2024-09-09 12:14:35.494661: 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 12:14:35.513016: 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 12:14:35.535014: 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 12:14:35.541769: 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 12:14:35.557993: 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 12:14:36.793402: 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 12:14:38 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False 09/09/2024 12:14:38 - 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=, 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=, 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, ) Downloading builder script: 0%| | 0.00/3.92k [00:00> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json [INFO|configuration_utils.py:800] 2024-09-09 12:14:50,537 >> Model config RobertaConfig { "_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es", "architectures": [ "RobertaForMaskedLM" ], "attention_probs_dropout_prob": 0.1, "bos_token_id": 0, "classifier_dropout": null, "eos_token_id": 2, "finetuning_task": "ner", "gradient_checkpointing": false, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "id2label": { "0": "O", "1": "B-SINTOMA", "2": "I-SINTOMA" }, "initializer_range": 0.02, "intermediate_size": 3072, "label2id": { "B-SINTOMA": 1, "I-SINTOMA": 2, "O": 0 }, "layer_norm_eps": 1e-05, "max_position_embeddings": 514, "model_type": "roberta", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 1, "position_embedding_type": "absolute", "transformers_version": "4.44.2", "type_vocab_size": 1, "use_cache": true, "vocab_size": 50262 } [INFO|configuration_utils.py:733] 2024-09-09 12:14:50,787 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json [INFO|configuration_utils.py:800] 2024-09-09 12:14:50,788 >> Model config RobertaConfig { "_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es", "architectures": [ "RobertaForMaskedLM" ], "attention_probs_dropout_prob": 0.1, "bos_token_id": 0, "classifier_dropout": null, "eos_token_id": 2, "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-05, "max_position_embeddings": 514, "model_type": "roberta", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 1, "position_embedding_type": "absolute", "transformers_version": "4.44.2", "type_vocab_size": 1, "use_cache": true, "vocab_size": 50262 } [INFO|tokenization_utils_base.py:2269] 2024-09-09 12:14:50,800 >> loading file vocab.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/vocab.json [INFO|tokenization_utils_base.py:2269] 2024-09-09 12:14:50,801 >> loading file merges.txt from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/merges.txt [INFO|tokenization_utils_base.py:2269] 2024-09-09 12:14:50,801 >> loading file tokenizer.json from cache at None [INFO|tokenization_utils_base.py:2269] 2024-09-09 12:14:50,801 >> loading file added_tokens.json from cache at None [INFO|tokenization_utils_base.py:2269] 2024-09-09 12:14:50,801 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/special_tokens_map.json [INFO|tokenization_utils_base.py:2269] 2024-09-09 12:14:50,801 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/tokenizer_config.json [INFO|configuration_utils.py:733] 2024-09-09 12:14:50,801 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json [INFO|configuration_utils.py:800] 2024-09-09 12:14:50,802 >> Model config RobertaConfig { "_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es", "architectures": [ "RobertaForMaskedLM" ], "attention_probs_dropout_prob": 0.1, "bos_token_id": 0, "classifier_dropout": null, "eos_token_id": 2, "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-05, "max_position_embeddings": 514, "model_type": "roberta", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 1, "position_embedding_type": "absolute", "transformers_version": "4.44.2", "type_vocab_size": 1, "use_cache": true, "vocab_size": 50262 } /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|configuration_utils.py:733] 2024-09-09 12:14:50,882 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json [INFO|configuration_utils.py:800] 2024-09-09 12:14:50,883 >> Model config RobertaConfig { "_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es", "architectures": [ "RobertaForMaskedLM" ], "attention_probs_dropout_prob": 0.1, "bos_token_id": 0, "classifier_dropout": null, "eos_token_id": 2, "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-05, "max_position_embeddings": 514, "model_type": "roberta", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 1, "position_embedding_type": "absolute", "transformers_version": "4.44.2", "type_vocab_size": 1, "use_cache": true, "vocab_size": 50262 } [INFO|modeling_utils.py:3678] 2024-09-09 12:14:51,213 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/pytorch_model.bin [INFO|modeling_utils.py:4497] 2024-09-09 12:14:51,293 >> Some weights of the model checkpoint at PlanTL-GOB-ES/bsc-bio-ehr-es were not used when initializing RobertaForTokenClassification: ['lm_head.bias', 'lm_head.decoder.bias', 'lm_head.decoder.weight', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight'] - This IS expected if you are initializing RobertaForTokenClassification 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 RobertaForTokenClassification 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 12:14:51,293 >> Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at PlanTL-GOB-ES/bsc-bio-ehr-es 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. Map: 0%| | 0/10936 [00:00> The following columns in the training set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:2134] 2024-09-09 12:14:55,636 >> ***** Running training ***** [INFO|trainer.py:2135] 2024-09-09 12:14:55,636 >> Num examples = 10,936 [INFO|trainer.py:2136] 2024-09-09 12:14:55,636 >> Num Epochs = 10 [INFO|trainer.py:2137] 2024-09-09 12:14:55,636 >> Instantaneous batch size per device = 32 [INFO|trainer.py:2140] 2024-09-09 12:14:55,636 >> Total train batch size (w. parallel, distributed & accumulation) = 64 [INFO|trainer.py:2141] 2024-09-09 12:14:55,636 >> Gradient Accumulation steps = 2 [INFO|trainer.py:2142] 2024-09-09 12:14:55,636 >> Total optimization steps = 1,710 [INFO|trainer.py:2143] 2024-09-09 12:14:55,637 >> Number of trainable parameters = 124,055,043 0%| | 0/1710 [00:00> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-09 12:16:15,510 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-09 12:16:15,510 >> Num examples = 2519 [INFO|trainer.py:3824] 2024-09-09 12:16:15,510 >> Batch size = 8 0%| | 0/315 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-171 [INFO|configuration_utils.py:472] 2024-09-09 12:16:21,501 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-171/config.json [INFO|modeling_utils.py:2799] 2024-09-09 12:16:22,527 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-171/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:16:22,528 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-171/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-09 12:16:22,529 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-171/special_tokens_map.json 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If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-09 12:17:44,891 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-09 12:17:44,891 >> Num examples = 2519 [INFO|trainer.py:3824] 2024-09-09 12:17:44,891 >> Batch size = 8 {'eval_loss': 0.15023551881313324, 'eval_precision': 0.5421052631578948, 'eval_recall': 0.6765188834154351, 'eval_f1': 0.6018991964937911, 'eval_accuracy': 0.9458275851005807, 'eval_runtime': 5.988, 'eval_samples_per_second': 420.673, 'eval_steps_per_second': 52.605, 'epoch': 1.0} 0%| | 0/315 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-342 [INFO|configuration_utils.py:472] 2024-09-09 12:17:50,796 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-342/config.json [INFO|modeling_utils.py:2799] 2024-09-09 12:17:51,803 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-342/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:17:51,804 >> tokenizer config file saved 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[04:18<11:39, 1.71it/s][INFO|trainer.py:811] 2024-09-09 12:19:13,918 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-09 12:19:13,920 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-09 12:19:13,920 >> Num examples = 2519 [INFO|trainer.py:3824] 2024-09-09 12:19:13,920 >> Batch size = 8 {'eval_loss': 0.15393643081188202, 'eval_precision': 0.5957753240518483, 'eval_recall': 0.6792556102900931, 'eval_f1': 0.6347826086956522, 'eval_accuracy': 0.9468221630466168, 'eval_runtime': 5.9023, 'eval_samples_per_second': 426.784, 'eval_steps_per_second': 53.369, 'epoch': 2.0} {'loss': 0.1273, 'grad_norm': 1.020290732383728, 'learning_rate': 3.538011695906433e-05, 'epoch': 2.92} 0%| | 0/315 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-513 [INFO|configuration_utils.py:472] 2024-09-09 12:19:19,843 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-513/config.json [INFO|modeling_utils.py:2799] 2024-09-09 12:19:20,869 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-513/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:19:20,870 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-513/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-09 12:19:20,871 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-513/special_tokens_map.json [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:19:25,899 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-09 12:19:25,900 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json 30%|███ | 514/1710 [04:30<1:23:28, 4.19s/it] 30%|███ | 515/1710 [04:31<1:00:28, 3.04s/it] 30%|███ | 516/1710 [04:31<45:06, 2.27s/it] 30%|███ | 517/1710 [04:32<33:52, 1.70s/it] 30%|███ | 518/1710 [04:32<27:22, 1.38s/it] 30%|███ | 519/1710 [04:33<21:46, 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If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-09 12:20:44,625 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-09 12:20:44,625 >> Num examples = 2519 [INFO|trainer.py:3824] 2024-09-09 12:20:44,625 >> Batch size = 8 {'eval_loss': 0.1837530881166458, 'eval_precision': 0.6325831702544031, 'eval_recall': 0.7077175697865353, 'eval_f1': 0.6680444329630587, 'eval_accuracy': 0.9468382046263916, 'eval_runtime': 5.9204, 'eval_samples_per_second': 425.477, 'eval_steps_per_second': 53.206, 'epoch': 3.0} 0%| | 0/315 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-684 [INFO|configuration_utils.py:472] 2024-09-09 12:20:50,547 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-684/config.json [INFO|modeling_utils.py:2799] 2024-09-09 12:20:51,556 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-684/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:20:51,557 >> tokenizer config file saved 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If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-09 12:22:13,600 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-09 12:22:13,600 >> Num examples = 2519 [INFO|trainer.py:3824] 2024-09-09 12:22:13,600 >> Batch size = 8 {'eval_loss': 0.20180054008960724, 'eval_precision': 0.6321671525753159, 'eval_recall': 0.7120963327859879, 'eval_f1': 0.6697554697554697, 'eval_accuracy': 0.9466296640893195, 'eval_runtime': 5.9193, 'eval_samples_per_second': 425.559, 'eval_steps_per_second': 53.216, 'epoch': 4.0} 0%| | 0/315 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-855 [INFO|configuration_utils.py:472] 2024-09-09 12:22:19,503 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-855/config.json [INFO|modeling_utils.py:2799] 2024-09-09 12:22:20,520 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-855/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:22:20,521 >> tokenizer config file 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If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-09 12:23:43,185 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-09 12:23:43,185 >> Num examples = 2519 [INFO|trainer.py:3824] 2024-09-09 12:23:43,185 >> Batch size = 8 {'eval_loss': 0.21526136994361877, 'eval_precision': 0.6441176470588236, 'eval_recall': 0.7192118226600985, 'eval_f1': 0.6795965865011637, 'eval_accuracy': 0.9465013314511213, 'eval_runtime': 5.9003, 'eval_samples_per_second': 426.928, 'eval_steps_per_second': 53.387, 'epoch': 5.0} {'loss': 0.0234, 'grad_norm': 1.644018530845642, 'learning_rate': 2.0760233918128656e-05, 'epoch': 5.85} 0%| | 0/315 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1026 [INFO|configuration_utils.py:472] 2024-09-09 12:23:49,150 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1026/config.json [INFO|modeling_utils.py:2799] 2024-09-09 12:23:50,155 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1026/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:23:50,156 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1026/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-09 12:23:50,156 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1026/special_tokens_map.json [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:23:53,206 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-09 12:23:53,207 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json 60%|██████ | 1027/1710 [08:58<39:37, 3.48s/it] 60%|██████ | 1028/1710 [08:58<29:05, 2.56s/it] 60%|██████ | 1029/1710 [08:58<21:41, 1.91s/it] 60%|██████ | 1030/1710 [08:59<16:26, 1.45s/it] 60%|██████ | 1031/1710 [08:59<12:41, 1.12s/it] 60%|██████ | 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2024-09-09 12:25:11,389 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-09 12:25:11,391 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-09 12:25:11,392 >> Num examples = 2519 [INFO|trainer.py:3824] 2024-09-09 12:25:11,392 >> Batch size = 8 {'eval_loss': 0.24975360929965973, 'eval_precision': 0.6461383139828369, 'eval_recall': 0.7006020799124247, 'eval_f1': 0.6722689075630252, 'eval_accuracy': 0.9469825788443645, 'eval_runtime': 5.9627, 'eval_samples_per_second': 422.461, 'eval_steps_per_second': 52.829, 'epoch': 6.0} 0%| | 0/315 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1197 [INFO|configuration_utils.py:472] 2024-09-09 12:25:17,291 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1197/config.json [INFO|modeling_utils.py:2799] 2024-09-09 12:25:18,342 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1197/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:25:18,343 >> tokenizer config file 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1.95it/s][INFO|trainer.py:811] 2024-09-09 12:26:42,159 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-09 12:26:42,162 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-09 12:26:42,162 >> Num examples = 2519 [INFO|trainer.py:3824] 2024-09-09 12:26:42,162 >> Batch size = 8 {'eval_loss': 0.2653313875198364, 'eval_precision': 0.636231884057971, 'eval_recall': 0.7208538587848933, 'eval_f1': 0.6759045419553503, 'eval_accuracy': 0.9461804998556258, 'eval_runtime': 5.8972, 'eval_samples_per_second': 427.151, 'eval_steps_per_second': 53.415, 'epoch': 7.0} 0%| | 0/315 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1368 [INFO|configuration_utils.py:472] 2024-09-09 12:26:48,072 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1368/config.json [INFO|modeling_utils.py:2799] 2024-09-09 12:26:49,101 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1368/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:26:49,102 >> tokenizer config file 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If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-09 12:28:10,970 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-09 12:28:10,970 >> Num examples = 2519 [INFO|trainer.py:3824] 2024-09-09 12:28:10,970 >> Batch size = 8 {'eval_loss': 0.28080618381500244, 'eval_precision': 0.6529382219989954, 'eval_recall': 0.7115489874110563, 'eval_f1': 0.680984808800419, 'eval_accuracy': 0.9473354935994097, 'eval_runtime': 5.9073, 'eval_samples_per_second': 426.421, 'eval_steps_per_second': 53.324, 'epoch': 8.0} {'loss': 0.0082, 'grad_norm': 0.22467799484729767, 'learning_rate': 6.140350877192982e-06, 'epoch': 8.77} 0%| | 0/315 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1539 [INFO|configuration_utils.py:472] 2024-09-09 12:28:16,908 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1539/config.json [INFO|modeling_utils.py:2799] 2024-09-09 12:28:17,937 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1539/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:28:17,938 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1539/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-09 12:28:17,939 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1539/special_tokens_map.json [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:28:22,554 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-09 12:28:22,555 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json 90%|█████████ | 1540/1710 [13:27<11:03, 3.90s/it] 90%|█████████ | 1541/1710 [13:27<08:00, 2.85s/it] 90%|█████████ | 1542/1710 [13:28<05:53, 2.11s/it] 90%|█████████ | 1543/1710 [13:28<04:32, 1.63s/it] 90%|█████████ | 1544/1710 [13:29<03:37, 1.31s/it] 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Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1710/special_tokens_map.json [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:29:47,798 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-09 12:29:47,799 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json [INFO|trainer.py:811] 2024-09-09 12:29:47,847 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-09 12:29:47,850 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-09 12:29:47,850 >> Num examples = 2519 [INFO|trainer.py:3824] 2024-09-09 12:29:47,850 >> Batch size = 8 {'eval_loss': 0.2917279005050659, 'eval_precision': 0.6458022851465475, 'eval_recall': 0.7115489874110563, 'eval_f1': 0.6770833333333333, 'eval_accuracy': 0.9466938304084186, 'eval_runtime': 5.9353, 'eval_samples_per_second': 424.408, 'eval_steps_per_second': 53.072, 'epoch': 9.0} 0%| | 0/315 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1710 [INFO|configuration_utils.py:472] 2024-09-09 12:29:53,788 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1710/config.json [INFO|modeling_utils.py:2799] 2024-09-09 12:29:55,303 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1710/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:29:55,306 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1710/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-09 12:29:55,306 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1710/special_tokens_map.json [INFO|trainer.py:2394] 2024-09-09 12:29:57,247 >> Training completed. Do not forget to share your model on huggingface.co/models =) [INFO|trainer.py:2632] 2024-09-09 12:29:57,247 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-1368 (score: 0.680984808800419). 100%|██████████| 1710/1710 [15:01<00:00, 1.94it/s] 100%|██████████| 1710/1710 [15:01<00:00, 1.90it/s] [INFO|trainer.py:4283] 2024-09-09 12:29:57,436 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes. [INFO|trainer.py:3503] 2024-09-09 12:30:42,660 >> Saving model checkpoint to /content/dissertation/scripts/ner/output [INFO|configuration_utils.py:472] 2024-09-09 12:30:42,661 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json [INFO|modeling_utils.py:2799] 2024-09-09 12:30:44,034 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:30:44,035 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-09 12:30:44,035 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json [INFO|trainer.py:3503] 2024-09-09 12:30:44,082 >> Saving model checkpoint to /content/dissertation/scripts/ner/output [INFO|configuration_utils.py:472] 2024-09-09 12:30:44,083 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json [INFO|modeling_utils.py:2799] 2024-09-09 12:30:47,113 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-09 12:30:47,114 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-09 12:30:47,114 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json {'eval_loss': 0.2930145561695099, 'eval_precision': 0.6548403446528129, 'eval_recall': 0.7071702244116037, 'eval_f1': 0.6799999999999999, 'eval_accuracy': 0.9481215310083737, 'eval_runtime': 5.9346, 'eval_samples_per_second': 424.463, 'eval_steps_per_second': 53.079, 'epoch': 10.0} {'train_runtime': 901.7971, 'train_samples_per_second': 121.269, 'train_steps_per_second': 1.896, 'train_loss': 0.047100042948248794, 'epoch': 10.0} events.out.tfevents.1725884095.0a1c9bec2a53.15221.0: 0%| | 0.00/10.9k [00:00