| 2024-08-30 21:54:12.390238: I tensorflow/core/util/port.cc:153] 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`. |
| 2024-08-30 21:54:12.408272: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered |
| 2024-08-30 21:54:12.429605: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered |
| 2024-08-30 21:54:12.436048: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered |
| 2024-08-30 21:54:12.451309: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. |
| To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. |
| 2024-08-30 21:54:13.743493: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT |
| /usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1494: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead |
| warnings.warn( |
| 08/30/2024 21:54:15 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False |
| 08/30/2024 21:54:15 - INFO - __main__ - Training/evaluation parameters TrainingArguments( |
| _n_gpu=1, |
| accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None, 'use_configured_state': False}, |
| adafactor=False, |
| adam_beta1=0.9, |
| adam_beta2=0.999, |
| adam_epsilon=1e-08, |
| auto_find_batch_size=False, |
| batch_eval_metrics=False, |
| bf16=False, |
| bf16_full_eval=False, |
| data_seed=None, |
| dataloader_drop_last=False, |
| dataloader_num_workers=0, |
| dataloader_persistent_workers=False, |
| dataloader_pin_memory=True, |
| dataloader_prefetch_factor=None, |
| ddp_backend=None, |
| ddp_broadcast_buffers=None, |
| ddp_bucket_cap_mb=None, |
| ddp_find_unused_parameters=None, |
| ddp_timeout=1800, |
| debug=[], |
| deepspeed=None, |
| disable_tqdm=False, |
| dispatch_batches=None, |
| do_eval=True, |
| do_predict=True, |
| do_train=True, |
| eval_accumulation_steps=None, |
| eval_delay=0, |
| eval_do_concat_batches=True, |
| eval_on_start=False, |
| eval_steps=None, |
| eval_strategy=epoch, |
| evaluation_strategy=epoch, |
| fp16=False, |
| fp16_backend=auto, |
| fp16_full_eval=False, |
| fp16_opt_level=O1, |
| fsdp=[], |
| fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, |
| fsdp_min_num_params=0, |
| fsdp_transformer_layer_cls_to_wrap=None, |
| full_determinism=False, |
| gradient_accumulation_steps=2, |
| gradient_checkpointing=False, |
| gradient_checkpointing_kwargs=None, |
| greater_is_better=True, |
| group_by_length=False, |
| half_precision_backend=auto, |
| hub_always_push=False, |
| hub_model_id=None, |
| hub_private_repo=False, |
| hub_strategy=every_save, |
| hub_token=<HUB_TOKEN>, |
| ignore_data_skip=False, |
| include_inputs_for_metrics=False, |
| include_num_input_tokens_seen=False, |
| include_tokens_per_second=False, |
| jit_mode_eval=False, |
| label_names=None, |
| label_smoothing_factor=0.0, |
| learning_rate=5e-05, |
| length_column_name=length, |
| load_best_model_at_end=True, |
| local_rank=0, |
| log_level=passive, |
| log_level_replica=warning, |
| log_on_each_node=True, |
| logging_dir=/content/dissertation/scripts/ner/output/tb, |
| logging_first_step=False, |
| logging_nan_inf_filter=True, |
| logging_steps=500, |
| logging_strategy=steps, |
| lr_scheduler_kwargs={}, |
| lr_scheduler_type=linear, |
| max_grad_norm=1.0, |
| max_steps=-1, |
| metric_for_best_model=f1, |
| mp_parameters=, |
| neftune_noise_alpha=None, |
| no_cuda=False, |
| num_train_epochs=10.0, |
| optim=adamw_torch, |
| optim_args=None, |
| optim_target_modules=None, |
| output_dir=/content/dissertation/scripts/ner/output, |
| overwrite_output_dir=True, |
| past_index=-1, |
| per_device_eval_batch_size=8, |
| per_device_train_batch_size=32, |
| prediction_loss_only=False, |
| push_to_hub=True, |
| push_to_hub_model_id=None, |
| push_to_hub_organization=None, |
| push_to_hub_token=<PUSH_TO_HUB_TOKEN>, |
| ray_scope=last, |
| remove_unused_columns=True, |
| report_to=['tensorboard'], |
| restore_callback_states_from_checkpoint=False, |
| resume_from_checkpoint=None, |
| run_name=/content/dissertation/scripts/ner/output, |
| save_on_each_node=False, |
| save_only_model=False, |
| save_safetensors=True, |
| save_steps=500, |
| save_strategy=epoch, |
| save_total_limit=None, |
| seed=42, |
| skip_memory_metrics=True, |
| split_batches=None, |
| tf32=None, |
| torch_compile=False, |
| torch_compile_backend=None, |
| torch_compile_mode=None, |
| torchdynamo=None, |
| tpu_metrics_debug=False, |
| tpu_num_cores=None, |
| use_cpu=False, |
| use_ipex=False, |
| use_legacy_prediction_loop=False, |
| use_mps_device=False, |
| warmup_ratio=0.0, |
| warmup_steps=0, |
| weight_decay=0.0, |
| ) |
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| [INFO|configuration_utils.py:733] 2024-08-30 21:54:27,962 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/config.json |
| [INFO|configuration_utils.py:800] 2024-08-30 21:54:27,966 >> Model config BertConfig { |
| "_name_or_path": "IVN-RIN/bioBIT", |
| "architectures": [ |
| "BertForMaskedLM" |
| ], |
| "attention_probs_dropout_prob": 0.1, |
| "classifier_dropout": null, |
| "finetuning_task": "ner", |
| "hidden_act": "gelu", |
| "hidden_dropout_prob": 0.1, |
| "hidden_size": 768, |
| "id2label": { |
| "0": "O", |
| "1": "B-FARMACO", |
| "2": "I-FARMACO" |
| }, |
| "initializer_range": 0.02, |
| "intermediate_size": 3072, |
| "label2id": { |
| "B-FARMACO": 1, |
| "I-FARMACO": 2, |
| "O": 0 |
| }, |
| "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", |
| "torch_dtype": "float32", |
| "transformers_version": "4.42.4", |
| "type_vocab_size": 2, |
| "use_cache": true, |
| "vocab_size": 31102 |
| } |
|
|
| [INFO|tokenization_utils_base.py:2161] 2024-08-30 21:54:29,333 >> loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/vocab.txt |
| [INFO|tokenization_utils_base.py:2161] 2024-08-30 21:54:29,334 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/tokenizer.json |
| [INFO|tokenization_utils_base.py:2161] 2024-08-30 21:54:29,334 >> loading file added_tokens.json from cache at None |
| [INFO|tokenization_utils_base.py:2161] 2024-08-30 21:54:29,334 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/special_tokens_map.json |
| [INFO|tokenization_utils_base.py:2161] 2024-08-30 21:54:29,334 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/tokenizer_config.json |
| [INFO|modeling_utils.py:3556] 2024-08-30 21:54:40,888 >> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/model.safetensors |
| [INFO|modeling_utils.py:4354] 2024-08-30 21:54:40,995 >> Some weights of the model checkpoint at IVN-RIN/bioBIT were not used when initializing BertForTokenClassification: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight'] |
| - This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). |
| [WARNING|modeling_utils.py:4366] 2024-08-30 21:54:40,995 >> Some weights of BertForTokenClassification were not initialized from the model checkpoint at IVN-RIN/bioBIT and are newly initialized: ['classifier.bias', 'classifier.weight'] |
| You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. |
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| /content/dissertation/scripts/ner/run_ner_train.py:397: 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("seqeval", trust_remote_code=True) |
| [INFO|trainer.py:805] 2024-08-30 21:54:47,484 >> The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`, you can safely ignore this message. |
| [INFO|trainer.py:2128] 2024-08-30 21:54:48,041 >> ***** Running training ***** |
| [INFO|trainer.py:2129] 2024-08-30 21:54:48,041 >> Num examples = 27,198 |
| [INFO|trainer.py:2130] 2024-08-30 21:54:48,041 >> Num Epochs = 10 |
| [INFO|trainer.py:2131] 2024-08-30 21:54:48,041 >> Instantaneous batch size per device = 32 |
| [INFO|trainer.py:2134] 2024-08-30 21:54:48,041 >> Total train batch size (w. parallel, distributed & accumulation) = 64 |
| [INFO|trainer.py:2135] 2024-08-30 21:54:48,041 >> Gradient Accumulation steps = 2 |
| [INFO|trainer.py:2136] 2024-08-30 21:54:48,041 >> Total optimization steps = 4,250 |
| [INFO|trainer.py:2137] 2024-08-30 21:54:48,042 >> Number of trainable parameters = 109,339,395 |
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10%|█ | 425/4250 [01:48<14:18, 4.46it/s][INFO|trainer.py:805] 2024-08-30 21:56:36,656 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`, you can safely ignore this message. |
| [INFO|trainer.py:3788] 2024-08-30 21:56:36,658 >> |
| ***** Running Evaluation ***** |
| [INFO|trainer.py:3790] 2024-08-30 21:56:36,658 >> Num examples = 6798 |
| [INFO|trainer.py:3793] 2024-08-30 21:56:36,658 >> Batch size = 8 |
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[A[INFO|trainer.py:3478] 2024-08-30 21:56:50,913 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-425 |
| [INFO|configuration_utils.py:472] 2024-08-30 21:56:50,914 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-425/config.json |
| [INFO|modeling_utils.py:2690] 2024-08-30 21:56:52,125 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-425/model.safetensors |
| [INFO|tokenization_utils_base.py:2574] 2024-08-30 21:56:52,126 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-425/tokenizer_config.json |
| [INFO|tokenization_utils_base.py:2583] 2024-08-30 21:56:52,126 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-425/special_tokens_map.json |
| [INFO|tokenization_utils_base.py:2574] 2024-08-30 21:56:54,071 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json |
| [INFO|tokenization_utils_base.py:2583] 2024-08-30 21:56:54,071 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json |
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11%|█▏ | 487/4250 [02:21<14:57, 4.19it/s]
11%|█▏ | 488/4250 [02:21<15:03, 4.16it/s] |
