2024-09-06 00:08:37.934804: 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`. 2024-09-06 00:08:37.953090: 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 2024-09-06 00:08:37.974597: 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 2024-09-06 00:08:37.981013: 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 2024-09-06 00:08:37.996733: 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. 2024-09-06 00:08:39.252831: 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( 09/06/2024 00:08:40 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False 09/06/2024 00:08:40 - 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%| | 0.00/4.00k [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] 2024-09-06 00:08:59,883 >> 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, "finetuning_task": "ner", "gradient_checkpointing": false, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "id2label": { "0": "O", "1": "B-ENFERMEDAD", "2": "I-ENFERMEDAD", "3": "B-PROCEDIMIENTO", "4": "I-PROCEDIMIENTO", "5": "B-SINTOMA", "6": "I-SINTOMA", "7": "B-FARMACO", "8": "I-FARMACO" }, "initializer_range": 0.02, "intermediate_size": 3072, "label2id": { "B-ENFERMEDAD": 1, "B-FARMACO": 7, "B-PROCEDIMIENTO": 3, "B-SINTOMA": 5, "I-ENFERMEDAD": 2, "I-FARMACO": 8, "I-PROCEDIMIENTO": 4, "I-SINTOMA": 6, "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] 2024-09-06 00:09:00,140 >> 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] 2024-09-06 00:09:00,140 >> 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] 2024-09-06 00:09:00,153 >> 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] 2024-09-06 00:09:00,153 >> 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] 2024-09-06 00:09:00,153 >> loading file tokenizer.json from cache at None [INFO|tokenization_utils_base.py:2269] 2024-09-06 00:09:00,153 >> loading file added_tokens.json from cache at None [INFO|tokenization_utils_base.py:2269] 2024-09-06 00:09:00,153 >> 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] 2024-09-06 00:09:00,153 >> 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] 2024-09-06 00:09:00,153 >> 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] 2024-09-06 00:09:00,154 >> 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] 2024-09-06 00:09:00,236 >> 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] 2024-09-06 00:09:00,238 >> 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] 2024-09-06 00:09:00,573 >> 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] 2024-09-06 00:09:00,651 >> 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] 2024-09-06 00:09:00,651 >> 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%| | 0/34604 [00:00> The following columns in the training set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:2134] 2024-09-06 00:09:08,371 >> ***** Running training ***** [INFO|trainer.py:2135] 2024-09-06 00:09:08,371 >> Num examples = 34,604 [INFO|trainer.py:2136] 2024-09-06 00:09:08,371 >> Num Epochs = 10 [INFO|trainer.py:2137] 2024-09-06 00:09:08,371 >> Instantaneous batch size per device = 32 [INFO|trainer.py:2140] 2024-09-06 00:09:08,371 >> Total train batch size (w. parallel, distributed & accumulation) = 64 [INFO|trainer.py:2141] 2024-09-06 00:09:08,371 >> Gradient Accumulation steps = 2 [INFO|trainer.py:2142] 2024-09-06 00:09:08,371 >> Total optimization steps = 5,410 [INFO|trainer.py:2143] 2024-09-06 00:09:08,372 >> Number of trainable parameters = 124,059,657 0%| | 0/5410 [00:00> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-06 00:11:24,747 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-06 00:11:24,747 >> Num examples = 6810 [INFO|trainer.py:3824] 2024-09-06 00:11:24,747 >> Batch size = 8 {'loss': 0.3191, 'grad_norm': 1.4528056383132935, 'learning_rate': 4.537892791127542e-05, 'epoch': 0.92} 0%| | 0/852 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-541 [INFO|configuration_utils.py:472] 2024-09-06 00:11:39,042 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-541/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:11:40,402 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-541/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:11:40,403 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-541/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:11:40,403 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-541/special_tokens_map.json [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:11:43,153 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:11:43,153 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json 10%|█ | 542/5410 [02:35<7:54:05, 5.84s/it] 10%|█ | 543/5410 [02:35<5:37:39, 4.16s/it] 10%|█ | 544/5410 [02:35<4:02:49, 2.99s/it] 10%|█ | 545/5410 [02:35<2:55:18, 2.16s/it] 10%|█ | 546/5410 [02:36<2:07:58, 1.58s/it] 10%|█ | 547/5410 [02:36<1:35:06, 1.17s/it] 10%|█ | 548/5410 [02:36<1:12:46, 1.11it/s] 10%|█ | 549/5410 [02:36<55:36, 1.46it/s] 10%|█ | 550/5410 [02:37<46:04, 1.76it/s] 10%|█ | 551/5410 [02:37<37:52, 2.14it/s] 10%|█ | 552/5410 [02:37<37:27, 2.16it/s] 10%|█ | 553/5410 [02:37<30:36, 2.65it/s] 10%|█ | 554/5410 [02:38<28:47, 2.81it/s] 10%|█ | 555/5410 [02:38<25:27, 3.18it/s] 10%|█ | 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[04:49<16:36, 4.35it/s] 20%|█▉ | 1077/5410 [04:49<16:48, 4.30it/s] 20%|█▉ | 1078/5410 [04:49<16:53, 4.28it/s] 20%|█▉ | 1079/5410 [04:50<19:46, 3.65it/s] 20%|█▉ | 1080/5410 [04:50<19:03, 3.79it/s] 20%|█▉ | 1081/5410 [04:50<18:06, 3.98it/s] 20%|██ | 1082/5410 [04:50<16:32, 4.36it/s][INFO|trainer.py:811] 2024-09-06 00:13:59,043 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-06 00:13:59,046 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-06 00:13:59,046 >> Num examples = 6810 [INFO|trainer.py:3824] 2024-09-06 00:13:59,046 >> Batch size = 8 {'eval_loss': 0.815065860748291, 'eval_precision': 0.08251737207833228, 'eval_recall': 0.9604779411764706, 'eval_f1': 0.15197789412449098, 'eval_accuracy': 0.7762986255097697, 'eval_runtime': 14.2875, 'eval_samples_per_second': 476.64, 'eval_steps_per_second': 59.632, 'epoch': 1.0} {'loss': 0.1619, 'grad_norm': 1.5587466955184937, 'learning_rate': 4.075785582255083e-05, 'epoch': 1.85} 0%| | 0/852 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1082 [INFO|configuration_utils.py:472] 2024-09-06 00:14:13,298 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1082/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:14:14,815 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1082/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:14:14,816 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1082/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:14:14,816 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1082/special_tokens_map.json [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:14:17,724 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:14:17,724 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json 20%|██ | 1083/5410 [05:09<7:03:15, 5.87s/it] 20%|██ | 1084/5410 [05:09<4:59:40, 4.16s/it] 20%|██ | 1085/5410 [05:10<3:33:52, 2.97s/it] 20%|██ | 1086/5410 [05:10<2:35:33, 2.16s/it] 20%|██ | 1087/5410 [05:10<1:54:41, 1.59s/it] 20%|██ | 1088/5410 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If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-06 00:16:32,506 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-06 00:16:32,507 >> Num examples = 6810 [INFO|trainer.py:3824] 2024-09-06 00:16:32,507 >> Batch size = 8 {'eval_loss': 0.833220362663269, 'eval_precision': 0.09218777996774771, 'eval_recall': 0.9457720588235294, 'eval_f1': 0.168, 'eval_accuracy': 0.7900778557403161, 'eval_runtime': 14.245, 'eval_samples_per_second': 478.064, 'eval_steps_per_second': 59.811, 'epoch': 2.0} {'loss': 0.11, 'grad_norm': 0.9616082310676575, 'learning_rate': 3.613678373382625e-05, 'epoch': 2.77} 0%| | 0/852 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1623 [INFO|configuration_utils.py:472] 2024-09-06 00:16:46,846 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1623/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:16:48,215 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1623/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:16:48,216 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1623/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:16:48,216 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1623/special_tokens_map.json [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:16:50,501 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:16:50,501 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json 30%|███ | 1624/5410 [07:42<5:59:13, 5.69s/it] 30%|███ | 1625/5410 [07:42<4:15:37, 4.05s/it] 30%|███ | 1626/5410 [07:43<3:04:11, 2.92s/it] 30%|███ | 1627/5410 [07:43<2:12:32, 2.10s/it] 30%|███ | 1628/5410 [07:43<1:37:08, 1.54s/it] 30%|███ | 1629/5410 [07:43<1:13:10, 1.16s/it] 30%|███ | 1630/5410 [07:43<55:13, 1.14it/s] 30%|███ 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If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-06 00:19:06,008 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-06 00:19:06,008 >> Num examples = 6810 [INFO|trainer.py:3824] 2024-09-06 00:19:06,008 >> Batch size = 8 {'eval_loss': 1.1093989610671997, 'eval_precision': 0.08991208982505004, 'eval_recall': 0.9494485294117647, 'eval_f1': 0.16426810845193607, 'eval_accuracy': 0.7738201491205184, 'eval_runtime': 14.3321, 'eval_samples_per_second': 475.159, 'eval_steps_per_second': 59.447, 'epoch': 3.0} {'loss': 0.0764, 'grad_norm': 1.4018645286560059, 'learning_rate': 3.1515711645101665e-05, 'epoch': 3.7} 0%| | 0/852 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2164 [INFO|configuration_utils.py:472] 2024-09-06 00:19:20,441 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2164/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:19:21,838 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2164/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:19:21,839 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2164/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:19:21,839 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2164/special_tokens_map.json [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:19:24,123 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:19:24,123 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json 40%|████ | 2165/5410 [10:16<5:11:18, 5.76s/it] 40%|████ | 2166/5410 [10:16<3:40:49, 4.08s/it] 40%|████ | 2167/5410 [10:16<2:38:21, 2.93s/it] 40%|████ | 2168/5410 [10:16<1:55:58, 2.15s/it] 40%|████ | 2169/5410 [10:17<1:25:43, 1.59s/it] 40%|████ | 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If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-06 00:21:39,894 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-06 00:21:39,894 >> Num examples = 6810 [INFO|trainer.py:3824] 2024-09-06 00:21:39,894 >> Batch size = 8 {'eval_loss': 1.1206157207489014, 'eval_precision': 0.08849100456227942, 'eval_recall': 0.9448529411764706, 'eval_f1': 0.16182605273514364, 'eval_accuracy': 0.7740055199307949, 'eval_runtime': 14.4251, 'eval_samples_per_second': 472.092, 'eval_steps_per_second': 59.064, 'epoch': 4.0} {'loss': 0.0567, 'grad_norm': 1.0904265642166138, 'learning_rate': 2.6894639556377083e-05, 'epoch': 4.62} 0%| | 0/852 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2705 [INFO|configuration_utils.py:472] 2024-09-06 00:21:54,100 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2705/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:21:55,474 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2705/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:21:55,475 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2705/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:21:55,476 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2705/special_tokens_map.json [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:21:57,861 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:21:57,861 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json 50%|█████ | 2706/5410 [12:49<4:14:26, 5.65s/it] 50%|█████ | 2707/5410 [12:49<3:01:06, 4.02s/it] 50%|█████ | 2708/5410 [12:50<2:09:13, 2.87s/it] 50%|█████ | 2709/5410 [12:50<1:34:08, 2.09s/it] 50%|█████ | 2710/5410 [12:50<1:09:08, 1.54s/it] 50%|█████ | 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If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-06 00:24:13,662 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-06 00:24:13,662 >> Num examples = 6810 [INFO|trainer.py:3824] 2024-09-06 00:24:13,662 >> Batch size = 8 {'eval_loss': 1.1805996894836426, 'eval_precision': 0.09400470929179497, 'eval_recall': 0.9540441176470589, 'eval_f1': 0.17114591920857378, 'eval_accuracy': 0.7890274211487498, 'eval_runtime': 14.1983, 'eval_samples_per_second': 479.634, 'eval_steps_per_second': 60.007, 'epoch': 5.0} {'loss': 0.0428, 'grad_norm': 1.240962028503418, 'learning_rate': 2.2273567467652497e-05, 'epoch': 5.55} 0%| | 0/852 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3246 [INFO|configuration_utils.py:472] 2024-09-06 00:24:27,857 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3246/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:24:29,238 >> Model weights saved in 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ner_tags, id. If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-06 00:26:46,928 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-06 00:26:46,928 >> Num examples = 6810 [INFO|trainer.py:3824] 2024-09-06 00:26:46,928 >> Batch size = 8 {'eval_loss': 1.31381094455719, 'eval_precision': 0.09005776299667426, 'eval_recall': 0.9457720588235294, 'eval_f1': 0.1644558094933674, 'eval_accuracy': 0.7827454103560493, 'eval_runtime': 14.1863, 'eval_samples_per_second': 480.042, 'eval_steps_per_second': 60.058, 'epoch': 6.0} {'loss': 0.0332, 'grad_norm': 0.44737720489501953, 'learning_rate': 1.7652495378927914e-05, 'epoch': 6.47} 0%| | 0/852 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3787 [INFO|configuration_utils.py:472] 2024-09-06 00:27:01,221 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3787/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:27:02,605 >> Model weights saved in 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If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-06 00:29:21,796 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-06 00:29:21,796 >> Num examples = 6810 [INFO|trainer.py:3824] 2024-09-06 00:29:21,797 >> Batch size = 8 {'eval_loss': 1.4008890390396118, 'eval_precision': 0.09217297102103457, 'eval_recall': 0.9384191176470589, 'eval_f1': 0.16785861076859843, 'eval_accuracy': 0.7874071429552226, 'eval_runtime': 14.2845, 'eval_samples_per_second': 476.742, 'eval_steps_per_second': 59.645, 'epoch': 7.0} {'loss': 0.0257, 'grad_norm': 0.7116318941116333, 'learning_rate': 1.3031423290203328e-05, 'epoch': 7.39} 0%| | 0/852 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4328 [INFO|configuration_utils.py:472] 2024-09-06 00:29:36,352 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4328/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:29:37,738 >> Model weights saved in 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ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-06 00:31:54,749 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-06 00:31:54,749 >> Num examples = 6810 [INFO|trainer.py:3824] 2024-09-06 00:31:54,749 >> Batch size = 8 {'eval_loss': 1.5610512495040894, 'eval_precision': 0.0904114426982165, 'eval_recall': 0.9411764705882353, 'eval_f1': 0.16497502819397453, 'eval_accuracy': 0.7790997844206132, 'eval_runtime': 14.5465, 'eval_samples_per_second': 468.153, 'eval_steps_per_second': 58.571, 'epoch': 8.0} {'loss': 0.022, 'grad_norm': 0.9520462155342102, 'learning_rate': 8.410351201478742e-06, 'epoch': 8.32} 0%| | 0/852 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4869 [INFO|configuration_utils.py:472] 2024-09-06 00:32:08,955 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4869/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:32:10,313 >> Model weights saved in 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/content/dissertation/scripts/ner/output/checkpoint-5410 [INFO|configuration_utils.py:472] 2024-09-06 00:34:29,136 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-5410/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:34:30,514 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-5410/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:34:30,516 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-5410/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:34:30,516 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-5410/special_tokens_map.json [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:34:32,841 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:34:32,841 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json [INFO|trainer.py:811] 2024-09-06 00:34:32,890 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-06 00:34:32,893 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-06 00:34:32,893 >> Num examples = 6810 [INFO|trainer.py:3824] 2024-09-06 00:34:32,893 >> Batch size = 8 {'eval_loss': 1.5933887958526611, 'eval_precision': 0.09211232337685567, 'eval_recall': 0.9466911764705882, 'eval_f1': 0.16788916055419725, 'eval_accuracy': 0.7835212215249838, 'eval_runtime': 14.1983, 'eval_samples_per_second': 479.635, 'eval_steps_per_second': 60.007, 'epoch': 9.0} {'loss': 0.0181, 'grad_norm': 0.6707109212875366, 'learning_rate': 3.789279112754159e-06, 'epoch': 9.24} 0%| | 0/852 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-5410 [INFO|configuration_utils.py:472] 2024-09-06 00:34:47,463 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-5410/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:34:48,865 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-5410/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:34:48,866 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-5410/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:34:48,866 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-5410/special_tokens_map.json [INFO|trainer.py:2394] 2024-09-06 00:34:50,732 >> Training completed. Do not forget to share your model on huggingface.co/models =) [INFO|trainer.py:2632] 2024-09-06 00:34:50,732 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-2705 (score: 0.17114591920857378). 100%|██████████| 5410/5410 [25:42<00:00, 3.15it/s] 100%|██████████| 5410/5410 [25:42<00:00, 3.51it/s] [INFO|trainer.py:4283] 2024-09-06 00:34:50,931 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes. [INFO|trainer.py:3503] 2024-09-06 00:35:00,710 >> Saving model checkpoint to /content/dissertation/scripts/ner/output [INFO|configuration_utils.py:472] 2024-09-06 00:35:00,711 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:35:02,143 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:35:02,144 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:35:02,144 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json [INFO|trainer.py:3503] 2024-09-06 00:35:02,196 >> Saving model checkpoint to /content/dissertation/scripts/ner/output [INFO|configuration_utils.py:472] 2024-09-06 00:35:02,198 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:35:03,525 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:35:03,526 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:35:03,526 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json {'eval_loss': 1.6103088855743408, 'eval_precision': 0.09223170184104176, 'eval_recall': 0.9439338235294118, 'eval_f1': 0.16804385175488834, 'eval_accuracy': 0.7862743213368668, 'eval_runtime': 14.5612, 'eval_samples_per_second': 467.681, 'eval_steps_per_second': 58.512, 'epoch': 10.0} {'train_runtime': 1542.5562, 'train_samples_per_second': 224.329, 'train_steps_per_second': 3.507, 'train_loss': 0.0812657987344287, 'epoch': 10.0} events.out.tfevents.1725581348.2a66098fac87.23851.0: 0%| | 0.00/12.6k [00:00> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-06 00:35:09,354 >> ***** Running Evaluation ***** [INFO|trainer.py:3821] 2024-09-06 00:35:09,354 >> Num examples = 6810 [INFO|trainer.py:3824] 2024-09-06 00:35:09,354 >> Batch size = 8 0%| | 0/852 [00:00> The following columns in the test set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message. [INFO|trainer.py:3819] 2024-09-06 00:35:23,875 >> ***** Running Prediction ***** [INFO|trainer.py:3821] 2024-09-06 00:35:23,875 >> Num examples = 14614 [INFO|trainer.py:3824] 2024-09-06 00:35:23,875 >> Batch size = 8 0%| | 0/1827 [00:00> Saving model checkpoint to /content/dissertation/scripts/ner/output [INFO|configuration_utils.py:472] 2024-09-06 00:35:54,297 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json [INFO|modeling_utils.py:2799] 2024-09-06 00:35:55,671 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors [INFO|tokenization_utils_base.py:2684] 2024-09-06 00:35:55,672 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json [INFO|tokenization_utils_base.py:2693] 2024-09-06 00:35:55,672 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json ***** predict metrics ***** predict_accuracy = 0.8712 predict_f1 = 0.2297 predict_loss = 0.7416 predict_precision = 0.1307 predict_recall = 0.9487 predict_runtime = 0:00:29.78 predict_samples_per_second = 490.651 predict_steps_per_second = 61.34 events.out.tfevents.1725582923.2a66098fac87.23851.1: 0%| | 0.00/560 [00:00