05/03/2024 16:57:20 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: False 05/03/2024 16:57:20 - INFO - __main__ - Training/evaluation parameters TrainingArguments( _n_gpu=1, adafactor=False, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=False, bf16=False, bf16_full_eval=False, data_seed=None, dataloader_drop_last=False, dataloader_num_workers=0, dataloader_pin_memory=True, ddp_bucket_cap_mb=None, ddp_find_unused_parameters=None, ddp_timeout=1800, debug=[], deepspeed=None, disable_tqdm=False, do_eval=True, do_predict=True, do_train=True, eval_accumulation_steps=1000, eval_delay=0, eval_steps=500, evaluation_strategy=steps, fp16=False, fp16_backend=auto, fp16_full_eval=False, fp16_opt_level=O1, fsdp=[], fsdp_min_num_params=0, fsdp_transformer_layer_cls_to_wrap=None, full_determinism=False, gradient_accumulation_steps=2, gradient_checkpointing=False, greater_is_better=True, group_by_length=False, half_precision_backend=auto, hub_model_id=None, hub_private_repo=False, hub_strategy=every_save, hub_token=, ignore_data_skip=False, include_inputs_for_metrics=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=-1, log_level=passive, log_level_replica=passive, log_on_each_node=True, logging_dir=./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/runs/May03_16-57-19_ppi-nlp6, logging_first_step=False, logging_nan_inf_filter=True, logging_steps=500, logging_strategy=steps, lr_scheduler_type=linear, max_grad_norm=1.0, max_steps=-1, metric_for_best_model=overall_f1, mp_parameters=, no_cuda=False, num_train_epochs=5.0, optim=adamw_hf, optim_args=None, output_dir=./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29, overwrite_output_dir=True, past_index=-1, per_device_eval_batch_size=4, per_device_train_batch_size=2, prediction_loss_only=False, push_to_hub=False, push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=, ray_scope=last, remove_unused_columns=True, report_to=[], resume_from_checkpoint=None, run_name=./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29, save_on_each_node=False, save_steps=500, save_strategy=steps, save_total_limit=2, seed=42, sharded_ddp=[], skip_memory_metrics=True, tf32=None, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, torchdynamo=None, tpu_metrics_debug=False, tpu_num_cores=None, use_ipex=False, use_legacy_prediction_loop=False, use_mps_device=False, warmup_ratio=0.0, warmup_steps=0, weight_decay=0.0, xpu_backend=None, ) /home/ext_maiara_frodrigues2000_gmail_/.pyenv/versions/spokennlp/lib/python3.8/site-packages/datasets/load.py:2555: FutureWarning: 'ignore_verifications' was deprecated in favor of 'verification_mode' in version 2.9.1 and will be removed in 3.0.0. You can remove this warning by passing 'verification_mode=no_checks' instead. warnings.warn( /home/ext_maiara_frodrigues2000_gmail_/.pyenv/versions/spokennlp/lib/python3.8/site-packages/datasets/load.py:929: FutureWarning: The repository for wiki_section_disease contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at ./src/datasets/wiki_section_disease/wiki_section_disease.py You can avoid this message in future by passing the argument `trust_remote_code=True`. Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`. warnings.warn( No config specified, defaulting to the single config: wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation 05/03/2024 16:57:20 - INFO - datasets.builder - No config specified, defaulting to the single config: wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation Using custom data configuration WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd 05/03/2024 16:57:20 - INFO - datasets.builder - Using custom data configuration WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd Loading Dataset Infos from /home/ext_maiara_frodrigues2000_gmail_/.cache/huggingface/modules/datasets_modules/datasets/wiki_section_disease/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8 05/03/2024 16:57:20 - INFO - datasets.info - Loading Dataset Infos from /home/ext_maiara_frodrigues2000_gmail_/.cache/huggingface/modules/datasets_modules/datasets/wiki_section_disease/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8 Generating dataset wiki_section_disease (/projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8) 05/03/2024 16:57:21 - INFO - datasets.builder - Generating dataset wiki_section_disease (/projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8) Downloading and preparing dataset wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation to /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8... 05/03/2024 16:57:21 - INFO - datasets.builder - Downloading and preparing dataset wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation to /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8... Downloading took 0.0 min 05/03/2024 16:57:21 - INFO - datasets.download.download_manager - Downloading took 0.0 min Checksum Computation took 0.0 min 05/03/2024 16:57:21 - INFO - datasets.download.download_manager - Checksum Computation took 0.0 min Generating train split 05/03/2024 16:57:21 - INFO - datasets.builder - Generating train split Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 1 examples [00:00, 6.70 examples/s] Generating train split: 164 examples [00:00, 794.20 examples/s] Generating train split: 316 examples [00:00, 1084.09 examples/s] Generating train split: 481 examples [00:00, 1286.37 examples/s] Generating train split: 652 examples [00:00, 1429.42 examples/s] Generating train split: 805 examples [00:00, 1457.55 examples/s] Generating train split: 989 examples [00:00, 1577.14 examples/s] Generating train split: 1156 examples [00:01, 468.44 examples/s] Generating train split: 1320 examples [00:01, 603.33 examples/s] Generating train split: 1470 examples [00:01, 728.87 examples/s] Generating train split: 1615 examples [00:01, 845.57 examples/s] Generating train split: 1758 examples [00:02, 957.06 examples/s] Generating train split: 1934 examples [00:02, 1125.95 examples/s] Generating train split: 2085 examples [00:02, 1004.54 examples/s] Generating train split: 2228 examples [00:02, 1095.01 examples/s] Generating train split: 2388 examples [00:02, 1211.03 examples/s] Generating train split: 2513 examples [00:02, 952.33 examples/s] Generating validation split topic_segmentation 05/03/2024 16:57:23 - INFO - datasets.builder - Generating validation split Generating validation split: 0 examples [00:00, ? examples/s] Generating validation split: 133 examples [00:00, 1316.16 examples/s] Generating validation split: 300 examples [00:00, 1515.86 examples/s] Generating validation split: 359 examples [00:00, 1352.40 examples/s] Generating test split topic_segmentation 05/03/2024 16:57:24 - INFO - datasets.builder - Generating test split Generating test split: 0 examples [00:00, ? examples/s] Generating test split: 128 examples [00:00, 1266.55 examples/s] Generating test split: 280 examples [00:00, 1406.73 examples/s] Generating test split: 441 examples [00:00, 1493.00 examples/s] Generating test split: 631 examples [00:00, 1640.48 examples/s] Generating test split: 718 examples [00:00, 1363.56 examples/s] Dataset wiki_section_disease downloaded and prepared to /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8. Subsequent calls will reuse this data. topic_segmentation 05/03/2024 16:57:24 - INFO - datasets.builder - Dataset wiki_section_disease downloaded and prepared to /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8. Subsequent calls will reuse this data. [INFO|configuration_utils.py:660] 2024-05-03 16:57:25,374 >> loading configuration file config.json from cache at /home/ext_maiara_frodrigues2000_gmail_/.cache/huggingface/hub/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json [INFO|configuration_utils.py:712] 2024-05-03 16:57:25,466 >> Model config BertConfig { "_name_or_path": "neuralmind/bert-base-portuguese-cased", "architectures": [ "BertForMaskedLM" ], "attention_probs_dropout_prob": 0.1, "classifier_dropout": null, "directionality": "bidi", "finetuning_task": "topic_segment", "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 12, "output_past": true, "pad_token_id": 0, "pooler_fc_size": 768, "pooler_num_attention_heads": 12, "pooler_num_fc_layers": 3, "pooler_size_per_head": 128, "pooler_type": "first_token_transform", "position_embedding_type": "absolute", "transformers_version": "4.26.0", "type_vocab_size": 2, "use_cache": true, "vocab_size": 29794 } [INFO|configuration_utils.py:660] 2024-05-03 16:57:25,668 >> loading configuration file config.json from cache at /home/ext_maiara_frodrigues2000_gmail_/.cache/huggingface/hub/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json [INFO|configuration_utils.py:712] 2024-05-03 16:57:25,669 >> Model config BertConfig { "_name_or_path": "neuralmind/bert-base-portuguese-cased", "architectures": [ "BertForMaskedLM" ], "attention_probs_dropout_prob": 0.1, "classifier_dropout": null, "directionality": "bidi", "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 12, "output_past": true, "pad_token_id": 0, "pooler_fc_size": 768, "pooler_num_attention_heads": 12, "pooler_num_fc_layers": 3, "pooler_size_per_head": 128, "pooler_type": "first_token_transform", "position_embedding_type": "absolute", "transformers_version": "4.26.0", "type_vocab_size": 2, "use_cache": true, "vocab_size": 29794 } [INFO|tokenization_utils_base.py:1802] 2024-05-03 16:57:26,251 >> loading file vocab.txt from cache at /home/ext_maiara_frodrigues2000_gmail_/.cache/huggingface/hub/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/vocab.txt [INFO|tokenization_utils_base.py:1802] 2024-05-03 16:57:26,251 >> loading file tokenizer.json from cache at None [INFO|tokenization_utils_base.py:1802] 2024-05-03 16:57:26,251 >> loading file added_tokens.json from cache at /home/ext_maiara_frodrigues2000_gmail_/.cache/huggingface/hub/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/added_tokens.json [INFO|tokenization_utils_base.py:1802] 2024-05-03 16:57:26,251 >> loading file special_tokens_map.json from cache at /home/ext_maiara_frodrigues2000_gmail_/.cache/huggingface/hub/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/special_tokens_map.json [INFO|tokenization_utils_base.py:1802] 2024-05-03 16:57:26,251 >> loading file tokenizer_config.json from cache at /home/ext_maiara_frodrigues2000_gmail_/.cache/huggingface/hub/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/tokenizer_config.json [INFO|configuration_utils.py:660] 2024-05-03 16:57:26,252 >> loading configuration file config.json from cache at /home/ext_maiara_frodrigues2000_gmail_/.cache/huggingface/hub/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json [INFO|configuration_utils.py:712] 2024-05-03 16:57:26,253 >> Model config BertConfig { "_name_or_path": "neuralmind/bert-base-portuguese-cased", "architectures": [ "BertForMaskedLM" ], "attention_probs_dropout_prob": 0.1, "classifier_dropout": null, "directionality": "bidi", "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 12, "output_past": true, "pad_token_id": 0, "pooler_fc_size": 768, "pooler_num_attention_heads": 12, "pooler_num_fc_layers": 3, "pooler_size_per_head": 128, "pooler_type": "first_token_transform", "position_embedding_type": "absolute", "transformers_version": "4.26.0", "type_vocab_size": 2, "use_cache": true, "vocab_size": 29794 } [INFO|configuration_utils.py:660] 2024-05-03 16:57:26,282 >> loading configuration file config.json from cache at /home/ext_maiara_frodrigues2000_gmail_/.cache/huggingface/hub/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/config.json [INFO|configuration_utils.py:712] 2024-05-03 16:57:26,283 >> Model config BertConfig { "_name_or_path": "neuralmind/bert-base-portuguese-cased", "architectures": [ "BertForMaskedLM" ], "attention_probs_dropout_prob": 0.1, "classifier_dropout": null, "directionality": "bidi", "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 12, "num_hidden_layers": 12, "output_past": true, "pad_token_id": 0, "pooler_fc_size": 768, "pooler_num_attention_heads": 12, "pooler_num_fc_layers": 3, "pooler_size_per_head": 128, "pooler_type": "first_token_transform", "position_embedding_type": "absolute", "transformers_version": "4.26.0", "type_vocab_size": 2, "use_cache": true, "vocab_size": 29794 } [INFO|modeling_utils.py:2275] 2024-05-03 16:57:30,436 >> loading weights file pytorch_model.bin from cache at /home/ext_maiara_frodrigues2000_gmail_/.cache/huggingface/hub/models--neuralmind--bert-base-portuguese-cased/snapshots/94d69c95f98f7d5b2a8700c420230ae10def0baa/pytorch_model.bin [WARNING|modeling_utils.py:2847] 2024-05-03 16:57:33,159 >> Some weights of the model checkpoint at neuralmind/bert-base-portuguese-cased were not used when initializing BertWithDAForSentenceLabelingTopicSegmentation: ['cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias'] - This IS expected if you are initializing BertWithDAForSentenceLabelingTopicSegmentation 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 BertWithDAForSentenceLabelingTopicSegmentation 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:2859] 2024-05-03 16:57:33,160 >> Some weights of BertWithDAForSentenceLabelingTopicSegmentation were not initialized from the model checkpoint at neuralmind/bert-base-portuguese-cased and are newly initialized: ['loss_calculator.classifier.weight', 'loss_calculator.tssp.classifier.weight', 'loss_calculator.tssp.classifier.bias', 'loss_calculator.classifier.bias'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. [ERROR|tokenization_utils_base.py:997] 2024-05-03 16:57:33,171 >> Using bos_token, but it is not set yet. [INFO|tokenization_utils_base.py:907] 2024-05-03 16:57:33,171 >> Assigning [BOS] to the bos_token key of the tokenizer Process #0 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8/cache-bert_wiki_section_disease_512_train_00000_of_00005.arrow labels_are_int: False label_to_id: {'B-EOP': 0, 'O': 1} config: BertConfig { "_name_or_path": "neuralmind/bert-base-portuguese-cased", "architectures": [ "BertForMaskedLM" ], "attention_probs_dropout_prob": 0.1, "cache_dir": null, "cl_anchor_level": "eop_list", "cl_loss_weight": 0.5, "cl_negative_k": 3, "cl_positive_k": 1, "cl_temp": 0.1, "classifier_dropout": null, "config_name": null, "directionality": "bidi", "do_cssl": true, "do_da_ts": true, "do_tssp": true, "finetuning_task": "topic_segment", "focal_loss_gamma": 0.0, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "ignore_mismatched_sizes": false, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_name_or_path": "neuralmind/bert-base-portuguese-cased", "model_revision": "main", "model_type": "bert", "num_attention_heads": 12, "num_gpu": 1, "num_hidden_layers": 12, "num_topic_labels": 0, "num_tssp_labels": 3, "output_past": true, "pad_token_id": 0, "pooler_fc_size": 768, "pooler_num_attention_heads": 12, "pooler_num_fc_layers": 3, "pooler_size_per_head": 128, "pooler_type": "first_token_transform", "position_embedding_type": "absolute", "sentence_pooler_type": null, "tokenizer_name": null, "transformers_version": "4.26.0", "ts_loss_weight": 1.0, "ts_score_predictor": "lt", "ts_score_predictor_cos_temp": 1, "tssp_ablation": "none", "tssp_loss_weight": 1.0, "type_vocab_size": 2, "use_auth_token": false, "use_cache": true, "vocab_size": 29794, "weight_label_zero": 0.5 } model_type: bert final max_seq_length: 512 label_list: ['B-EOP', 'O'] 05/03/2024 16:57:33 - INFO - datasets.arrow_dataset - Process #0 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8/cache-bert_wiki_section_disease_512_train_00000_of_00005.arrow Process #1 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8/cache-bert_wiki_section_disease_512_train_00001_of_00005.arrow 05/03/2024 16:57:33 - INFO - datasets.arrow_dataset - Process #1 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8/cache-bert_wiki_section_disease_512_train_00001_of_00005.arrow Process #2 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8/cache-bert_wiki_section_disease_512_train_00002_of_00005.arrow 05/03/2024 16:57:33 - INFO - datasets.arrow_dataset - Process #2 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8/cache-bert_wiki_section_disease_512_train_00002_of_00005.arrow Process #3 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8/cache-bert_wiki_section_disease_512_train_00003_of_00005.arrow 05/03/2024 16:57:33 - INFO - datasets.arrow_dataset - Process #3 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8/cache-bert_wiki_section_disease_512_train_00003_of_00005.arrow Process #4 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8/cache-bert_wiki_section_disease_512_train_00004_of_00005.arrow 05/03/2024 16:57:33 - INFO - datasets.arrow_dataset - Process #4 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_disease_neuralmind/bert-base-portuguese-cased_512/wiki_section_disease/WikiSectionDiseaseForDocumentSegmentation-cd3e6789d682cabd/1.1.1/2a8de58e5edfabadae5522178d4ba307ba7ac6f5cf7cff700081c4a0fd13e1b8/cache-bert_wiki_section_disease_512_train_00004_of_00005.arrow Spawning 5 processes 05/03/2024 16:57:33 - INFO - datasets.arrow_dataset - Spawning 5 processes Running tokenizer on train dataset (num_proc=5): 0%| | 0/2513 [00:00> The following columns in the training set don't have a corresponding argument in `BertWithDAForSentenceLabelingTopicSegmentation.forward` and have been ignored: sentences, example_id. If sentences, example_id are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. /home/ext_maiara_frodrigues2000_gmail_/.pyenv/versions/spokennlp/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning warnings.warn( [INFO|trainer.py:1650] 2024-05-03 16:58:47,925 >> ***** Running training ***** [INFO|trainer.py:1651] 2024-05-03 16:58:47,925 >> Num examples = 10739 [INFO|trainer.py:1652] 2024-05-03 16:58:47,925 >> Num Epochs = 5 [INFO|trainer.py:1653] 2024-05-03 16:58:47,925 >> Instantaneous batch size per device = 2 [INFO|trainer.py:1654] 2024-05-03 16:58:47,925 >> Total train batch size (w. parallel, distributed & accumulation) = 4 [INFO|trainer.py:1655] 2024-05-03 16:58:47,925 >> Gradient Accumulation steps = 2 [INFO|trainer.py:1656] 2024-05-03 16:58:47,925 >> Total optimization steps = 13425 [INFO|trainer.py:1657] 2024-05-03 16:58:47,926 >> Number of trainable parameters = 108927749 0%| | 0/13425 [00:00> The following columns in the evaluation set don't have a corresponding argument in `BertWithDAForSentenceLabelingTopicSegmentation.forward` and have been ignored: sentences, example_id. If sentences, example_id are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-05-03 17:39:05,183 >> ***** Running Evaluation ***** [INFO|trainer.py:2966] 2024-05-03 17:39:05,183 >> Num examples = 1523 [INFO|trainer.py:2969] 2024-05-03 17:39:05,183 >> Batch size = 4 {'loss': 0.9152, 'learning_rate': 4.000372439478585e-05, 'epoch': 1.0} 0%| | 0/381 [00:00> Saving model checkpoint to ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-2684 [INFO|configuration_utils.py:453] 2024-05-03 17:41:00,526 >> Configuration saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-2684/config.json [INFO|modeling_utils.py:1704] 2024-05-03 17:41:01,451 >> Model weights saved in 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If sentences, example_id are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-05-03 18:21:08,117 >> ***** Running Evaluation ***** [INFO|trainer.py:2966] 2024-05-03 18:21:08,117 >> Num examples = 1523 [INFO|trainer.py:2969] 2024-05-03 18:21:08,117 >> Batch size = 4 {'eval_loss': nan, 'eval_EOP_precision': 0.882909520993495, 'eval_EOP_recall': 0.6103843008994276, 'eval_EOP_f1': 0.7217790669567319, 'eval_EOP_number': 2446, 'eval_overall_precision': 0.882909520993495, 'eval_overall_recall': 0.6103843008994276, 'eval_overall_f1': 0.7217790669567319, 'eval_overall_accuracy': 0.9441993503660251, 'eval_da_EOP_precision': 0.9193099610461881, 'eval_da_EOP_recall': 0.6109467455621301, 'eval_da_EOP_f1': 0.7340590979782271, 'eval_da_EOP_number': 2704, 'eval_da_overall_precision': 0.9193099610461881, 'eval_da_overall_recall': 0.6109467455621301, 'eval_da_overall_f1': 0.7340590979782271, 'eval_da_overall_accuracy': 0.9443618109138235, 'eval_runtime': 115.3406, 'eval_samples_per_second': 13.204, 'eval_steps_per_second': 3.303, 'epoch': 1.0} {'loss': 0.6588, 'learning_rate': 3.0007448789571697e-05, 'epoch': 2.0} 0%| | 0/381 [00:00> Saving model checkpoint to ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-5368 [INFO|configuration_utils.py:453] 2024-05-03 18:23:03,198 >> Configuration saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-5368/config.json [INFO|modeling_utils.py:1704] 2024-05-03 18:23:04,087 >> Model weights saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-5368/pytorch_model.bin [INFO|tokenization_utils_base.py:2160] 2024-05-03 18:23:04,088 >> tokenizer config file saved in 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If sentences, example_id are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-05-03 19:03:11,659 >> ***** Running Evaluation ***** [INFO|trainer.py:2966] 2024-05-03 19:03:11,659 >> Num examples = 1523 [INFO|trainer.py:2969] 2024-05-03 19:03:11,659 >> Batch size = 4 {'eval_loss': nan, 'eval_EOP_precision': 0.8140900195694716, 'eval_EOP_recall': 0.6802943581357318, 'eval_EOP_f1': 0.7412026726057906, 'eval_EOP_number': 2446, 'eval_overall_precision': 0.8140900195694716, 'eval_overall_recall': 0.6802943581357318, 'eval_overall_f1': 0.7412026726057906, 'eval_overall_accuracy': 0.9436660687448489, 'eval_da_EOP_precision': 0.8529544429409112, 'eval_da_EOP_recall': 0.6993343195266272, 'eval_da_EOP_f1': 0.7685429790692948, 'eval_da_EOP_number': 2704, 'eval_da_overall_precision': 0.8529544429409112, 'eval_da_overall_recall': 0.6993343195266272, 'eval_da_overall_f1': 0.7685429790692948, 'eval_da_overall_accuracy': 0.94705772985033, 'eval_runtime': 115.0795, 'eval_samples_per_second': 13.234, 'eval_steps_per_second': 3.311, 'epoch': 2.0} {'loss': 0.4949, 'learning_rate': 2.0011173184357543e-05, 'epoch': 3.0} 0%| | 0/381 [00:00> Saving model checkpoint to ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-8052 [INFO|configuration_utils.py:453] 2024-05-03 19:05:06,558 >> Configuration saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-8052/config.json [INFO|modeling_utils.py:1704] 2024-05-03 19:05:07,417 >> Model weights saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-8052/pytorch_model.bin [INFO|tokenization_utils_base.py:2160] 2024-05-03 19:05:07,417 >> tokenizer config file saved in 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If sentences, example_id are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-05-03 19:45:14,737 >> ***** Running Evaluation ***** [INFO|trainer.py:2966] 2024-05-03 19:45:14,737 >> Num examples = 1523 [INFO|trainer.py:2969] 2024-05-03 19:45:14,737 >> Batch size = 4 {'eval_loss': nan, 'eval_EOP_precision': 0.8115804461319411, 'eval_EOP_recall': 0.6991005723630417, 'eval_EOP_f1': 0.7511530858774434, 'eval_EOP_number': 2446, 'eval_overall_precision': 0.8115804461319411, 'eval_overall_recall': 0.6991005723630417, 'eval_overall_f1': 0.7511530858774434, 'eval_overall_accuracy': 0.9450719930188588, 'eval_da_EOP_precision': 0.8529937444146559, 'eval_da_EOP_recall': 0.705991124260355, 'eval_da_EOP_f1': 0.7725617159044921, 'eval_da_EOP_number': 2704, 'eval_da_overall_precision': 0.8529937444146559, 'eval_da_overall_recall': 0.705991124260355, 'eval_da_overall_f1': 0.7725617159044921, 'eval_da_overall_accuracy': 0.9477549502649437, 'eval_runtime': 114.897, 'eval_samples_per_second': 13.255, 'eval_steps_per_second': 3.316, 'epoch': 3.0} {'loss': 0.3741, 'learning_rate': 1.001489757914339e-05, 'epoch': 4.0} 0%| | 0/381 [00:00> Saving model checkpoint to ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-10736 [INFO|configuration_utils.py:453] 2024-05-03 19:47:09,612 >> Configuration saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-10736/config.json [INFO|modeling_utils.py:1704] 2024-05-03 19:47:10,487 >> Model weights saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-10736/pytorch_model.bin [INFO|tokenization_utils_base.py:2160] 2024-05-03 19:47:10,488 >> tokenizer config file saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-10736/tokenizer_config.json [INFO|tokenization_utils_base.py:2167] 2024-05-03 19:47:10,488 >> Special tokens file saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-10736/special_tokens_map.json [INFO|trainer.py:2787] 2024-05-03 19:47:12,261 >> Deleting older checkpoint [output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-5368] due to args.save_total_limit 80%|███████▉ | 10737/13425 [2:48:25<27:03:10, 36.23s/it] 80%|███████▉ | 10738/13425 [2:48:26<19:07:48, 25.63s/it] 80%|███████▉ | 10739/13425 [2:48:27<13:34:58, 18.21s/it] 80%|████████ | 10740/13425 [2:48:27<9:39:30, 12.95s/it] 80%|████████ | 10741/13425 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`BertWithDAForSentenceLabelingTopicSegmentation.forward` and have been ignored: sentences, example_id. If sentences, example_id are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-05-03 20:27:17,569 >> ***** Running Evaluation ***** [INFO|trainer.py:2966] 2024-05-03 20:27:17,569 >> Num examples = 1523 [INFO|trainer.py:2969] 2024-05-03 20:27:17,569 >> Batch size = 4 {'eval_loss': nan, 'eval_EOP_precision': 0.799265043638034, 'eval_EOP_recall': 0.7113654946852003, 'eval_EOP_f1': 0.752757949383517, 'eval_EOP_number': 2446, 'eval_overall_precision': 0.799265043638034, 'eval_overall_recall': 0.7113654946852003, 'eval_overall_f1': 0.752757949383517, 'eval_overall_accuracy': 0.9445871915450623, 'eval_da_EOP_precision': 0.8338342636324603, 'eval_da_EOP_recall': 0.7181952662721893, 'eval_da_EOP_f1': 0.7717067355454003, 'eval_da_EOP_number': 2704, 'eval_da_overall_precision': 0.8338342636324603, 'eval_da_overall_recall': 0.7181952662721893, 'eval_da_overall_f1': 0.7717067355454003, 'eval_da_overall_accuracy': 0.9465929162405875, 'eval_runtime': 114.8732, 'eval_samples_per_second': 13.258, 'eval_steps_per_second': 3.317, 'epoch': 4.0} {'loss': 0.2844, 'learning_rate': 1.86219739292365e-08, 'epoch': 5.0} 0%| | 0/381 [00:00> Saving model checkpoint to ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-13420 [INFO|configuration_utils.py:453] 2024-05-03 20:29:12,368 >> Configuration saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-13420/config.json [INFO|modeling_utils.py:1704] 2024-05-03 20:29:13,233 >> Model weights saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-13420/pytorch_model.bin [INFO|tokenization_utils_base.py:2160] 2024-05-03 20:29:13,233 >> tokenizer config file saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-13420/tokenizer_config.json [INFO|tokenization_utils_base.py:2167] 2024-05-03 20:29:13,234 >> Special tokens file saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-13420/special_tokens_map.json [INFO|trainer.py:2787] 2024-05-03 20:29:15,006 >> Deleting older checkpoint [output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-8052] due to args.save_total_limit 100%|█████████▉| 13421/13425 [3:30:28<02:24, 36.20s/it] 100%|█████████▉| 13422/13425 [3:30:29<01:16, 25.60s/it] 100%|█████████▉| 13423/13425 [3:30:29<00:36, 18.19s/it] 100%|█████████▉| 13424/13425 [3:30:30<00:13, 13.00s/it] 100%|██████████| 13425/13425 [3:30:31<00:00, 9.31s/it][INFO|trainer.py:1901] 2024-05-03 20:29:19,505 >> Training completed. Do not forget to share your model on huggingface.co/models =) [INFO|trainer.py:2025] 2024-05-03 20:29:19,505 >> Loading best model from ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/checkpoint-13420 (score: 0.7546452702702704). 100%|██████████| 13425/13425 [3:30:31<00:00, 9.31s/it] 100%|██████████| 13425/13425 [3:30:31<00:00, 1.06it/s] [INFO|trainer.py:2709] 2024-05-03 20:29:19,901 >> Saving model checkpoint to ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29 [INFO|configuration_utils.py:453] 2024-05-03 20:29:19,902 >> Configuration saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/config.json [INFO|modeling_utils.py:1704] 2024-05-03 20:29:20,678 >> Model weights saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/pytorch_model.bin [INFO|tokenization_utils_base.py:2160] 2024-05-03 20:29:20,679 >> tokenizer config file saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/tokenizer_config.json [INFO|tokenization_utils_base.py:2167] 2024-05-03 20:29:20,679 >> Special tokens file saved in ./output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/special_tokens_map.json {'eval_loss': nan, 'eval_EOP_precision': 0.780349344978166, 'eval_EOP_recall': 0.7305805396565822, 'eval_EOP_f1': 0.7546452702702704, 'eval_EOP_number': 2446, 'eval_overall_precision': 0.780349344978166, 'eval_overall_recall': 0.7305805396565822, 'eval_overall_f1': 0.7546452702702704, 'eval_overall_accuracy': 0.9436660687448489, 'eval_da_EOP_precision': 0.8069246435845214, 'eval_da_EOP_recall': 0.7326183431952663, 'eval_da_EOP_f1': 0.7679782903663502, 'eval_da_EOP_number': 2704, 'eval_da_overall_precision': 0.8069246435845214, 'eval_da_overall_recall': 0.7326183431952663, 'eval_da_overall_f1': 0.7679782903663502, 'eval_da_overall_accuracy': 0.9443618109138235, 'eval_runtime': 114.7975, 'eval_samples_per_second': 13.267, 'eval_steps_per_second': 3.319, 'epoch': 5.0} {'train_runtime': 12631.9735, 'train_samples_per_second': 4.251, 'train_steps_per_second': 1.063, 'train_loss': 0.545391144610428, 'epoch': 5.0} ***** train metrics ***** epoch = 5.0 train_loss = 0.5454 train_runtime = 3:30:31.97 train_samples = 10739 train_samples_per_second = 4.251 train_steps_per_second = 1.063 05/03/2024 20:29:20 - INFO - __main__ - *** Evaluate *** [INFO|trainer.py:710] 2024-05-03 20:29:20,726 >> The following columns in the evaluation set don't have a corresponding argument in `BertWithDAForSentenceLabelingTopicSegmentation.forward` and have been ignored: sentences, example_id. If sentences, example_id are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-05-03 20:29:20,733 >> ***** Running Evaluation ***** [INFO|trainer.py:2966] 2024-05-03 20:29:20,733 >> Num examples = 1523 [INFO|trainer.py:2969] 2024-05-03 20:29:20,733 >> Batch size = 4 0%| | 0/381 [00:00> The following columns in the test set don't have a corresponding argument in `BertWithDAForSentenceLabelingTopicSegmentation.forward` and have been ignored: sentences, example_id. If sentences, example_id are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-05-03 20:31:16,992 >> ***** Running Prediction ***** [INFO|trainer.py:2966] 2024-05-03 20:31:16,992 >> Num examples = 2918 [INFO|trainer.py:2969] 2024-05-03 20:31:16,992 >> Batch size = 4 num predict samples: 2918 num predict examples: 718 0%| | 0/730 [00:00 get_wiki_section_sent_level_metric(data_file, pred_file) File "./src/postprocess_predictions.py", line 33, in get_wiki_section_sent_level_metric total_para_level_predictions, total_para_level_labels, total_sent_level_labels = read_total_pred_and_labels(data_file, pred_file) File "./src/postprocess_predictions.py", line 11, in read_total_pred_and_labels with open(pred_file, "r") as f: FileNotFoundError: [Errno 2] No such file or directory: './output/neuralmind/bert-base-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-03_16:56:29/predict_wiki_section_max_seq512_ts_score_lt.txt' 0 Run postprocess_predictions to get sent level metric Save sent level metric