 'Engelbert kör Volvo till Herrängens fotbollsklubb' gets tokenized as 'Engel ##bert kör Volvo till Herr ##ängens fotbolls ##klubb'. To glue parts back together one can use something like this:\n\n\nWhich should result in the following (though less cleanly formated):", "### ALBERT base\n\n\nThe easisest way to do this is, again, using Huggingface Transformers:\n\n\nAcknowledgements ️\n------------------\n\n\n* Resources from Stockholms University, Umeå University and Swedish Language Bank at Gothenburg University were used when fine-tuning BERT for NER.\n* Model pretraining was made partly in-house at the KBLab and partly (for material without active copyright) with the support of Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).\n* Models are hosted on S3 by Huggingface\n\n\nURL" ]

[ 49, 40, 233, 127 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #sv #arxiv-2007.01658 #autotrain_compatible #endpoints_compatible #region-us \n### BERT Base Swedish\n\n\nA standard BERT base for Swedish trained on a variety of sources. Vocabulary size is ~50k. Using Huggingface Transformers the model can be loaded in Python as follows:### BERT base fine-tuned for Swedish NER\n\n\nThis model is fine-tuned on the SUC 3.0 dataset. Using the Huggingface pipeline the model can be easily instantiated. For Transformer<2.4.1 it seems the tokenizer must be loaded separately to disable lower-casing of input strings:\n\n\nRunning the Python code above should produce in something like the result below. Entity types used are 'TME' for time, 'PRS' for personal names, 'LOC' for locations, 'EVN' for events and 'ORG' for organisations. These labels are subject to change.\n\n\nThe BERT tokenizer often splits words into multiple tokens, with the subparts starting with '##', for example the string 'Engelbert kör Volvo till Herrängens fotbollsklubb' gets tokenized as 'Engel ##bert kör Volvo till Herr ##ängens fotbolls ##klubb'. To glue parts back together one can use something like this:\n\n\nWhich should result in the following (though less cleanly formated):### ALBERT base\n\n\nThe easisest way to do this is, again, using Huggingface Transformers:\n\n\nAcknowledgements ️\n------------------\n\n\n* Resources from Stockholms University, Umeå University and Swedish Language Bank at Gothenburg University were used when fine-tuning BERT for NER.\n* Model pretraining was made partly in-house at the KBLab and partly (for material without active copyright) with the support of Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).\n* Models are hosted on S3 by Huggingface\n\n\nURL" ]

[ "transformers", "pytorch", "speech-encoder-decoder", "automatic-speech-recognition", "generated_from_trainer", "asr_seq2seq", "endpoints_compatible", "region:us" ]

[]

[]

[]

[ "TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #asr_seq2seq #endpoints_compatible #region-us \n" ]

[ 47 ]

[ "TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #asr_seq2seq #endpoints_compatible #region-us \n" ]

[ "transformers", "pytorch", "safetensors", "bart", "text2text-generation", "sv", "arxiv:1910.13461", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]

[ "1910.13461" ]

[ "sv" ]

[ "## KB-BART\n\nA BART model trained on a Swedish corpus consisting of 15 billion tokens (about 80GB of text). The model was trained with Fairseq, and converted to be compatible with Huggingface. \n\nTraining code can be found here.", "## Usage", "## Acknowledgements\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ "TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #sv #arxiv-1910.13461 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "## KB-BART\n\nA BART model trained on a Swedish corpus consisting of 15 billion tokens (about 80GB of text). The model was trained with Fairseq, and converted to be compatible with Huggingface. \n\nTraining code can be found here.", "## Usage", "## Acknowledgements\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ 50, 52, 3, 51 ]

[ "TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #sv #arxiv-1910.13461 #autotrain_compatible #endpoints_compatible #has_space #region-us \n## KB-BART\n\nA BART model trained on a Swedish corpus consisting of 15 billion tokens (about 80GB of text). The model was trained with Fairseq, and converted to be compatible with Huggingface. \n\nTraining code can be found here.## Usage## Acknowledgements\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ "transformers", "pytorch", "safetensors", "bert", "fill-mask", "sv", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "sv" ]

[ "# BERT Swedish\n\nThis BERT model was trained using the transformers library.\nThe size of the model is a regular BERT-base with 110M parameters.\nThe model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.\nTo avoid excessive padding documents shorter than 512 tokens were concatenated into one large sequence of 512 tokens, and larger documents were split into multiple 512 token sequences, following URL\n\nTraining was done for a bit more than 8 epochs with a batch size of 2048, resulting in a little less than 125k training steps.\n\nThe model has three sister models trained on the same dataset:\n- Megatron-BERT-base-125k\n- Megatron-BERT-base-600k\n- Megatron-BERT-large-110k", "## Acknowledgements\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #sv #autotrain_compatible #endpoints_compatible #region-us \n", "# BERT Swedish\n\nThis BERT model was trained using the transformers library.\nThe size of the model is a regular BERT-base with 110M parameters.\nThe model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.\nTo avoid excessive padding documents shorter than 512 tokens were concatenated into one large sequence of 512 tokens, and larger documents were split into multiple 512 token sequences, following URL\n\nTraining was done for a bit more than 8 epochs with a batch size of 2048, resulting in a little less than 125k training steps.\n\nThe model has three sister models trained on the same dataset:\n- Megatron-BERT-base-125k\n- Megatron-BERT-base-600k\n- Megatron-BERT-large-110k", "## Acknowledgements\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ 34, 167, 52 ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #sv #autotrain_compatible #endpoints_compatible #region-us \n# BERT Swedish\n\nThis BERT model was trained using the transformers library.\nThe size of the model is a regular BERT-base with 110M parameters.\nThe model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.\nTo avoid excessive padding documents shorter than 512 tokens were concatenated into one large sequence of 512 tokens, and larger documents were split into multiple 512 token sequences, following URL\n\nTraining was done for a bit more than 8 epochs with a batch size of 2048, resulting in a little less than 125k training steps.\n\nThe model has three sister models trained on the same dataset:\n- Megatron-BERT-base-125k\n- Megatron-BERT-base-600k\n- Megatron-BERT-large-110k## Acknowledgements\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ "transformers", "pytorch", "megatron-bert", "token-classification", "sequence-tagger-model", "bert", "sv", "dataset:KBLab/sucx3_ner", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "sv" ]

[ "# KB-BERT for NER", "## Cased data\n\nThis model is based on KB-BERT and was fine-tuned on the SUCX 3.0 - NER corpus, using the _simple_ tags and cased data.\nFor this model we used a variation of the data that did not use BIO-encoding to differentiate between the beginnings (B), and insides (I) of named entity tags.\n\nThe model was trained on the training data only, with the best model chosen by its performance on the validation data.\nYou find more information about the model and the performance on our blog: URL" ]

[ "TAGS\n#transformers #pytorch #megatron-bert #token-classification #sequence-tagger-model #bert #sv #dataset-KBLab/sucx3_ner #autotrain_compatible #endpoints_compatible #region-us \n", "# KB-BERT for NER", "## Cased data\n\nThis model is based on KB-BERT and was fine-tuned on the SUCX 3.0 - NER corpus, using the _simple_ tags and cased data.\nFor this model we used a variation of the data that did not use BIO-encoding to differentiate between the beginnings (B), and insides (I) of named entity tags.\n\nThe model was trained on the training data only, with the best model chosen by its performance on the validation data.\nYou find more information about the model and the performance on our blog: URL" ]

[ 56, 7, 117 ]

[ "TAGS\n#transformers #pytorch #megatron-bert #token-classification #sequence-tagger-model #bert #sv #dataset-KBLab/sucx3_ner #autotrain_compatible #endpoints_compatible #region-us \n# KB-BERT for NER## Cased data\n\nThis model is based on KB-BERT and was fine-tuned on the SUCX 3.0 - NER corpus, using the _simple_ tags and cased data.\nFor this model we used a variation of the data that did not use BIO-encoding to differentiate between the beginnings (B), and insides (I) of named entity tags.\n\nThe model was trained on the training data only, with the best model chosen by its performance on the validation data.\nYou find more information about the model and the performance on our blog: URL" ]

[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "sequence-tagger-model", "sv", "dataset:KBLab/sucx3_ner", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "sv" ]

[ "# KB-BERT for NER", "## Mixed cased and uncased data\n\nThis model is based on KB-BERT and was fine-tuned on the SUCX 3.0 - NER corpus, using the _simple_ tags and partially lowercased data.\nFor this model we used a variation of the data that did not use BIO-encoding to differentiate between the beginnings (B), and insides (I) of named entity tags.\n\nThe model was trained on the training data only, with the best model chosen by its performance on the validation data.\nYou find more information about the model and the performance on our blog: URL" ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #token-classification #sequence-tagger-model #sv #dataset-KBLab/sucx3_ner #autotrain_compatible #endpoints_compatible #region-us \n", "# KB-BERT for NER", "## Mixed cased and uncased data\n\nThis model is based on KB-BERT and was fine-tuned on the SUCX 3.0 - NER corpus, using the _simple_ tags and partially lowercased data.\nFor this model we used a variation of the data that did not use BIO-encoding to differentiate between the beginnings (B), and insides (I) of named entity tags.\n\nThe model was trained on the training data only, with the best model chosen by its performance on the validation data.\nYou find more information about the model and the performance on our blog: URL" ]

[ 55, 7, 122 ]

[ "TAGS\n#transformers #pytorch #safetensors #bert #token-classification #sequence-tagger-model #sv #dataset-KBLab/sucx3_ner #autotrain_compatible #endpoints_compatible #region-us \n# KB-BERT for NER## Mixed cased and uncased data\n\nThis model is based on KB-BERT and was fine-tuned on the SUCX 3.0 - NER corpus, using the _simple_ tags and partially lowercased data.\nFor this model we used a variation of the data that did not use BIO-encoding to differentiate between the beginnings (B), and insides (I) of named entity tags.\n\nThe model was trained on the training data only, with the best model chosen by its performance on the validation data.\nYou find more information about the model and the performance on our blog: URL" ]

[ "transformers", "pytorch", "safetensors", "megatron-bert", "fill-mask", "sv", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "sv" ]

[ "# Megatron-BERT-base Swedish 600k\n\nThis BERT model was trained using the Megatron-LM library.\nThe size of the model is a regular BERT-base with 110M parameters.\nThe model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 600k training steps. Its sister model used the same setup, but was instead trained for only 125k steps.\n\n\nThe model has three sister models trained on the same dataset:\n- BERT Swedish\n- Megatron-BERT-base-125k\n- Megatron-BERT-large-110k", "## Acknowledgements\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ "TAGS\n#transformers #pytorch #safetensors #megatron-bert #fill-mask #sv #autotrain_compatible #endpoints_compatible #region-us \n", "# Megatron-BERT-base Swedish 600k\n\nThis BERT model was trained using the Megatron-LM library.\nThe size of the model is a regular BERT-base with 110M parameters.\nThe model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 600k training steps. Its sister model used the same setup, but was instead trained for only 125k steps.\n\n\nThe model has three sister models trained on the same dataset:\n- BERT Swedish\n- Megatron-BERT-base-125k\n- Megatron-BERT-large-110k", "## Acknowledgements\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ 37, 130, 52 ]

[ "TAGS\n#transformers #pytorch #safetensors #megatron-bert #fill-mask #sv #autotrain_compatible #endpoints_compatible #region-us \n# Megatron-BERT-base Swedish 600k\n\nThis BERT model was trained using the Megatron-LM library.\nThe size of the model is a regular BERT-base with 110M parameters.\nThe model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 600k training steps. Its sister model used the same setup, but was instead trained for only 125k steps.\n\n\nThe model has three sister models trained on the same dataset:\n- BERT Swedish\n- Megatron-BERT-base-125k\n- Megatron-BERT-large-110k## Acknowledgements\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ "transformers", "pytorch", "safetensors", "megatron-bert", "fill-mask", "sv", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "sv" ]

[ "# Megatron-BERT-base Swedish 125k\n\nThis BERT model was trained using the Megatron-LM library.\nThe size of the model is a regular BERT-base with 110M parameters.\nThe model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 125k training steps. Its sister model used the same setup, but was instead trained for 600k steps.\n\n\nThe model has three sister models trained on the same dataset:\n- BERT Swedish\n- Megatron-BERT-base-600k\n- Megatron-BERT-large-110k", "## Acknowledgements\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ "TAGS\n#transformers #pytorch #safetensors #megatron-bert #fill-mask #sv #autotrain_compatible #endpoints_compatible #region-us \n", "# Megatron-BERT-base Swedish 125k\n\nThis BERT model was trained using the Megatron-LM library.\nThe size of the model is a regular BERT-base with 110M parameters.\nThe model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 125k training steps. Its sister model used the same setup, but was instead trained for 600k steps.\n\n\nThe model has three sister models trained on the same dataset:\n- BERT Swedish\n- Megatron-BERT-base-600k\n- Megatron-BERT-large-110k", "## Acknowledgements\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ 37, 129, 52 ]

[ "TAGS\n#transformers #pytorch #safetensors #megatron-bert #fill-mask #sv #autotrain_compatible #endpoints_compatible #region-us \n# Megatron-BERT-base Swedish 125k\n\nThis BERT model was trained using the Megatron-LM library.\nThe size of the model is a regular BERT-base with 110M parameters.\nThe model was trained on about 70GB of data, consisting mostly of OSCAR and Swedish newspaper text curated by the National Library of Sweden.\n\nTraining was done for 125k training steps. Its sister model used the same setup, but was instead trained for 600k steps.\n\n\nThe model has three sister models trained on the same dataset:\n- BERT Swedish\n- Megatron-BERT-base-600k\n- Megatron-BERT-large-110k## Acknowledgements\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL) for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ "transformers", "pytorch", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "# Roberta base TEST" ]

[ "TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n", "# Roberta base TEST" ]

[ 28, 4 ]

[ "TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# Roberta base TEST" ]

[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "sv", "arxiv:2004.09813", "license:apache-2.0", "endpoints_compatible", "has_space", "region:us" ]

[ "2004.09813" ]

[ "sv" ]

[ "### Loading an older model version (Sentence-Transformers)\n\n\nCurrently, the easiest way to load an older model version is to clone the model repository and load it from disk. For example, to clone the v1.0 model:\n\n\nThen you can load the model by pointing to the local folder where you cloned the model:\n\n\nUsage (HuggingFace Transformers)\n--------------------------------\n\n\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "### Loading an older model (Hugginfface Transformers)\n\n\nTo load an older model specify the version tag with the 'revision' arg. For example, to load the v1.0 model, use the following code:\n\n\nEvaluation Results\n------------------\n\n\nThe model was evaluated on SweParaphrase v1.0 and SweParaphrase v2.0. This test set is part of SuperLim -- a Swedish evaluation suite for natural langage understanding tasks. We calculated Pearson and Spearman correlation between predicted model similarity scores and the human similarity score labels. Results from SweParaphrase v1.0 are displayed below.\n\n\nModel version: v1.0, Pearson: 0.9183, Spearman: 0.9114\nModel version: v1.1, Pearson: 0.9183, Spearman: 0.9114\nModel version: v2.0, Pearson: 0.9283, Spearman: 0.9130\n\n\nThe following code snippet can be used to reproduce the above results:", "### Sweparaphrase v2.0\n\n\nIn general, v1.1 correlates the most with human assessment of text similarity on SweParaphrase v2.0. Below, we present zero-shot evaluation results on all data splits. They display the model's performance out of the box, without any fine-tuning.", "### SweFAQ v2.0\n\n\nWhen it comes to retrieval tasks, v2.0 performs the best by quite a substantial margin. It is better at matching the correct answer to a question compared to v1.1 and v1.0.\n\n\nModel version: v1.0, Data split: train, Accuracy: 0.5262\nModel version: v1.1, Data split: train, Accuracy: 0.6236\nModel version: v2.0, Data split: train, Accuracy: 0.7106\nModel version: v1.0, Data split: dev, Accuracy: 0.4636\nModel version: v1.1, Data split: dev, Accuracy: 0.5818\nModel version: v2.0, Data split: dev, Accuracy: 0.6727\nModel version: v1.0, Data split: test, Accuracy: 0.4495\nModel version: v1.1, Data split: test, Accuracy: 0.5229\nModel version: v2.0, Data split: test, Accuracy: 0.5871\n\n\nExamples how to evaluate the models on some of the test sets of the SuperLim suites can be found on the following links: evaluate\\_faq.py (Swedish FAQ), evaluate\\_swesat.py (SweSAT synonyms), evaluate\\_supersim.py (SuperSim).\n\n\nTraining\n--------\n\n\nAn article with more details on data and v1.0 of the model can be found on the KBLab blog.\n\n\nAround 14.6 million sentences from English-Swedish parallel corpuses were used to train the model. Data was sourced from the Open Parallel Corpus (OPUS) and downloaded via the python package opustools. Datasets used were: JW300, Europarl, DGT-TM, EMEA, ELITR-ECA, TED2020, Tatoeba and OpenSubtitles.\n\n\nThe model was trained with the parameters:\n\n\nDataLoader:\n\n\n'URL.dataloader.DataLoader' of length 180513 with parameters:\n\n\nLoss:\n\n\n'sentence\\_transformers.losses.MSELoss.MSELoss'\n\n\nParameters of the fit()-Method:\n\n\nFull Model Architecture\n-----------------------\n\n\nCiting & Authors\n----------------\n\n\nThis model was trained by KBLab, a data lab at the National Library of Sweden.\n\n\nYou can cite the article on our blog: URL .\n\n\nAcknowledgements\n----------------\n\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ "TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #sv #arxiv-2004.09813 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n", "### Loading an older model version (Sentence-Transformers)\n\n\nCurrently, the easiest way to load an older model version is to clone the model repository and load it from disk. For example, to clone the v1.0 model:\n\n\nThen you can load the model by pointing to the local folder where you cloned the model:\n\n\nUsage (HuggingFace Transformers)\n--------------------------------\n\n\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "### Loading an older model (Hugginfface Transformers)\n\n\nTo load an older model specify the version tag with the 'revision' arg. For example, to load the v1.0 model, use the following code:\n\n\nEvaluation Results\n------------------\n\n\nThe model was evaluated on SweParaphrase v1.0 and SweParaphrase v2.0. This test set is part of SuperLim -- a Swedish evaluation suite for natural langage understanding tasks. We calculated Pearson and Spearman correlation between predicted model similarity scores and the human similarity score labels. Results from SweParaphrase v1.0 are displayed below.\n\n\nModel version: v1.0, Pearson: 0.9183, Spearman: 0.9114\nModel version: v1.1, Pearson: 0.9183, Spearman: 0.9114\nModel version: v2.0, Pearson: 0.9283, Spearman: 0.9130\n\n\nThe following code snippet can be used to reproduce the above results:", "### Sweparaphrase v2.0\n\n\nIn general, v1.1 correlates the most with human assessment of text similarity on SweParaphrase v2.0. Below, we present zero-shot evaluation results on all data splits. They display the model's performance out of the box, without any fine-tuning.", "### SweFAQ v2.0\n\n\nWhen it comes to retrieval tasks, v2.0 performs the best by quite a substantial margin. It is better at matching the correct answer to a question compared to v1.1 and v1.0.\n\n\nModel version: v1.0, Data split: train, Accuracy: 0.5262\nModel version: v1.1, Data split: train, Accuracy: 0.6236\nModel version: v2.0, Data split: train, Accuracy: 0.7106\nModel version: v1.0, Data split: dev, Accuracy: 0.4636\nModel version: v1.1, Data split: dev, Accuracy: 0.5818\nModel version: v2.0, Data split: dev, Accuracy: 0.6727\nModel version: v1.0, Data split: test, Accuracy: 0.4495\nModel version: v1.1, Data split: test, Accuracy: 0.5229\nModel version: v2.0, Data split: test, Accuracy: 0.5871\n\n\nExamples how to evaluate the models on some of the test sets of the SuperLim suites can be found on the following links: evaluate\\_faq.py (Swedish FAQ), evaluate\\_swesat.py (SweSAT synonyms), evaluate\\_supersim.py (SuperSim).\n\n\nTraining\n--------\n\n\nAn article with more details on data and v1.0 of the model can be found on the KBLab blog.\n\n\nAround 14.6 million sentences from English-Swedish parallel corpuses were used to train the model. Data was sourced from the Open Parallel Corpus (OPUS) and downloaded via the python package opustools. Datasets used were: JW300, Europarl, DGT-TM, EMEA, ELITR-ECA, TED2020, Tatoeba and OpenSubtitles.\n\n\nThe model was trained with the parameters:\n\n\nDataLoader:\n\n\n'URL.dataloader.DataLoader' of length 180513 with parameters:\n\n\nLoss:\n\n\n'sentence\\_transformers.losses.MSELoss.MSELoss'\n\n\nParameters of the fit()-Method:\n\n\nFull Model Architecture\n-----------------------\n\n\nCiting & Authors\n----------------\n\n\nThis model was trained by KBLab, a data lab at the National Library of Sweden.\n\n\nYou can cite the article on our blog: URL .\n\n\nAcknowledgements\n----------------\n\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ 55, 155, 232, 73, 612 ]

[ "TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #sv #arxiv-2004.09813 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n### Loading an older model version (Sentence-Transformers)\n\n\nCurrently, the easiest way to load an older model version is to clone the model repository and load it from disk. For example, to clone the v1.0 model:\n\n\nThen you can load the model by pointing to the local folder where you cloned the model:\n\n\nUsage (HuggingFace Transformers)\n--------------------------------\n\n\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.### Loading an older model (Hugginfface Transformers)\n\n\nTo load an older model specify the version tag with the 'revision' arg. For example, to load the v1.0 model, use the following code:\n\n\nEvaluation Results\n------------------\n\n\nThe model was evaluated on SweParaphrase v1.0 and SweParaphrase v2.0. This test set is part of SuperLim -- a Swedish evaluation suite for natural langage understanding tasks. We calculated Pearson and Spearman correlation between predicted model similarity scores and the human similarity score labels. Results from SweParaphrase v1.0 are displayed below.\n\n\nModel version: v1.0, Pearson: 0.9183, Spearman: 0.9114\nModel version: v1.1, Pearson: 0.9183, Spearman: 0.9114\nModel version: v2.0, Pearson: 0.9283, Spearman: 0.9130\n\n\nThe following code snippet can be used to reproduce the above results:### Sweparaphrase v2.0\n\n\nIn general, v1.1 correlates the most with human assessment of text similarity on SweParaphrase v2.0. Below, we present zero-shot evaluation results on all data splits. They display the model's performance out of the box, without any fine-tuning.### SweFAQ v2.0\n\n\nWhen it comes to retrieval tasks, v2.0 performs the best by quite a substantial margin. It is better at matching the correct answer to a question compared to v1.1 and v1.0.\n\n\nModel version: v1.0, Data split: train, Accuracy: 0.5262\nModel version: v1.1, Data split: train, Accuracy: 0.6236\nModel version: v2.0, Data split: train, Accuracy: 0.7106\nModel version: v1.0, Data split: dev, Accuracy: 0.4636\nModel version: v1.1, Data split: dev, Accuracy: 0.5818\nModel version: v2.0, Data split: dev, Accuracy: 0.6727\nModel version: v1.0, Data split: test, Accuracy: 0.4495\nModel version: v1.1, Data split: test, Accuracy: 0.5229\nModel version: v2.0, Data split: test, Accuracy: 0.5871\n\n\nExamples how to evaluate the models on some of the test sets of the SuperLim suites can be found on the following links: evaluate\\_faq.py (Swedish FAQ), evaluate\\_swesat.py (SweSAT synonyms), evaluate\\_supersim.py (SuperSim).\n\n\nTraining\n--------\n\n\nAn article with more details on data and v1.0 of the model can be found on the KBLab blog.\n\n\nAround 14.6 million sentences from English-Swedish parallel corpuses were used to train the model. Data was sourced from the Open Parallel Corpus (OPUS) and downloaded via the python package opustools. Datasets used were: JW300, Europarl, DGT-TM, EMEA, ELITR-ECA, TED2020, Tatoeba and OpenSubtitles.\n\n\nThe model was trained with the parameters:\n\n\nDataLoader:\n\n\n'URL.dataloader.DataLoader' of length 180513 with parameters:\n\n\nLoss:\n\n\n'sentence\\_transformers.losses.MSELoss.MSELoss'\n\n\nParameters of the fit()-Method:\n\n\nFull Model Architecture\n-----------------------\n\n\nCiting & Authors\n----------------\n\n\nThis model was trained by KBLab, a data lab at the National Library of Sweden.\n\n\nYou can cite the article on our blog: URL .\n\n\nAcknowledgements\n----------------\n\n\nWe gratefully acknowledge the HPC RIVR consortium (URL) and EuroHPC JU (URL for funding this research by providing computing resources of the HPC system Vega at the Institute of Information Science (URL)." ]

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "voxpopuli", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

[]

[ "sv-SE" ]

[ "# Wav2vec 2.0 base-voxpopuli-sv-swedish\nFinetuned version of Facebooks VoxPopuli-sv base model using NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 5.62%, WER for Common Voice test set is 19.15%.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Usage\nThe model can be used directly (without a language model) as follows:" ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #voxpopuli #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n", "# Wav2vec 2.0 base-voxpopuli-sv-swedish\nFinetuned version of Facebooks VoxPopuli-sv base model using NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 5.62%, WER for Common Voice test set is 19.15%.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Usage\nThe model can be used directly (without a language model) as follows:" ]

[ 50, 105, 18 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #voxpopuli #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n# Wav2vec 2.0 base-voxpopuli-sv-swedish\nFinetuned version of Facebooks VoxPopuli-sv base model using NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 5.62%, WER for Common Voice test set is 19.15%.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:" ]

[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "voxpopuli", "license:cc-by-nc-4.0", "model-index", "endpoints_compatible", "region:us" ]

[]

[ "sv-SE" ]

[ "# Wav2vec 2.0 large-voxpopuli-sv-swedish\n\nPLEASE NOTE that this model performs better and has a less restrictive license.\n\n\nAdditionally pretrained and finetuned version of Facebooks VoxPopuli-sv large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 3.95%. WER for Common Voice test set is 10.99% directly and 7.82% with a 4-gram language model.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Training\nThis model has additionally pretrained on 1000h of Swedish local radio broadcasts, fine-tuned for 120000 updates on NST + CommonVoice and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed].", "## Usage\nThe model can be used directly (without a language model) as follows:" ]

[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #voxpopuli #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us \n", "# Wav2vec 2.0 large-voxpopuli-sv-swedish\n\nPLEASE NOTE that this model performs better and has a less restrictive license.\n\n\nAdditionally pretrained and finetuned version of Facebooks VoxPopuli-sv large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 3.95%. WER for Common Voice test set is 10.99% directly and 7.82% with a 4-gram language model.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.", "## Training\nThis model has additionally pretrained on 1000h of Swedish local radio broadcasts, fine-tuned for 120000 updates on NST + CommonVoice and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed].", "## Usage\nThe model can be used directly (without a language model) as follows:" ]

[ 56, 141, 99, 18 ]

[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #voxpopuli #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us \n# Wav2vec 2.0 large-voxpopuli-sv-swedish\n\nPLEASE NOTE that this model performs better and has a less restrictive license.\n\n\nAdditionally pretrained and finetuned version of Facebooks VoxPopuli-sv large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 3.95%. WER for Common Voice test set is 10.99% directly and 7.82% with a 4-gram language model.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Training\nThis model has additionally pretrained on 1000h of Swedish local radio broadcasts, fine-tuned for 120000 updates on NST + CommonVoice and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed].## Usage\nThe model can be used directly (without a language model) as follows:" ]

[ "transformers", "pytorch", "safetensors", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "hf-asr-leaderboard", "sv", "dataset:common_voice", "dataset:NST_Swedish_ASR_Database", "dataset:P4", "arxiv:2205.03026", "license:cc0-1.0", "model-index", "endpoints_compatible", "region:us" ]

[ "2205.03026" ]

[ "sv" ]

[ "# Wav2vec 2.0 large VoxRex Swedish (C)\n\nFinetuned version of KBs VoxRex large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 2.5%. WER for Common Voice test set is 8.49% directly and 7.37% with a 4-gram language model.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nUpdate 2022-01-10: Updated to VoxRex-C version.\n\nUpdate 2022-05-16: Paper is is here.", "# Performance\\*\n\n!Comparison\n<center><del>*<i>Chart shows performance without the additional 20k steps of Common Voice fine-tuning</i></del></center>", "## Training\nThis model has been fine-tuned for 120000 updates on NST + CommonVoice<del> and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed]</del>.\n\n!WER during training", "## Usage\nThe model can be used directly (without a language model) as follows:\n\n\nURL" ]

[ "TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #sv #dataset-common_voice #dataset-NST_Swedish_ASR_Database #dataset-P4 #arxiv-2205.03026 #license-cc0-1.0 #model-index #endpoints_compatible #region-us \n", "# Wav2vec 2.0 large VoxRex Swedish (C)\n\nFinetuned version of KBs VoxRex large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 2.5%. WER for Common Voice test set is 8.49% directly and 7.37% with a 4-gram language model.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nUpdate 2022-01-10: Updated to VoxRex-C version.\n\nUpdate 2022-05-16: Paper is is here.", "# Performance\\*\n\n!Comparison\n<center><del>*<i>Chart shows performance without the additional 20k steps of Common Voice fine-tuning</i></del></center>", "## Training\nThis model has been fine-tuned for 120000 updates on NST + CommonVoice<del> and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed]</del>.\n\n!WER during training", "## Usage\nThe model can be used directly (without a language model) as follows:\n\n\nURL" ]

[ 99, 149, 43, 99, 20 ]

[ "TAGS\n#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #sv #dataset-common_voice #dataset-NST_Swedish_ASR_Database #dataset-P4 #arxiv-2205.03026 #license-cc0-1.0 #model-index #endpoints_compatible #region-us \n# Wav2vec 2.0 large VoxRex Swedish (C)\n\nFinetuned version of KBs VoxRex large model using Swedish radio broadcasts, NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is 2.5%. WER for Common Voice test set is 8.49% directly and 7.37% with a 4-gram language model.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nUpdate 2022-01-10: Updated to VoxRex-C version.\n\nUpdate 2022-05-16: Paper is is here.# Performance\\*\n\n!Comparison\n<center><del>*<i>Chart shows performance without the additional 20k steps of Common Voice fine-tuning</i></del></center>## Training\nThis model has been fine-tuned for 120000 updates on NST + CommonVoice<del> and then for an additional 20000 updates on CommonVoice only. The additional fine-tuning on CommonVoice hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed]</del>.\n\n!WER during training## Usage\nThe model can be used directly (without a language model) as follows:\n\n\nURL" ]

[ "transformers", "pytorch", "wav2vec2", "pretraining", "audio", "automatic-speech-recognition", "voxrex", "sv", "arxiv:2205.03026", "license:cc0-1.0", "endpoints_compatible", "region:us" ]

[ "2205.03026" ]

[ "sv" ]

[ "# Wav2vec 2.0 large VoxRex (C)\n\nPlease note: The model hosted in this repository is a pretrained wav2vec2 without a CTC head, as such it cannot do speech-to-text. If you are interested in speech-to-text, see our finetuned version of this model, which can be found at KBLab/wav2vec2-large-voxrex-swedish. The weights found in this repository are from the pure acoustic model after unsupervised pretraining. This model is suitable for anyone interested in i) continued wav2vec2-pretraining with your own unsupervised data, ii) a feature extractor for finetuning your own downstream tasks (e.g. if you want to train your own CTC head, or an audio classifier). \n\nDisclaimer: This is a work in progress.<br>\nUpdate 2022-01-08: Updated to VoxRex-C version, use git to get the older (B) version.<br>\nUpdate 2022-05-16: Paper is is here.\n\nThis model has been pretrained for 400,000 updates on the P4-10k corpus which contains 10 000 hours of swedish local public service radio as well as 1500 hours of audio books and other speech from KBs collections.\n\n!Accuracy during training" ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxrex #sv #arxiv-2205.03026 #license-cc0-1.0 #endpoints_compatible #region-us \n", "# Wav2vec 2.0 large VoxRex (C)\n\nPlease note: The model hosted in this repository is a pretrained wav2vec2 without a CTC head, as such it cannot do speech-to-text. If you are interested in speech-to-text, see our finetuned version of this model, which can be found at KBLab/wav2vec2-large-voxrex-swedish. The weights found in this repository are from the pure acoustic model after unsupervised pretraining. This model is suitable for anyone interested in i) continued wav2vec2-pretraining with your own unsupervised data, ii) a feature extractor for finetuning your own downstream tasks (e.g. if you want to train your own CTC head, or an audio classifier). \n\nDisclaimer: This is a work in progress.<br>\nUpdate 2022-01-08: Updated to VoxRex-C version, use git to get the older (B) version.<br>\nUpdate 2022-05-16: Paper is is here.\n\nThis model has been pretrained for 400,000 updates on the P4-10k corpus which contains 10 000 hours of swedish local public service radio as well as 1500 hours of audio books and other speech from KBs collections.\n\n!Accuracy during training" ]

[ 62, 297 ]

[ "TAGS\n#transformers #pytorch #wav2vec2 #pretraining #audio #automatic-speech-recognition #voxrex #sv #arxiv-2205.03026 #license-cc0-1.0 #endpoints_compatible #region-us \n# Wav2vec 2.0 large VoxRex (C)\n\nPlease note: The model hosted in this repository is a pretrained wav2vec2 without a CTC head, as such it cannot do speech-to-text. If you are interested in speech-to-text, see our finetuned version of this model, which can be found at KBLab/wav2vec2-large-voxrex-swedish. The weights found in this repository are from the pure acoustic model after unsupervised pretraining. This model is suitable for anyone interested in i) continued wav2vec2-pretraining with your own unsupervised data, ii) a feature extractor for finetuning your own downstream tasks (e.g. if you want to train your own CTC head, or an audio classifier). \n\nDisclaimer: This is a work in progress.<br>\nUpdate 2022-01-08: Updated to VoxRex-C version, use git to get the older (B) version.<br>\nUpdate 2022-05-16: Paper is is here.\n\nThis model has been pretrained for 400,000 updates on the P4-10k corpus which contains 10 000 hours of swedish local public service radio as well as 1500 hours of audio books and other speech from KBs collections.\n\n!Accuracy during training" ]

[ "transformers", "pytorch", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "sv", "dataset:common_voice", "dataset:KTH/nst", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

[]

[ "sv" ]

[ "# Wav2Vec2-Large-XLSR-53-Swedish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Swedish using the NST Swedish Dictation.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nNote: We recommend using our newer model wav2vec2-large-voxrex-swedish for the best performance.", "## Usage\n\nThe model can be used directly (without a language model) as follows:", "## Evaluation\n\nThe model can be evaluated as follows on the Swedish test data of Common Voice.\n\n\n\n\nWER: 14.298610%\nCER: 4.925294%", "## Training\n\nFirst the XLSR model was further pre-trained for 50 epochs with a corpus consisting of 1000 hours spoken Swedish from various radio stations. Secondly NST Swedish Dictation was used for fine tuning as well as Common Voice. Lastly only Common Voice dataset was used for final finetuning. The Fairseq scripts were used." ]

[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sv #dataset-common_voice #dataset-KTH/nst #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "# Wav2Vec2-Large-XLSR-53-Swedish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Swedish using the NST Swedish Dictation.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nNote: We recommend using our newer model wav2vec2-large-voxrex-swedish for the best performance.", "## Usage\n\nThe model can be used directly (without a language model) as follows:", "## Evaluation\n\nThe model can be evaluated as follows on the Swedish test data of Common Voice.\n\n\n\n\nWER: 14.298610%\nCER: 4.925294%", "## Training\n\nFirst the XLSR model was further pre-trained for 50 epochs with a corpus consisting of 1000 hours spoken Swedish from various radio stations. Secondly NST Swedish Dictation was used for fine tuning as well as Common Voice. Lastly only Common Voice dataset was used for final finetuning. The Fairseq scripts were used." ]

[ 75, 90, 18, 38, 71 ]

[ "TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #sv #dataset-common_voice #dataset-KTH/nst #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-Swedish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Swedish using the NST Swedish Dictation.\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nNote: We recommend using our newer model wav2vec2-large-voxrex-swedish for the best performance.## Usage\n\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Swedish test data of Common Voice.\n\n\n\n\nWER: 14.298610%\nCER: 4.925294%## Training\n\nFirst the XLSR model was further pre-trained for 50 epochs with a corpus consisting of 1000 hours spoken Swedish from various radio stations. Secondly NST Swedish Dictation was used for fine tuning as well as Common Voice. Lastly only Common Voice dataset was used for final finetuning. The Fairseq scripts were used." ]

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Harry Potter DialoGPT Model" ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Harry Potter DialoGPT Model" ]

[ 39, 7 ]

[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model" ]

[ "transformers", "pytorch", "safetensors", "t5", "text2text-generation", "sentence correction", "en", "dataset:jfleg", "arxiv:1702.04066", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1702.04066" ]

[ "en" ]

[ "# Model\nThis model utilises T5-base pre-trained model. It was fine tuned using a modified version of the JFLEG dataset and Happy Transformer framework. This model was fine-tuned for sentence correction on normal English translations and positional English translations of local Caribbean English Creole. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creole checkout the library Caribe.\n\n___", "# Re-training/Fine Tuning\n\nThe results of fine-tuning resulted in a final accuracy of 92%", "# Usage \n\n\n\n\n___", "# Usage with Transformers\n\n\n___" ]

[ "TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #sentence correction #en #dataset-jfleg #arxiv-1702.04066 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model\nThis model utilises T5-base pre-trained model. It was fine tuned using a modified version of the JFLEG dataset and Happy Transformer framework. This model was fine-tuned for sentence correction on normal English translations and positional English translations of local Caribbean English Creole. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creole checkout the library Caribe.\n\n___", "# Re-training/Fine Tuning\n\nThe results of fine-tuning resulted in a final accuracy of 92%", "# Usage \n\n\n\n\n___", "# Usage with Transformers\n\n\n___" ]

[ 77, 90, 21, 5, 7 ]

[ "TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #sentence correction #en #dataset-jfleg #arxiv-1702.04066 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model\nThis model utilises T5-base pre-trained model. It was fine tuned using a modified version of the JFLEG dataset and Happy Transformer framework. This model was fine-tuned for sentence correction on normal English translations and positional English translations of local Caribbean English Creole. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creole checkout the library Caribe.\n\n___# Re-training/Fine Tuning\n\nThe results of fine-tuning resulted in a final accuracy of 92%# Usage \n\n\n\n\n___# Usage with Transformers\n\n\n___" ]

[ "transformers", "pytorch", "t5", "text2text-generation", "Trinidad and Tobago English Parser", "Caribe", "en", "arxiv:1702.04066", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1702.04066" ]

[ "en" ]

[ "# Trinidad English Creole Parser\nThis model was trained as a parser to Trinidad English Creole.\n\n---", "# Model\nThis model utilises T5-base pre-trained model. It was fine tuned using a combination of a custom dataset and creolised JFLEG dataset. JFLEG dataset was creolised using the file encoding feature of the Caribe library. For more on Caribbean Creole checkout the library Caribe.\n\n___", "# Usage with Transformers" ]

[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #Trinidad and Tobago English Parser #Caribe #en #arxiv-1702.04066 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Trinidad English Creole Parser\nThis model was trained as a parser to Trinidad English Creole.\n\n---", "# Model\nThis model utilises T5-base pre-trained model. It was fine tuned using a combination of a custom dataset and creolised JFLEG dataset. JFLEG dataset was creolised using the file encoding feature of the Caribe library. For more on Caribbean Creole checkout the library Caribe.\n\n___", "# Usage with Transformers" ]

[ 73, 22, 75, 4 ]

[ "TAGS\n#transformers #pytorch #t5 #text2text-generation #Trinidad and Tobago English Parser #Caribe #en #arxiv-1702.04066 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Trinidad English Creole Parser\nThis model was trained as a parser to Trinidad English Creole.\n\n---# Model\nThis model utilises T5-base pre-trained model. It was fine tuned using a combination of a custom dataset and creolised JFLEG dataset. JFLEG dataset was creolised using the file encoding feature of the Caribe library. For more on Caribbean Creole checkout the library Caribe.\n\n___# Usage with Transformers" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "t5", "text2text-generation", "ko", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[ "ko" ]

[ "# Model Card for ke-t5-base-ko", "# Model Details", "## Model Description\n \n \n- Developed by: Korea Electronics Technology Institute Artificial Intelligence Research Center\n- Shared by [Optional]: More information needed\n- Model type: Text2Text Generation\n- Language(s) (NLP): More information needed\n- License: More information needed\n- Related Models:\n - Parent Model: T5\n- Resources for more information: \n - GitHub Repo\n - KE-T5 Github Repo\n - Paper\n - Associated Paper\n - Blog Post", "# Uses", "## Direct Use\n \nThis model can be used for the task of Text2Text Generation", "## Downstream Use [Optional]\n \nMore information needed", "## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.", "# Bias, Risks, and Limitations\n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.", "## Recommendations\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.", "# Training Details", "## Training Data\n \nThe model is pre-trained on the Colossal Clean Crawled Corpus (C4), which was developed and released in the context of the same research paper as T5.\n \nThe model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.).\n \n See the t5-base model card for further information.", "## Training Procedure", "### Preprocessing\n \nMore information needed", "### Speeds, Sizes, Times\n \nMore information needed", "# Evaluation", "## Testing Data, Factors & Metrics", "### Testing Data\n \nMore information needed", "### Factors", "### Metrics\n \nMore information needed", "## Results \n \nMore information needed", "# Model Examination\n \nMore information needed", "# Environmental Impact\n \n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: More information needed\n- Hours used: More information needed\n- Cloud Provider: More information needed\n- Compute Region: More information needed\n- Carbon Emitted: More information needed", "# Technical Specifications [optional]", "## Model Architecture and Objective\n \nMore information needed", "## Compute Infrastructure\n \nMore information needed", "### Hardware\n \nMore information needed", "### Software\nMore information needed\n \nBibTeX:\n \n \n\n\n \n \nAPA:", "# Glossary [optional]\nMore information needed", "# More Information [optional]\n \nMore information needed", "# Model Card Authors [optional]\n \n \nKorea Electronics Technology Institute Artificial Intelligence Research Center in collaboration with Ezi Ozoani and the Hugging Face team", "# Model Card Contact\n \nMore information needed", "# How to Get Started with the Model\n \nUse the code below to get started with the model.\n \n<details>\n<summary> Click to expand </summary>\n\n\n</details>" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #ko #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "# Model Card for ke-t5-base-ko", "# Model Details", "## Model Description\n \n \n- Developed by: Korea Electronics Technology Institute Artificial Intelligence Research Center\n- Shared by [Optional]: More information needed\n- Model type: Text2Text Generation\n- Language(s) (NLP): More information needed\n- License: More information needed\n- Related Models:\n - Parent Model: T5\n- Resources for more information: \n - GitHub Repo\n - KE-T5 Github Repo\n - Paper\n - Associated Paper\n - Blog Post", "# Uses", "## Direct Use\n \nThis model can be used for the task of Text2Text Generation", "## Downstream Use [Optional]\n \nMore information needed", "## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.", "# Bias, Risks, and Limitations\n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.", "## Recommendations\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.", "# Training Details", "## Training Data\n \nThe model is pre-trained on the Colossal Clean Crawled Corpus (C4), which was developed and released in the context of the same research paper as T5.\n \nThe model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.).\n \n See the t5-base model card for further information.", "## Training Procedure", "### Preprocessing\n \nMore information needed", "### Speeds, Sizes, Times\n \nMore information needed", "# Evaluation", "## Testing Data, Factors & Metrics", "### Testing Data\n \nMore information needed", "### Factors", "### Metrics\n \nMore information needed", "## Results \n \nMore information needed", "# Model Examination\n \nMore information needed", "# Environmental Impact\n \n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: More information needed\n- Hours used: More information needed\n- Cloud Provider: More information needed\n- Compute Region: More information needed\n- Carbon Emitted: More information needed", "# Technical Specifications [optional]", "## Model Architecture and Objective\n \nMore information needed", "## Compute Infrastructure\n \nMore information needed", "### Hardware\n \nMore information needed", "### Software\nMore information needed\n \nBibTeX:\n \n \n\n\n \n \nAPA:", "# Glossary [optional]\nMore information needed", "# More Information [optional]\n \nMore information needed", "# Model Card Authors [optional]\n \n \nKorea Electronics Technology Institute Artificial Intelligence Research Center in collaboration with Ezi Ozoani and the Hugging Face team", "# Model Card Contact\n \nMore information needed", "# How to Get Started with the Model\n \nUse the code below to get started with the model.\n \n<details>\n<summary> Click to expand </summary>\n\n\n</details>" ]

[ 70, 12, 3, 93, 2, 17, 10, 25, 70, 33, 3, 82, 4, 10, 11, 2, 9, 8, 4, 8, 6, 6, 63, 6, 9, 7, 7, 14, 9, 9, 28, 7, 36 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #ko #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# Model Card for ke-t5-base-ko# Model Details## Model Description\n \n \n- Developed by: Korea Electronics Technology Institute Artificial Intelligence Research Center\n- Shared by [Optional]: More information needed\n- Model type: Text2Text Generation\n- Language(s) (NLP): More information needed\n- License: More information needed\n- Related Models:\n - Parent Model: T5\n- Resources for more information: \n - GitHub Repo\n - KE-T5 Github Repo\n - Paper\n - Associated Paper\n - Blog Post# Uses## Direct Use\n \nThis model can be used for the task of Text2Text Generation## Downstream Use [Optional]\n \nMore information needed## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.# Bias, Risks, and Limitations\n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.## Recommendations\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.# Training Details## Training Data\n \nThe model is pre-trained on the Colossal Clean Crawled Corpus (C4), which was developed and released in the context of the same research paper as T5.\n \nThe model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.).\n \n See the t5-base model card for further information.## Training Procedure### Preprocessing\n \nMore information needed### Speeds, Sizes, Times\n \nMore information needed# Evaluation## Testing Data, Factors & Metrics### Testing Data\n \nMore information needed### Factors### Metrics\n \nMore information needed## Results \n \nMore information needed# Model Examination\n \nMore information needed# Environmental Impact\n \n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: More information needed\n- Hours used: More information needed\n- Cloud Provider: More information needed\n- Compute Region: More information needed\n- Carbon Emitted: More information needed# Technical Specifications [optional]## Model Architecture and Objective\n \nMore information needed## Compute Infrastructure\n \nMore information needed### Hardware\n \nMore information needed### Software\nMore information needed\n \nBibTeX:\n \n \n\n\n \n \nAPA:# Glossary [optional]\nMore information needed# More Information [optional]\n \nMore information needed# Model Card Authors [optional]\n \n \nKorea Electronics Technology Institute Artificial Intelligence Research Center in collaboration with Ezi Ozoani and the Hugging Face team# Model Card Contact\n \nMore information needed# How to Get Started with the Model\n \nUse the code below to get started with the model.\n \n<details>\n<summary> Click to expand </summary>\n\n\n</details>" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "t5", "text2text-generation", "ko", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "ko", "en" ]

[ "# ke-t5 base\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.", "## How to use", "## BibTeX entry and citation info" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #ko #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ke-t5 base\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.", "## How to use", "## BibTeX entry and citation info" ]

[ 58, 29, 5, 9 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #ko #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ke-t5 base\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.## How to use## BibTeX entry and citation info" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "t5", "text2text-generation", "en", "ko", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[ "en", "ko" ]

[ "# Model Card for ke-t5-base", "# Model Details", "## Model Description\n \nThe developers of the Text-To-Text Transfer Transformer (T5) write: \n \n> With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.\n \nT5-Base is the checkpoint with 220 million parameters. \n \n \n- Developed by: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. \n- Shared by [Optional]: Korea Electronics Technology Institute Artificial Intelligence Research Center\n- Model type: Text Generation\n- Language(s) (NLP):More information needed\n- License: More information needed\n- Related Models: \n - Parent Model: T5\n- Resources for more information: \n - GitHub Repo\n - KE-T5 Github Repo\n - Paper\n - Associated Paper\n - Blog Post", "# Uses", "## Direct Use\n \nThe developers write in a blog post that the model: \n \n> Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself", "## Downstream Use [Optional]\n \nMore information needed", "## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.", "# Bias, Risks, and Limitations\n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.", "## Recommendations\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.", "# Training Details", "## Training Data\n \nThe model is pre-trained on the Colossal Clean Crawled Corpus (C4), which was developed and released in the context of the same research paper as T5.\n \nThe model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.).\n \n See the t5-base model card for further information.", "## Training Procedure", "### Preprocessing\n \nMore information needed", "### Speeds, Sizes, Times\n \nMore information needed", "# Evaluation", "## Testing Data, Factors & Metrics", "### Testing Data\n \nThe developers evaluated the model on 24 tasks, see the research paper for full details.", "### Factors\nMore information needed", "### Metrics\n \nMore information needed", "## Results \n \nFor full results for T5-Base, see the research paper, Table 14.", "# Model Examination\n \nMore information needed", "# Environmental Impact\n \n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: Google Cloud TPU Pods\n- Hours used: More information needed\n- Cloud Provider: GCP\n- Compute Region: More information needed\n- Carbon Emitted: More information needed", "# Technical Specifications [optional]", "## Model Architecture and Objective\n \nMore information needed", "## Compute Infrastructure\n \nMore information needed", "### Hardware\n \nMore information needed", "### Software\nMore information needed\n \nBibTeX:\n\n\n\n\n \nAPA:\n- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67.", "# Glossary [optional]\nMore information needed", "# More Information [optional]\n \nMore information needed", "# Model Card Authors [optional]\n \n \nKorea Electronics Technology Institute Artificial Intelligence Research Center in collaboration with Ezi Ozoani and the Hugging Face team", "# Model Card Contact\n \nMore information needed", "# How to Get Started with the Model\n \nUse the code below to get started with the model.\n \n<details>\n<summary> Click to expand </summary>\n\n\n \nSee the Hugging Face T5 docs and a Colab Notebook created by the model developers for more examples.\n</details>" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #en #ko #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for ke-t5-base", "# Model Details", "## Model Description\n \nThe developers of the Text-To-Text Transfer Transformer (T5) write: \n \n> With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.\n \nT5-Base is the checkpoint with 220 million parameters. \n \n \n- Developed by: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. \n- Shared by [Optional]: Korea Electronics Technology Institute Artificial Intelligence Research Center\n- Model type: Text Generation\n- Language(s) (NLP):More information needed\n- License: More information needed\n- Related Models: \n - Parent Model: T5\n- Resources for more information: \n - GitHub Repo\n - KE-T5 Github Repo\n - Paper\n - Associated Paper\n - Blog Post", "# Uses", "## Direct Use\n \nThe developers write in a blog post that the model: \n \n> Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself", "## Downstream Use [Optional]\n \nMore information needed", "## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.", "# Bias, Risks, and Limitations\n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.", "## Recommendations\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.", "# Training Details", "## Training Data\n \nThe model is pre-trained on the Colossal Clean Crawled Corpus (C4), which was developed and released in the context of the same research paper as T5.\n \nThe model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.).\n \n See the t5-base model card for further information.", "## Training Procedure", "### Preprocessing\n \nMore information needed", "### Speeds, Sizes, Times\n \nMore information needed", "# Evaluation", "## Testing Data, Factors & Metrics", "### Testing Data\n \nThe developers evaluated the model on 24 tasks, see the research paper for full details.", "### Factors\nMore information needed", "### Metrics\n \nMore information needed", "## Results \n \nFor full results for T5-Base, see the research paper, Table 14.", "# Model Examination\n \nMore information needed", "# Environmental Impact\n \n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: Google Cloud TPU Pods\n- Hours used: More information needed\n- Cloud Provider: GCP\n- Compute Region: More information needed\n- Carbon Emitted: More information needed", "# Technical Specifications [optional]", "## Model Architecture and Objective\n \nMore information needed", "## Compute Infrastructure\n \nMore information needed", "### Hardware\n \nMore information needed", "### Software\nMore information needed\n \nBibTeX:\n\n\n\n\n \nAPA:\n- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67.", "# Glossary [optional]\nMore information needed", "# More Information [optional]\n \nMore information needed", "# Model Card Authors [optional]\n \n \nKorea Electronics Technology Institute Artificial Intelligence Research Center in collaboration with Ezi Ozoani and the Hugging Face team", "# Model Card Contact\n \nMore information needed", "# How to Get Started with the Model\n \nUse the code below to get started with the model.\n \n<details>\n<summary> Click to expand </summary>\n\n\n \nSee the Hugging Face T5 docs and a Colab Notebook created by the model developers for more examples.\n</details>" ]

[ 68, 10, 3, 241, 2, 95, 10, 25, 70, 33, 3, 82, 4, 10, 11, 2, 9, 22, 7, 8, 20, 6, 64, 6, 9, 7, 7, 100, 9, 9, 28, 7, 58 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #en #ko #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for ke-t5-base# Model Details## Model Description\n \nThe developers of the Text-To-Text Transfer Transformer (T5) write: \n \n> With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.\n \nT5-Base is the checkpoint with 220 million parameters. \n \n \n- Developed by: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. \n- Shared by [Optional]: Korea Electronics Technology Institute Artificial Intelligence Research Center\n- Model type: Text Generation\n- Language(s) (NLP):More information needed\n- License: More information needed\n- Related Models: \n - Parent Model: T5\n- Resources for more information: \n - GitHub Repo\n - KE-T5 Github Repo\n - Paper\n - Associated Paper\n - Blog Post# Uses## Direct Use\n \nThe developers write in a blog post that the model: \n \n> Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself## Downstream Use [Optional]\n \nMore information needed## Out-of-Scope Use\n \nThe model should not be used to intentionally create hostile or alienating environments for people.# Bias, Risks, and Limitations\n \nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.## Recommendations\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.# Training Details## Training Data\n \nThe model is pre-trained on the Colossal Clean Crawled Corpus (C4), which was developed and released in the context of the same research paper as T5.\n \nThe model was pre-trained on a on a multi-task mixture of unsupervised (1.) and supervised tasks (2.).\n \n See the t5-base model card for further information.## Training Procedure### Preprocessing\n \nMore information needed### Speeds, Sizes, Times\n \nMore information needed# Evaluation## Testing Data, Factors & Metrics### Testing Data\n \nThe developers evaluated the model on 24 tasks, see the research paper for full details.### Factors\nMore information needed### Metrics\n \nMore information needed## Results \n \nFor full results for T5-Base, see the research paper, Table 14.# Model Examination\n \nMore information needed# Environmental Impact\n \n \nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n \n- Hardware Type: Google Cloud TPU Pods\n- Hours used: More information needed\n- Cloud Provider: GCP\n- Compute Region: More information needed\n- Carbon Emitted: More information needed# Technical Specifications [optional]## Model Architecture and Objective\n \nMore information needed## Compute Infrastructure\n \nMore information needed### Hardware\n \nMore information needed### Software\nMore information needed\n \nBibTeX:\n\n\n\n\n \nAPA:\n- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21(140), 1-67.# Glossary [optional]\nMore information needed# More Information [optional]\n \nMore information needed# Model Card Authors [optional]\n \n \nKorea Electronics Technology Institute Artificial Intelligence Research Center in collaboration with Ezi Ozoani and the Hugging Face team# Model Card Contact\n \nMore information needed# How to Get Started with the Model\n \nUse the code below to get started with the model.\n \n<details>\n<summary> Click to expand </summary>\n\n\n \nSee the Hugging Face T5 docs and a Colab Notebook created by the model developers for more examples.\n</details>" ]

[ "transformers", "pytorch", "tf", "jax", "t5", "text2text-generation", "ko", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "ko" ]

[ "# ke-t5 base\n\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.", "## How to use", "## BibTeX entry and citation info" ]

[ "TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ke-t5 base\n\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.", "## How to use", "## BibTeX entry and citation info" ]

[ 52, 29, 5, 9 ]

[ "TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ke-t5 base\n\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.## How to use## BibTeX entry and citation info" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "t5", "text2text-generation", "ko", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "ko", "en" ]

[ "# ke-t5 base\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.", "## How to use", "## BibTeX entry and citation info" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #ko #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ke-t5 base\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.", "## How to use", "## BibTeX entry and citation info" ]

[ 58, 29, 5, 9 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #ko #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ke-t5 base\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.## How to use## BibTeX entry and citation info" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "t5", "text2text-generation", "en", "ko", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "en", "ko" ]

[ "# ke-t5 base\n\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.", "## How to use", "## BibTeX entry and citation info" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #en #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ke-t5 base\n\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.", "## How to use", "## BibTeX entry and citation info" ]

[ 58, 29, 5, 9 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #en #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ke-t5 base\n\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.## How to use## BibTeX entry and citation info" ]

[ "transformers", "pytorch", "tf", "jax", "safetensors", "t5", "text2text-generation", "ko", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "ko" ]

[ "# ke-t5 base\n\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.", "## How to use", "## BibTeX entry and citation info" ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ke-t5 base\n\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.", "## How to use", "## BibTeX entry and citation info" ]

[ 56, 29, 5, 9 ]

[ "TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #ko #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ke-t5 base\n\nPretrained T5 Model on Korean and English. See Github and Paper Korean paper for more details.## How to use## BibTeX entry and citation info" ]