| 2024-09-09 14:14:39.290280: 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`. | |
| 2024-09-09 14:14:39.308004: 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 | |
| 2024-09-09 14:14:39.329151: 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 | |
| 2024-09-09 14:14:39.335520: 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 | |
| 2024-09-09 14:14:39.350729: 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. | |
| 2024-09-09 14:14:40.598840: 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:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead | |
| warnings.warn( | |
| 09/09/2024 14:14:42 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False | |
| 09/09/2024 14:14:42 - 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, | |
| eval_use_gather_object=False, | |
| 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, | |
| torch_empty_cache_steps=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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| Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 664 examples [00:00, 6622.91 examples/s] Generating train split: 1371 examples [00:00, 6435.03 examples/s] Generating train split: 2045 examples [00:00, 6566.17 examples/s] Generating train split: 2763 examples [00:00, 6794.86 examples/s] Generating train split: 3818 examples [00:00, 6893.76 examples/s] Generating train split: 4836 examples [00:00, 6849.09 examples/s] Generating train split: 5809 examples [00:00, 6708.79 examples/s] Generating train split: 6849 examples [00:01, 6779.46 examples/s] Generating train split: 7889 examples [00:01, 6824.46 examples/s] Generating train split: 8897 examples [00:01, 6787.18 examples/s] Generating train split: 9919 examples [00:01, 6794.08 examples/s] Generating train split: 10944 examples [00:01, 6802.46 examples/s] Generating train split: 11973 examples [00:01, 6817.21 examples/s] Generating train split: 12961 examples [00:01, 6738.96 examples/s] Generating train split: 13968 examples [00:02, 6728.33 examples/s] Generating train split: 14993 examples [00:02, 6757.85 examples/s] Generating train split: 16000 examples [00:02, 6672.01 examples/s] Generating train split: 16724 examples [00:02, 6794.81 examples/s] Generating train split: 17741 examples [00:02, 6785.13 examples/s] Generating train split: 18442 examples [00:02, 6836.96 examples/s] Generating train split: 19493 examples [00:02, 6891.33 examples/s] Generating train split: 20472 examples [00:03, 6769.89 examples/s] Generating train split: 21478 examples [00:03, 6739.98 examples/s] Generating train split: 22488 examples [00:03, 6734.76 examples/s] Generating train split: 23507 examples [00:03, 6749.97 examples/s] Generating train split: 24510 examples [00:03, 6728.57 examples/s] Generating train split: 25510 examples [00:03, 6706.83 examples/s] Generating train split: 26546 examples [00:03, 6762.80 examples/s] Generating train split: 27524 examples [00:04, 6686.71 examples/s] Generating train split: 28567 examples [00:04, 6765.12 examples/s] Generating train split: 28668 examples [00:04, 6751.89 examples/s] | |
| Generating validation split: 0 examples [00:00, ? examples/s] Generating validation split: 750 examples [00:00, 7483.25 examples/s] Generating validation split: 1804 examples [00:00, 7152.87 examples/s] Generating validation split: 2812 examples [00:00, 6931.64 examples/s] Generating validation split: 3859 examples [00:00, 6946.74 examples/s] Generating validation split: 4881 examples [00:00, 4501.98 examples/s] Generating validation split: 5540 examples [00:01, 4892.51 examples/s] Generating validation split: 6203 examples [00:01, 5257.28 examples/s] Generating validation split: 6931 examples [00:01, 5725.20 examples/s] Generating validation split: 6946 examples [00:01, 5659.21 examples/s] | |
| Generating test split: 0 examples [00:00, ? examples/s] Generating test split: 812 examples [00:00, 8097.25 examples/s] Generating test split: 1963 examples [00:00, 7802.97 examples/s] Generating test split: 3084 examples [00:00, 7633.70 examples/s] Generating test split: 3854 examples [00:00, 7651.80 examples/s] Generating test split: 5000 examples [00:00, 7499.40 examples/s] Generating test split: 5895 examples [00:00, 7894.02 examples/s] Generating test split: 6710 examples [00:00, 7963.58 examples/s] Generating test split: 7934 examples [00:01, 8025.82 examples/s] Generating test split: 9082 examples [00:01, 7891.04 examples/s] Generating test split: 10232 examples [00:01, 7814.50 examples/s] Generating test split: 11028 examples [00:01, 7845.41 examples/s] Generating test split: 11910 examples [00:01, 8089.06 examples/s] Generating test split: 12987 examples [00:01, 7762.30 examples/s] Generating test split: 14085 examples [00:01, 7606.72 examples/s] Generating test split: 14715 examples [00:01, 7749.98 examples/s] | |
| [INFO|configuration_utils.py:733] 2024-09-09 14:15:00,004 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/config.json | |
| [INFO|configuration_utils.py:800] 2024-09-09 14:15:00,008 >> Model config BertConfig { | |
| "_name_or_path": "michiyasunaga/BioLinkBERT-base", | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "finetuning_task": "ner", | |
| "gradient_checkpointing": false, | |
| "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", | |
| "transformers_version": "4.44.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 28895 | |
| } | |
| [INFO|tokenization_utils_base.py:2269] 2024-09-09 14:15:00,242 >> loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/vocab.txt | |
| [INFO|tokenization_utils_base.py:2269] 2024-09-09 14:15:00,242 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/tokenizer.json | |
| [INFO|tokenization_utils_base.py:2269] 2024-09-09 14:15:00,242 >> loading file added_tokens.json from cache at None | |
| [INFO|tokenization_utils_base.py:2269] 2024-09-09 14:15:00,242 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/special_tokens_map.json | |
| [INFO|tokenization_utils_base.py:2269] 2024-09-09 14:15:00,242 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/tokenizer_config.json | |
| /usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884 | |
| warnings.warn( | |
| [INFO|modeling_utils.py:3678] 2024-09-09 14:15:00,548 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/pytorch_model.bin | |
| [INFO|modeling_utils.py:4497] 2024-09-09 14:15:00,628 >> Some weights of the model checkpoint at michiyasunaga/BioLinkBERT-base were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.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:4509] 2024-09-09 14:15:00,628 >> Some weights of BertForTokenClassification were not initialized from the model checkpoint at michiyasunaga/BioLinkBERT-base 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:811] 2024-09-09 14:15:07,256 >> 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:2134] 2024-09-09 14:15:07,820 >> ***** Running training ***** | |
| [INFO|trainer.py:2135] 2024-09-09 14:15:07,820 >> Num examples = 28,668 | |
| [INFO|trainer.py:2136] 2024-09-09 14:15:07,820 >> Num Epochs = 10 | |
| [INFO|trainer.py:2137] 2024-09-09 14:15:07,820 >> Instantaneous batch size per device = 32 | |
| [INFO|trainer.py:2140] 2024-09-09 14:15:07,820 >> Total train batch size (w. parallel, distributed & accumulation) = 64 | |
| [INFO|trainer.py:2141] 2024-09-09 14:15:07,820 >> Gradient Accumulation steps = 2 | |
| [INFO|trainer.py:2142] 2024-09-09 14:15:07,820 >> Total optimization steps = 4,480 | |
| [INFO|trainer.py:2143] 2024-09-09 14:15:07,820 >> Number of trainable parameters = 107,644,419 | |
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`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:3819] 2024-09-09 14:17:54,863 >> | |
| ***** Running Evaluation ***** | |
| [INFO|trainer.py:3821] 2024-09-09 14:17:54,863 >> Num examples = 6946 | |
| [INFO|trainer.py:3824] 2024-09-09 14:17:54,863 >> Batch size = 8 | |
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| [A[INFO|trainer.py:3503] 2024-09-09 14:18:09,952 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-448 | |
| [INFO|configuration_utils.py:472] 2024-09-09 14:18:09,953 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-448/config.json | |
| [INFO|modeling_utils.py:2799] 2024-09-09 14:18:10,847 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-448/model.safetensors | |
| [INFO|tokenization_utils_base.py:2684] 2024-09-09 14:18:10,848 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-448/tokenizer_config.json | |
| [INFO|tokenization_utils_base.py:2693] 2024-09-09 14:18:10,848 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-448/special_tokens_map.json | |
| [INFO|tokenization_utils_base.py:2684] 2024-09-09 14:18:13,510 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json | |
| [INFO|tokenization_utils_base.py:2693] 2024-09-09 14:18:13,510 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json | |
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