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| 2023-04-10 14:38:24.700309: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F AVX512_VNNI FMA | |
| To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. | |
| 2023-04-10 14:38:24.840794: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. | |
| 2023-04-10 14:38:25.603565: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64: | |
| 2023-04-10 14:38:25.603723: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64: | |
| 2023-04-10 14:38:25.603751: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. | |
| 04/10/2023 14:38:27 - INFO - __main__ - Distributed environment: NO | |
| Num processes: 1 | |
| Process index: 0 | |
| Local process index: 0 | |
| Device: cuda | |
| Mixed precision type: no | |
| 04/10/2023 14:38:31 - WARNING - datasets.builder - Found cached dataset glue (/home/dingdaizong/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad) | |
| 0%| | 0/3 100%|██████████| 3/3 | |
| loading configuration file config.json from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/config.json | |
| Model config BertConfig { | |
| "_name_or_path": "bert-base-uncased", | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "finetuning_task": "sst2", | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.26.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |
| loading configuration file config.json from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/config.json | |
| Model config BertConfig { | |
| "_name_or_path": "bert-base-uncased", | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.26.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |
| loading file vocab.txt from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/vocab.txt | |
| loading file tokenizer.json from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/tokenizer.json | |
| loading file added_tokens.json from cache at None | |
| loading file special_tokens_map.json from cache at None | |
| loading file tokenizer_config.json from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/tokenizer_config.json | |
| loading configuration file config.json from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/config.json | |
| Model config BertConfig { | |
| "_name_or_path": "bert-base-uncased", | |
| "architectures": [ | |
| "BertForMaskedLM" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "transformers_version": "4.26.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |
| loading weights file pytorch_model.bin from cache at /home/dingdaizong/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/pytorch_model.bin | |
| Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight'] | |
| - This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). | |
| - This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). | |
| Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['bert.encoder.skim_predictors.5.predictor.2.bias', 'bert.encoder.skim_predictors.5.predictor.4.weight', 'bert.encoder.skim_predictors.4.predictor.0.weight', 'bert.encoder.skim_predictors.8.predictor.1.bias', 'bert.encoder.skim_predictors.6.predictor.0.weight', 'bert.encoder.skim_predictors.6.predictor.1.bias', 'bert.encoder.skim_predictors.11.predictor.2.weight', 'bert.encoder.skim_predictors.2.predictor.1.bias', 'bert.encoder.skim_predictors.11.predictor.1.bias', 'classifier.bias', 'bert.encoder.skim_predictors.9.predictor.1.bias', 'bert.encoder.skim_predictors.7.predictor.2.bias', 'bert.encoder.skim_predictors.7.predictor.0.weight', 'bert.encoder.skim_predictors.1.predictor.2.bias', 'bert.encoder.skim_predictors.5.predictor.4.bias', 'bert.encoder.skim_predictors.8.predictor.2.bias', 'classifier.weight', 'bert.encoder.skim_predictors.1.predictor.1.bias', 'bert.encoder.skim_predictors.2.predictor.0.weight', 'bert.encoder.skim_predictors.9.predictor.2.weight', 'bert.encoder.skim_predictors.9.predictor.4.bias', 'bert.encoder.skim_predictors.5.predictor.0.bias', 'bert.encoder.skim_predictors.3.predictor.2.weight', 'bert.encoder.skim_predictors.8.predictor.1.weight', 'bert.encoder.skim_predictors.6.predictor.2.weight', 'bert.encoder.skim_predictors.3.predictor.1.bias', 'bert.encoder.skim_predictors.0.predictor.2.bias', 'bert.encoder.skim_predictors.8.predictor.4.weight', 'bert.encoder.skim_predictors.11.predictor.0.bias', 'bert.encoder.skim_predictors.5.predictor.1.weight', 'bert.encoder.skim_predictors.6.predictor.0.bias', 'bert.encoder.skim_predictors.6.predictor.4.weight', 'bert.encoder.skim_predictors.1.predictor.0.bias', 'bert.encoder.skim_predictors.7.predictor.2.weight', 'bert.encoder.skim_predictors.0.predictor.0.bias', 'bert.encoder.skim_predictors.7.predictor.4.bias', 'bert.encoder.skim_predictors.1.predictor.4.bias', 'bert.encoder.skim_predictors.2.predictor.2.weight', 'bert.encoder.skim_predictors.0.predictor.2.weight', 'bert.encoder.skim_predictors.10.predictor.1.weight', 'bert.encoder.skim_predictors.0.predictor.1.weight', 'bert.encoder.skim_predictors.2.predictor.1.weight', 'bert.encoder.skim_predictors.7.predictor.4.weight', 'bert.encoder.skim_predictors.1.predictor.1.weight', 'bert.encoder.skim_predictors.3.predictor.4.bias', 'bert.encoder.skim_predictors.4.predictor.4.weight', 'bert.encoder.skim_predictors.5.predictor.0.weight', 'bert.encoder.skim_predictors.0.predictor.4.bias', 'bert.encoder.skim_predictors.11.predictor.4.bias', 'bert.encoder.skim_predictors.8.predictor.0.bias', 'bert.encoder.skim_predictors.10.predictor.2.bias', 'bert.encoder.skim_predictors.4.predictor.2.bias', 'bert.encoder.skim_predictors.7.predictor.0.bias', 'bert.encoder.skim_predictors.0.predictor.1.bias', 'bert.encoder.skim_predictors.10.predictor.1.bias', 'bert.encoder.skim_predictors.11.predictor.4.weight', 'bert.encoder.skim_predictors.3.predictor.0.weight', 'bert.encoder.skim_predictors.10.predictor.0.bias', 'bert.encoder.skim_predictors.2.predictor.4.bias', 'bert.encoder.skim_predictors.2.predictor.2.bias', 'bert.encoder.skim_predictors.5.predictor.2.weight', 'bert.encoder.skim_predictors.10.predictor.0.weight', 'bert.encoder.skim_predictors.1.predictor.4.weight', 'bert.encoder.skim_predictors.8.predictor.2.weight', 'bert.encoder.skim_predictors.6.predictor.4.bias', 'bert.encoder.skim_predictors.11.predictor.0.weight', 'bert.encoder.skim_predictors.3.predictor.2.bias', 'bert.encoder.skim_predictors.11.predictor.1.weight', 'bert.encoder.skim_predictors.5.predictor.1.bias', 'bert.encoder.skim_predictors.3.predictor.1.weight', 'bert.encoder.skim_predictors.6.predictor.1.weight', 'bert.encoder.skim_predictors.7.predictor.1.weight', 'bert.encoder.skim_predictors.1.predictor.2.weight', 'bert.encoder.skim_predictors.4.predictor.1.bias', 'bert.encoder.skim_predictors.8.predictor.0.weight', 'bert.encoder.skim_predictors.9.predictor.2.bias', 'bert.encoder.skim_predictors.9.predictor.1.weight', 'bert.encoder.skim_predictors.0.predictor.4.weight', 'bert.encoder.skim_predictors.9.predictor.0.weight', 'bert.encoder.skim_predictors.1.predictor.0.weight', 'bert.encoder.skim_predictors.3.predictor.4.weight', 'bert.encoder.skim_predictors.11.predictor.2.bias', 'bert.encoder.skim_predictors.4.predictor.2.weight', 'bert.encoder.skim_predictors.9.predictor.4.weight', 'bert.encoder.skim_predictors.7.predictor.1.bias', 'bert.encoder.skim_predictors.10.predictor.4.bias', 'bert.encoder.skim_predictors.0.predictor.0.weight', 'bert.encoder.skim_predictors.2.predictor.4.weight', 'bert.encoder.skim_predictors.10.predictor.4.weight', 'bert.encoder.skim_predictors.2.predictor.0.bias', 'bert.encoder.skim_predictors.3.predictor.0.bias', 'bert.encoder.skim_predictors.10.predictor.2.weight', 'bert.encoder.skim_predictors.8.predictor.4.bias', 'bert.encoder.skim_predictors.4.predictor.1.weight', 'bert.encoder.skim_predictors.4.predictor.0.bias', 'bert.encoder.skim_predictors.6.predictor.2.bias', 'bert.encoder.skim_predictors.9.predictor.0.bias', 'bert.encoder.skim_predictors.4.predictor.4.bias'] | |
| You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. | |
| Running tokenizer on dataset: 0%| | 0/67349 [00:00<?, ? examples/s] Running tokenizer on dataset: 1%|▏ | 1000/67349 [00:00<00:23, 2864.18 examples/s] Running tokenizer on dataset: 7%|▋ | 5000/67349 [00:00<00:05, 12037.71 examples/s] Running tokenizer on dataset: 15%|█▍ | 10000/67349 [00:00<00:02, 20081.14 examples/s] Running tokenizer on dataset: 21%|██ | 14000/67349 [00:00<00:02, 24192.62 examples/s] Running tokenizer on dataset: 27%|██▋ | 18000/67349 [00:00<00:01, 27588.38 examples/s] Running tokenizer on dataset: 34%|███▍ | 23000/67349 [00:00<00:01, 31490.62 examples/s] Running tokenizer on dataset: 40%|████ | 27000/67349 [00:01<00:01, 22659.67 examples/s] Running tokenizer on dataset: 46%|████▌ | 31000/67349 [00:01<00:01, 25449.15 examples/s] Running tokenizer on dataset: 52%|█████▏ | 35000/67349 [00:01<00:01, 28242.87 examples/s] Running tokenizer on dataset: 61%|██████ | 41000/67349 [00:01<00:00, 33479.16 examples/s] Running tokenizer on dataset: 70%|██████▉ | 47000/67349 [00:01<00:00, 35911.20 examples/s] Running tokenizer on dataset: 77%|███████▋ | 52000/67349 [00:02<00:00, 26993.24 examples/s] Running tokenizer on dataset: 85%|████████▍ | 57000/67349 [00:02<00:00, 30014.72 examples/s] Running tokenizer on dataset: 94%|█████████▎| 63000/67349 [00:02<00:00, 33072.85 examples/s] Running tokenizer on dataset: 100%|██████████| 67349/67349 [00:02<00:00, 34104.73 examples/s] Running tokenizer on dataset: 0%| | 0/872 [00:00<?, ? examples/s] Running tokenizer on dataset: 0%| | 0/1821 [00:00<?, ? examples/s] 04/10/2023 14:38:37 - INFO - __main__ - Sample 14592 of the training set: {'input_ids': [101, 1037, 2307, 3185, 102], 'token_type_ids': [0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1], 'labels': 1}. | |
| 04/10/2023 14:38:37 - INFO - __main__ - Sample 3278 of the training set: {'input_ids': [101, 14036, 1010, 2065, 5399, 16367, 1010, 2895, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': 1}. | |
| 04/10/2023 14:38:37 - INFO - __main__ - Sample 36048 of the training set: {'input_ids': [101, 2130, 2043, 2045, 2024, 11320, 12718, 1010, 1996, 6699, 4025, 14469, 1010, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': 1}. | |
| /home/dingdaizong/mlsnrs/pxd/Finetune_GPT-J_6B_8-bit/chatgpt/lib/python3.9/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning | |
| warnings.warn( | |
| /home/dingdaizong/mlsnrs/zsy/Transkimmer/src/run_glue_no_trainer.py:472: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate | |
| metric = load_metric("glue", args.task_name) | |
| 04/10/2023 14:38:41 - INFO - __main__ - ***** Running training ***** | |
| 04/10/2023 14:38:41 - INFO - __main__ - Num examples = 67349 | |
| 04/10/2023 14:38:41 - INFO - __main__ - Num Epochs = 3 | |
| 04/10/2023 14:38:41 - INFO - __main__ - Instantaneous batch size per device = 32 | |
| 04/10/2023 14:38:41 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 64 | |
| 04/10/2023 14:38:41 - INFO - __main__ - Gradient Accumulation steps = 2 | |
| 04/10/2023 14:38:41 - INFO - __main__ - Total optimization steps = 3159 | |
| 0%| | 0/3159 [00:00<?, ?it/s]You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. | |
| /home/dingdaizong/mlsnrs/pxd/Finetune_GPT-J_6B_8-bit/chatgpt/lib/python3.9/site-packages/transformers/modeling_utils.py:810: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers. | |
| warnings.warn( | |
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6.18it/s] 3%|▎ | 79/3159 [00:14<08:46, 5.85it/s] 3%|▎ | 80/3159 [00:14<08:42, 5.89it/s] 3%|▎ | 81/3159 [00:14<08:47, 5.83it/s] 3%|▎ | 82/3159 [00:15<08:52, 5.78it/s] 3%|▎ | 83/3159 [00:15<08:31, 6.01it/s] 3%|▎ | 84/3159 [00:15<08:42, 5.89it/s] 3%|▎ | 85/3159 [00:15<08:31, 6.00it/s] 3%|▎ | 86/3159 [00:15<08:36, 5.95it/s] 3%|▎ | 87/3159 [00:15<08:15, 6.20it/s] 3%|▎ | 88/3159 [00:16<08:40, 5.90it/s] 3%|▎ | 89/3159 [00:16<08:28, 6.04it/s] 3%|▎ | 90/3159 [00:16<08:20, 6.14it/s] 3%|▎ | 91/3159 [00:16<08:07, 6.30it/s] 3%|▎ | 92/3159 [00:16<08:27, 6.04it/s] 3%|▎ | 93/3159 [00:16<08:14, 6.20it/s] 3%|▎ | 94/3159 [00:17<08:17, 6.16it/s] 3%|▎ | 95/3159 [00:17<08:53, 5.75it/s] 3%|▎ | 96/3159 [00:17<08:39, 5.90it/s] 3%|▎ | 97/3159 [00:17<08:55, 5.72it/s] 3%|▎ | 98/3159 [00:17<08:46, 5.82it/s] 3%|▎ | 99/3159 [00:17<08:38, 5.90it/s] 3%|▎ | 100/3159 [00:18<08:41, 5.86it/s] 3%|▎ | 101/3159 [00:18<08:38, 5.90it/s] 3%|▎ | 102/3159 [00:18<08:50, 5.76it/s] 3%|▎ | 103/3159 [00:18<08:49, 5.78it/s] 3%|▎ | 104/3159 [00:18<08:56, 5.69it/s] 3%|▎ | 105/3159 [00:18<08:32, 5.96it/s] 3%|▎ | 106/3159 [00:19<08:16, 6.15it/s] 3%|▎ | 107/3159 [00:19<08:13, 6.19it/s] 3%|▎ | 108/3159 [00:19<08:27, 6.01it/s] 3%|▎ | 109/3159 [00:19<08:38, 5.88it/s] 3%|▎ | 110/3159 [00:19<08:21, 6.08it/s] 4%|▎ | 111/3159 [00:19<08:19, 6.10it/s] 4%|▎ | 112/3159 [00:20<08:26, 6.01it/s] 4%|▎ | 113/3159 [00:20<08:46, 5.78it/s] 4%|▎ | 114/3159 [00:20<08:27, 6.00it/s] 4%|▎ | 115/3159 [00:20<08:30, 5.96it/s] 4%|▎ | 116/3159 [00:20<08:29, 5.97it/s] 4%|▎ | 117/3159 [00:20<08:31, 5.95it/s] 4%|▎ | 118/3159 [00:21<08:45, 5.79it/s] 4%|▍ | 119/3159 [00:21<08:40, 5.84it/s] 4%|▍ | 120/3159 [00:21<08:43, 5.80it/s] 4%|▍ | 121/3159 [00:21<08:36, 5.89it/s] 4%|▍ | 122/3159 [00:21<08:40, 5.84it/s] 4%|▍ | 123/3159 [00:21<08:55, 5.67it/s] 4%|▍ | 124/3159 [00:22<08:57, 5.64it/s] 4%|▍ | 125/3159 [00:22<08:33, 5.91it/s] 4%|▍ | 126/3159 [00:22<08:22, 6.04it/s] 4%|▍ | 127/3159 [00:22<08:33, 5.91it/s] 4%|▍ | 128/3159 [00:22<08:50, 5.71it/s] 4%|▍ | 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[02:57<05:40, 6.18it/s] 33%|███▎ | 1052/3159 [02:58<05:49, 6.03it/s] 33%|███▎ | 1053/3159 [02:58<06:00, 5.85it/s]04/10/2023 14:41:40 - INFO - __main__ - epoch 0: {'accuracy': 0.8864678899082569}, skim_loss: 0.3177367150783539, tokens_remained: 0.602006733417511, layers: 0 0.9619914889335632 1 0.9600661993026733 2 0.7706087231636047 3 0.7703922986984253 4 0.7677611708641052 5 0.6576831340789795 6 0.529758632183075 7 0.5250933766365051 8 0.5245134830474854 9 0.5157971382141113 10 0.1531151831150055 11 0.0873003751039505 | |
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67%|██████▋ | 2106/3159 [05:56<03:03, 5.72it/s]04/10/2023 14:44:38 - INFO - __main__ - epoch 1: {'accuracy': 0.9094036697247706}, skim_loss: 0.2771478295326233, tokens_remained: 0.5250838398933411, layers: 0 0.9602835774421692 1 0.9588721990585327 2 0.6740683913230896 3 0.6737950444221497 4 0.6708476543426514 5 0.5446354150772095 6 0.43421828746795654 7 0.43116840720176697 8 0.4307778477668762 9 0.416260302066803 10 0.06037415936589241 11 0.04570453613996506 | |
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[08:53<00:00, 5.81it/s] 100%|█████████▉| 3156/3159 [08:53<00:00, 5.79it/s] 100%|█████████▉| 3157/3159 [08:53<00:00, 5.96it/s] 100%|█████████▉| 3158/3159 [08:53<00:00, 5.95it/s] 100%|██████████| 3159/3159 [08:54<00:00, 5.68it/s]04/10/2023 14:47:36 - INFO - __main__ - epoch 2: {'accuracy': 0.9105504587155964}, skim_loss: 0.26928749680519104, tokens_remained: 0.5101129412651062, layers: 0 0.9598081707954407 1 0.9589188694953918 2 0.6685288548469543 3 0.6680372953414917 4 0.6655147075653076 5 0.5209729671478271 6 0.40471377968788147 7 0.40040579438209534 8 0.4000111520290375 9 0.38064661622047424 10 0.05123211815953255 11 0.042564891278743744 | |
| 04/10/2023 14:47:37 - INFO - __main__ - eval: {'accuracy': 0.9036697247706422}, skim_loss: 0.2691411077976227, tokens_remained: 0.5098466873168945, layers: 0 0.9600897431373596 1 0.9590127468109131 2 0.6699763536453247 3 0.6696270704269409 4 0.6664778590202332 5 0.5199223160743713 6 0.4035145342350006 7 0.39916759729385376 8 0.398638516664505 9 0.3795333802700043 10 0.0497579500079155 11 0.04244190827012062 | |
| Configuration saved in model/glue/sst2/time_04-10-14-38/config.json | |
| Model weights saved in model/glue/sst2/time_04-10-14-38/pytorch_model.bin | |
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