07/03/2024 20:57:49 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: False 07/03/2024 20:57:49 - 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-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/runs/Jul03_20-57-49_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-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35, 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-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35, 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_city contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at ./src/datasets/wiki_section_city/wiki_section_city.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_city/WikiSectionCityForDocumentSegmentation 07/03/2024 20:57:50 - INFO - datasets.builder - No config specified, defaulting to the single config: wiki_section_city/WikiSectionCityForDocumentSegmentation Using custom data configuration WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad 07/03/2024 20:57:50 - INFO - datasets.builder - Using custom data configuration WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad Loading Dataset Infos from /home/ext_luti4497_gmail_com/.cache/huggingface/modules/datasets_modules/datasets/wiki_section_city/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4 07/03/2024 20:57:50 - INFO - datasets.info - Loading Dataset Infos from /home/ext_luti4497_gmail_com/.cache/huggingface/modules/datasets_modules/datasets/wiki_section_city/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4 Generating dataset wiki_section_city (/projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4) 07/03/2024 20:57:50 - INFO - datasets.builder - Generating dataset wiki_section_city (/projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4) Downloading and preparing dataset wiki_section_city/WikiSectionCityForDocumentSegmentation to /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4... 07/03/2024 20:57:50 - INFO - datasets.builder - Downloading and preparing dataset wiki_section_city/WikiSectionCityForDocumentSegmentation to /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4... Downloading took 0.0 min 07/03/2024 20:57:50 - INFO - datasets.download.download_manager - Downloading took 0.0 min Checksum Computation took 0.0 min 07/03/2024 20:57:50 - INFO - datasets.download.download_manager - Checksum Computation took 0.0 min Generating train split 07/03/2024 20:57:50 - INFO - datasets.builder - Generating train split Generating train split: 0 examples [00:00, ? examples/s] Generating train split: 1 examples [00:01, 2.00s/ examples] Generating train split: 162 examples [00:02, 107.97 examples/s] Generating train split: 321 examples [00:02, 236.58 examples/s] Generating train split: 477 examples [00:02, 381.74 examples/s] Generating train split: 668 examples [00:02, 587.07 examples/s] Generating train split: 862 examples [00:02, 805.60 examples/s] Generating train split: 1087 examples [00:02, 781.16 examples/s] Generating train split: 1258 examples [00:02, 928.94 examples/s] Generating train split: 1464 examples [00:03, 1139.96 examples/s] Generating train split: 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topic_segmentation 07/03/2024 20:58:01 - INFO - datasets.builder - Generating validation split Generating validation split: 0 examples [00:00, ? examples/s] Generating validation split: 1 examples [00:00, 2.08 examples/s] Generating validation split: 138 examples [00:00, 315.75 examples/s] Generating validation split: 286 examples [00:00, 602.46 examples/s] Generating validation split: 451 examples [00:00, 873.28 examples/s] Generating validation split: 618 examples [00:00, 1084.20 examples/s] Generating validation split: 804 examples [00:00, 1298.82 examples/s] Generating validation split: 969 examples [00:01, 1397.34 examples/s] Generating validation split: 1179 examples [00:01, 1233.93 examples/s] Generating validation split: 1372 examples [00:01, 1396.95 examples/s] Generating validation split: 1546 examples [00:01, 1481.53 examples/s] Generating validation split: 1710 examples [00:01, 1521.31 examples/s] Generating validation split: 1908 examples [00:01, 1639.01 examples/s] Generating validation split: 1952 examples [00:01, 1102.42 examples/s] Generating test split topic_segmentation 07/03/2024 20:58:02 - INFO - datasets.builder - Generating test split Generating test split: 0 examples [00:00, ? examples/s] Generating test split: 1 examples [00:00, 1.58 examples/s] Generating test split: 159 examples [00:00, 292.19 examples/s] Generating test split: 302 examples [00:00, 527.82 examples/s] Generating test split: 477 examples [00:00, 807.16 examples/s] Generating test split: 674 examples [00:01, 1091.71 examples/s] Generating test split: 835 examples [00:01, 1223.25 examples/s] Generating test split: 1000 examples [00:01, 1122.83 examples/s] Generating test split: 1146 examples [00:01, 1200.70 examples/s] Generating test split: 1318 examples [00:01, 1327.61 examples/s] Generating test split: 1497 examples [00:01, 1445.80 examples/s] Generating test split: 1683 examples [00:01, 1549.87 examples/s] Generating test split: 1880 examples [00:01, 1660.85 examples/s] Generating test split: 2084 examples [00:02, 1413.91 examples/s] Generating test split: 2265 examples [00:02, 1508.16 examples/s] Generating test split: 2436 examples [00:02, 1550.00 examples/s] Generating test split: 2633 examples [00:02, 1662.05 examples/s] Generating test split: 2861 examples [00:02, 1592.09 examples/s] Generating test split: 3070 examples [00:02, 1409.73 examples/s] Generating test split: 3263 examples [00:02, 1528.87 examples/s] Generating test split: 3459 examples [00:02, 1632.75 examples/s] Generating test split: 3713 examples [00:02, 1652.23 examples/s] Generating test split: 3906 examples [00:03, 1668.19 examples/s] Generating test split: 3906 examples [00:03, 1230.47 examples/s] Dataset wiki_section_city downloaded and prepared to /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4. Subsequent calls will reuse this data. topic_segmentation 07/03/2024 20:58:06 - INFO - datasets.builder - Dataset wiki_section_city downloaded and prepared to /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4. Subsequent calls will reuse this data. [INFO|configuration_utils.py:660] 2024-07-03 20:58:07,402 >> loading configuration file config.json from cache at /home/ext_luti4497_gmail_com/.cache/huggingface/hub/models--neuralmind--bert-large-portuguese-cased/snapshots/aa302f6ea73b759f7df9cad58bd272127b67ec28/config.json [INFO|configuration_utils.py:712] 2024-07-03 20:58:07,496 >> Model config BertConfig { "_name_or_path": "neuralmind/bert-large-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": 1024, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 16, "num_hidden_layers": 24, "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] 2024-07-03 20:58:07,620 >> loading configuration file config.json from cache at /home/ext_luti4497_gmail_com/.cache/huggingface/hub/models--neuralmind--bert-large-portuguese-cased/snapshots/aa302f6ea73b759f7df9cad58bd272127b67ec28/config.json [INFO|configuration_utils.py:712] 2024-07-03 20:58:07,622 >> Model config BertConfig { "_name_or_path": "neuralmind/bert-large-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": 1024, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 16, "num_hidden_layers": 24, "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] 2024-07-03 20:58:07,983 >> loading file vocab.txt from cache at /home/ext_luti4497_gmail_com/.cache/huggingface/hub/models--neuralmind--bert-large-portuguese-cased/snapshots/aa302f6ea73b759f7df9cad58bd272127b67ec28/vocab.txt [INFO|tokenization_utils_base.py:1802] 2024-07-03 20:58:07,983 >> loading file tokenizer.json from cache at None [INFO|tokenization_utils_base.py:1802] 2024-07-03 20:58:07,983 >> loading file added_tokens.json from cache at /home/ext_luti4497_gmail_com/.cache/huggingface/hub/models--neuralmind--bert-large-portuguese-cased/snapshots/aa302f6ea73b759f7df9cad58bd272127b67ec28/added_tokens.json [INFO|tokenization_utils_base.py:1802] 2024-07-03 20:58:07,983 >> loading file special_tokens_map.json from cache at /home/ext_luti4497_gmail_com/.cache/huggingface/hub/models--neuralmind--bert-large-portuguese-cased/snapshots/aa302f6ea73b759f7df9cad58bd272127b67ec28/special_tokens_map.json [INFO|tokenization_utils_base.py:1802] 2024-07-03 20:58:07,983 >> loading file tokenizer_config.json from cache at /home/ext_luti4497_gmail_com/.cache/huggingface/hub/models--neuralmind--bert-large-portuguese-cased/snapshots/aa302f6ea73b759f7df9cad58bd272127b67ec28/tokenizer_config.json [INFO|configuration_utils.py:660] 2024-07-03 20:58:07,983 >> loading configuration file config.json from cache at /home/ext_luti4497_gmail_com/.cache/huggingface/hub/models--neuralmind--bert-large-portuguese-cased/snapshots/aa302f6ea73b759f7df9cad58bd272127b67ec28/config.json [INFO|configuration_utils.py:712] 2024-07-03 20:58:07,984 >> Model config BertConfig { "_name_or_path": "neuralmind/bert-large-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": 1024, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 16, "num_hidden_layers": 24, "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] 2024-07-03 20:58:08,012 >> loading configuration file config.json from cache at /home/ext_luti4497_gmail_com/.cache/huggingface/hub/models--neuralmind--bert-large-portuguese-cased/snapshots/aa302f6ea73b759f7df9cad58bd272127b67ec28/config.json [INFO|configuration_utils.py:712] 2024-07-03 20:58:08,013 >> Model config BertConfig { "_name_or_path": "neuralmind/bert-large-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": 1024, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_type": "bert", "num_attention_heads": 16, "num_hidden_layers": 24, "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] 2024-07-03 20:58:14,621 >> loading weights file pytorch_model.bin from cache at /home/ext_luti4497_gmail_com/.cache/huggingface/hub/models--neuralmind--bert-large-portuguese-cased/snapshots/aa302f6ea73b759f7df9cad58bd272127b67ec28/pytorch_model.bin [WARNING|modeling_utils.py:2847] 2024-07-03 20:58:18,535 >> Some weights of the model checkpoint at neuralmind/bert-large-portuguese-cased were not used when initializing BertWithDAForSentenceLabelingTopicSegmentation: ['cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.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] 2024-07-03 20:58:18,535 >> Some weights of BertWithDAForSentenceLabelingTopicSegmentation were not initialized from the model checkpoint at neuralmind/bert-large-portuguese-cased and are newly initialized: ['loss_calculator.tssp.classifier.weight', 'loss_calculator.tssp.classifier.bias', 'loss_calculator.classifier.weight', '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] 2024-07-03 20:58:18,596 >> Using bos_token, but it is not set yet. [INFO|tokenization_utils_base.py:907] 2024-07-03 20:58:18,596 >> Assigning [BOS] to the bos_token key of the tokenizer Process #0 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4/cache-bert_wiki_section_city_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-large-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": 1024, "ignore_mismatched_sizes": false, "initializer_range": 0.02, "intermediate_size": 4096, "layer_norm_eps": 1e-12, "max_position_embeddings": 512, "model_name_or_path": "neuralmind/bert-large-portuguese-cased", "model_revision": "main", "model_type": "bert", "num_attention_heads": 16, "num_gpu": 1, "num_hidden_layers": 24, "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'] 07/03/2024 20:58:19 - INFO - datasets.arrow_dataset - Process #0 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4/cache-bert_wiki_section_city_512_train_00000_of_00005.arrow Process #1 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4/cache-bert_wiki_section_city_512_train_00001_of_00005.arrow 07/03/2024 20:58:19 - INFO - datasets.arrow_dataset - Process #1 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4/cache-bert_wiki_section_city_512_train_00001_of_00005.arrow Process #2 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4/cache-bert_wiki_section_city_512_train_00002_of_00005.arrow 07/03/2024 20:58:19 - INFO - datasets.arrow_dataset - Process #2 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4/cache-bert_wiki_section_city_512_train_00002_of_00005.arrow Process #3 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4/cache-bert_wiki_section_city_512_train_00003_of_00005.arrow 07/03/2024 20:58:19 - INFO - datasets.arrow_dataset - Process #3 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4/cache-bert_wiki_section_city_512_train_00003_of_00005.arrow Process #4 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4/cache-bert_wiki_section_city_512_train_00004_of_00005.arrow 07/03/2024 20:58:19 - INFO - datasets.arrow_dataset - Process #4 will write at /projeto/SpokenNLP/emnlp2023-topic_segmentation/cached_data/wiki_section_city_neuralmind/bert-large-portuguese-cased_512/wiki_section_city/WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad/1.1.1/cafcd4e7f86f04cfd46e90d782f42526ea3b1d6deab48ae6dcf7f4bab4f252d4/cache-bert_wiki_section_city_512_train_00004_of_00005.arrow Spawning 5 processes 07/03/2024 20:58:19 - INFO - datasets.arrow_dataset - Spawning 5 processes Running tokenizer on train dataset (num_proc=5): 0%| | 0/13676 [00:00> The following columns in the training set don't have a corresponding argument in `BertWithDAForSentenceLabelingTopicSegmentation.forward` and have been ignored: example_id, sentences. If example_id, sentences 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] 2024-07-03 21:02:12,714 >> ***** Running training ***** [INFO|trainer.py:1651] 2024-07-03 21:02:12,714 >> Num examples = 52001 [INFO|trainer.py:1652] 2024-07-03 21:02:12,715 >> Num Epochs = 5 [INFO|trainer.py:1653] 2024-07-03 21:02:12,715 >> Instantaneous batch size per device = 2 [INFO|trainer.py:1654] 2024-07-03 21:02:12,715 >> Total train batch size (w. parallel, distributed & accumulation) = 4 [INFO|trainer.py:1655] 2024-07-03 21:02:12,715 >> Gradient Accumulation steps = 2 [INFO|trainer.py:1656] 2024-07-03 21:02:12,715 >> Total optimization steps = 65000 [INFO|trainer.py:1657] 2024-07-03 21:02:12,717 >> Number of trainable parameters = 334402565 0%| | 0/65000 [00:00> The following columns in the evaluation set don't have a corresponding argument in `BertWithDAForSentenceLabelingTopicSegmentation.forward` and have been ignored: example_id, sentences. If example_id, sentences are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-07-04 07:15:45,916 >> ***** Running Evaluation ***** [INFO|trainer.py:2966] 2024-07-04 07:15:45,916 >> Num examples = 7725 [INFO|trainer.py:2969] 2024-07-04 07:15:45,916 >> Batch size = 4 {'loss': 0.5927, 'learning_rate': 4e-05, 'epoch': 1.0} 0%| | 0/1932 [00:00> Saving model checkpoint to ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-13000 [INFO|configuration_utils.py:453] 2024-07-04 07:47:04,331 >> Configuration saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-13000/config.json [INFO|modeling_utils.py:1704] 2024-07-04 07:47:06,582 >> Model weights saved in 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have a corresponding argument in `BertWithDAForSentenceLabelingTopicSegmentation.forward` and have been ignored: example_id, sentences. If example_id, sentences are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-07-04 17:56:33,246 >> ***** Running Evaluation ***** [INFO|trainer.py:2966] 2024-07-04 17:56:33,246 >> Num examples = 7725 [INFO|trainer.py:2969] 2024-07-04 17:56:33,246 >> Batch size = 4 {'eval_loss': nan, 'eval_EOP_precision': 0.8817145957677699, 'eval_EOP_recall': 0.8464276412883063, 'eval_EOP_f1': 0.8637108561810692, 'eval_EOP_number': 11519, 'eval_overall_precision': 0.8817145957677699, 'eval_overall_recall': 0.8464276412883063, 'eval_overall_f1': 0.8637108561810692, 'eval_overall_accuracy': 0.9725439457481931, 'eval_da_EOP_precision': 0.9035557331202557, 'eval_da_EOP_recall': 0.8668455346876198, 'eval_da_EOP_f1': 0.8848200312989045, 'eval_da_EOP_number': 13045, 'eval_da_overall_precision': 0.9035557331202557, 'eval_da_overall_recall': 0.8668455346876198, 'eval_da_overall_f1': 0.8848200312989045, 'eval_da_overall_accuracy': 0.974613248710829, 'eval_runtime': 1878.3511, 'eval_samples_per_second': 4.113, 'eval_steps_per_second': 1.029, 'epoch': 1.0} {'loss': 0.4209, 'learning_rate': 3e-05, 'epoch': 2.0} 0%| | 0/1932 [00:00> Saving model checkpoint to ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-26000 [INFO|configuration_utils.py:453] 2024-07-04 18:27:51,859 >> Configuration saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-26000/config.json [INFO|modeling_utils.py:1704] 2024-07-04 18:27:54,037 >> Model weights saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-26000/pytorch_model.bin [INFO|tokenization_utils_base.py:2160] 2024-07-04 18:27:54,038 >> tokenizer config file saved in 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38995/65000 [31:35:05<20:17:12, 2.81s/it] 60%|█████▉ | 38996/65000 [31:35:08<20:25:55, 2.83s/it] 60%|█████▉ | 38997/65000 [31:35:10<20:31:17, 2.84s/it] 60%|█████▉ | 38998/65000 [31:35:13<20:33:02, 2.85s/it] 60%|█████▉ | 38999/65000 [31:35:16<20:37:26, 2.86s/it] 60%|██████ | 39000/65000 [31:35:19<20:26:03, 2.83s/it] 60%|██████ | 39000/65000 [31:35:19<20:26:03, 2.83s/it][INFO|trainer.py:710] 2024-07-05 04:37:32,194 >> The following columns in the evaluation set don't have a corresponding argument in `BertWithDAForSentenceLabelingTopicSegmentation.forward` and have been ignored: example_id, sentences. If example_id, sentences are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-07-05 04:37:32,201 >> ***** Running Evaluation ***** [INFO|trainer.py:2966] 2024-07-05 04:37:32,201 >> Num examples = 7725 [INFO|trainer.py:2969] 2024-07-05 04:37:32,201 >> Batch size = 4 {'eval_loss': nan, 'eval_EOP_precision': 0.9104463615903976, 'eval_EOP_recall': 0.8428683045403247, 'eval_EOP_f1': 0.8753550015777848, 'eval_EOP_number': 11519, 'eval_overall_precision': 0.9104463615903976, 'eval_overall_recall': 0.8428683045403247, 'eval_overall_f1': 0.8753550015777848, 'eval_overall_accuracy': 0.9753279200499688, 'eval_da_EOP_precision': 0.94299347061778, 'eval_da_EOP_recall': 0.8635492525871982, 'eval_da_EOP_f1': 0.9015245488375816, 'eval_da_EOP_number': 13045, 'eval_da_overall_precision': 0.94299347061778, 'eval_da_overall_recall': 0.8635492525871982, 'eval_da_overall_f1': 0.9015245488375816, 'eval_da_overall_accuracy': 0.9787782625942086, 'eval_runtime': 1878.6106, 'eval_samples_per_second': 4.112, 'eval_steps_per_second': 1.028, 'epoch': 2.0} {'loss': 0.3373, 'learning_rate': 2e-05, 'epoch': 3.0} 0%| | 0/1932 [00:00> Saving model checkpoint to ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-39000 [INFO|configuration_utils.py:453] 2024-07-05 05:08:51,644 >> Configuration saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-39000/config.json [INFO|modeling_utils.py:1704] 2024-07-05 05:08:53,904 >> Model weights saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-39000/pytorch_model.bin [INFO|tokenization_utils_base.py:2160] 2024-07-05 05:08:53,905 >> tokenizer config file saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-39000/tokenizer_config.json [INFO|tokenization_utils_base.py:2167] 2024-07-05 05:08:53,905 >> Special tokens file saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-39000/special_tokens_map.json [INFO|trainer.py:2787] 2024-07-05 05:09:08,754 >> Deleting older checkpoint [output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-13000] due to args.save_total_limit 60%|██████ | 39001/65000 [32:07:00<4131:59:57, 572.15s/it] 60%|██████ | 39002/65000 [32:07:02<2898:23:19, 401.35s/it] 60%|██████ | 39003/65000 [32:07:05<2034:55:18, 281.79s/it] 60%|██████ | 39004/65000 [32:07:08<1430:35:30, 198.11s/it] 60%|██████ | 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If example_id, sentences are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-07-05 15:18:19,339 >> ***** Running Evaluation ***** [INFO|trainer.py:2966] 2024-07-05 15:18:19,340 >> Num examples = 7725 [INFO|trainer.py:2969] 2024-07-05 15:18:19,340 >> Batch size = 4 {'eval_loss': nan, 'eval_EOP_precision': 0.9094867518992032, 'eval_EOP_recall': 0.8522441184130567, 'eval_EOP_f1': 0.879935463631067, 'eval_EOP_number': 11519, 'eval_overall_precision': 0.9094867518992032, 'eval_overall_recall': 0.8522441184130567, 'eval_overall_f1': 0.879935463631067, 'eval_overall_accuracy': 0.9760952975818685, 'eval_da_EOP_precision': 0.938241308793456, 'eval_da_EOP_recall': 0.8792640858566501, 'eval_da_EOP_f1': 0.9077958053027304, 'eval_da_EOP_number': 13045, 'eval_da_overall_precision': 0.938241308793456, 'eval_da_overall_recall': 0.8792640858566501, 'eval_da_overall_f1': 0.9077958053027304, 'eval_da_overall_accuracy': 0.9799079040408396, 'eval_runtime': 1879.4394, 'eval_samples_per_second': 4.11, 'eval_steps_per_second': 1.028, 'epoch': 3.0} {'loss': 0.259, 'learning_rate': 1e-05, 'epoch': 4.0} 0%| | 0/1932 [00:00> Saving model checkpoint to ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-52000 [INFO|configuration_utils.py:453] 2024-07-05 15:49:36,687 >> Configuration saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-52000/config.json [INFO|modeling_utils.py:1704] 2024-07-05 15:49:38,842 >> Model weights saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-52000/pytorch_model.bin [INFO|tokenization_utils_base.py:2160] 2024-07-05 15:49:38,843 >> tokenizer config file saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-52000/tokenizer_config.json [INFO|tokenization_utils_base.py:2167] 2024-07-05 15:49:38,843 >> Special tokens file saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-52000/special_tokens_map.json [INFO|trainer.py:2787] 2024-07-05 15:49:53,747 >> Deleting older checkpoint [output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-26000] due to args.save_total_limit 80%|████████ | 52001/65000 [42:47:44<2063:28:53, 571.47s/it] 80%|████████ | 52002/65000 [42:47:47<1447:15:50, 400.84s/it] 80%|████████ | 52003/65000 [42:47:50<1016:05:12, 281.44s/it] 80%|████████ | 52004/65000 [42:47:53<714:20:26, 197.88s/it] 80%|████████ | 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[52:56:21<00:17, 2.85s/it] 100%|█████████▉| 64995/65000 [52:56:24<00:14, 2.85s/it] 100%|█████████▉| 64996/65000 [52:56:27<00:11, 2.83s/it] 100%|█████████▉| 64997/65000 [52:56:30<00:08, 2.82s/it] 100%|█████████▉| 64998/65000 [52:56:33<00:05, 2.80s/it] 100%|█████████▉| 64999/65000 [52:56:35<00:02, 2.79s/it] 100%|██████████| 65000/65000 [52:56:38<00:00, 2.82s/it] 100%|██████████| 65000/65000 [52:56:38<00:00, 2.82s/it][INFO|trainer.py:710] 2024-07-06 01:58:51,479 >> The following columns in the evaluation set don't have a corresponding argument in `BertWithDAForSentenceLabelingTopicSegmentation.forward` and have been ignored: example_id, sentences. If example_id, sentences are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-07-06 01:58:51,485 >> ***** Running Evaluation ***** [INFO|trainer.py:2966] 2024-07-06 01:58:51,485 >> Num examples = 7725 [INFO|trainer.py:2969] 2024-07-06 01:58:51,485 >> Batch size = 4 {'eval_loss': nan, 'eval_EOP_precision': 0.8974011911207364, 'eval_EOP_recall': 0.8633561941140724, 'eval_EOP_f1': 0.8800495553294101, 'eval_EOP_number': 11519, 'eval_overall_precision': 0.8974011911207364, 'eval_overall_recall': 0.8633561941140724, 'eval_overall_f1': 0.8800495553294101, 'eval_overall_accuracy': 0.9758097617560453, 'eval_da_EOP_precision': 0.9363356552729629, 'eval_da_EOP_recall': 0.8861632809505557, 'eval_da_EOP_f1': 0.9105588594383838, 'eval_da_EOP_number': 13045, 'eval_da_overall_precision': 0.9363356552729629, 'eval_da_overall_recall': 0.8861632809505557, 'eval_da_overall_f1': 0.9105588594383838, 'eval_da_overall_accuracy': 0.9804166738526809, 'eval_runtime': 1877.3417, 'eval_samples_per_second': 4.115, 'eval_steps_per_second': 1.029, 'epoch': 4.0} {'loss': 0.1861, 'learning_rate': 0.0, 'epoch': 5.0} 0%| | 0/1932 [00:00> Saving model checkpoint to ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-65000 [INFO|configuration_utils.py:453] 2024-07-06 02:30:09,161 >> Configuration saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-65000/config.json [INFO|modeling_utils.py:1704] 2024-07-06 02:30:11,315 >> Model weights saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-65000/pytorch_model.bin [INFO|tokenization_utils_base.py:2160] 2024-07-06 02:30:11,316 >> tokenizer config file saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-65000/tokenizer_config.json [INFO|tokenization_utils_base.py:2167] 2024-07-06 02:30:11,316 >> Special tokens file saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-65000/special_tokens_map.json [INFO|trainer.py:2787] 2024-07-06 02:30:26,286 >> Deleting older checkpoint [output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-39000] due to args.save_total_limit [INFO|trainer.py:1901] 2024-07-06 02:30:26,440 >> Training completed. Do not forget to share your model on huggingface.co/models =) [INFO|trainer.py:2025] 2024-07-06 02:30:26,440 >> Loading best model from ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/checkpoint-52000 (score: 0.8800495553294101). 100%|██████████| 65000/65000 [53:28:46<00:00, 2.82s/it] 100%|██████████| 65000/65000 [53:28:46<00:00, 2.96s/it] [INFO|trainer.py:2709] 2024-07-06 02:30:59,545 >> Saving model checkpoint to ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35 [INFO|configuration_utils.py:453] 2024-07-06 02:30:59,546 >> Configuration saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/config.json [INFO|modeling_utils.py:1704] 2024-07-06 02:31:01,685 >> Model weights saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/pytorch_model.bin [INFO|tokenization_utils_base.py:2160] 2024-07-06 02:31:01,686 >> tokenizer config file saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/tokenizer_config.json [INFO|tokenization_utils_base.py:2167] 2024-07-06 02:31:01,686 >> Special tokens file saved in ./output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/special_tokens_map.json {'eval_loss': nan, 'eval_EOP_precision': 0.885175059529059, 'eval_EOP_recall': 0.8713429985241774, 'eval_EOP_f1': 0.8782045673287252, 'eval_EOP_number': 11519, 'eval_overall_precision': 0.885175059529059, 'eval_overall_recall': 0.8713429985241774, 'eval_overall_f1': 0.8782045673287252, 'eval_overall_accuracy': 0.9751583831533863, 'eval_da_EOP_precision': 0.9250654190785822, 'eval_da_EOP_recall': 0.8942889996167114, 'eval_da_EOP_f1': 0.9094169005300903, 'eval_da_EOP_number': 13045, 'eval_da_overall_precision': 0.9250654190785822, 'eval_da_overall_recall': 0.8942889996167114, 'eval_da_overall_f1': 0.9094169005300903, 'eval_da_overall_accuracy': 0.9799596433437386, 'eval_runtime': 1877.6728, 'eval_samples_per_second': 4.114, 'eval_steps_per_second': 1.029, 'epoch': 5.0} {'train_runtime': 192526.8268, 'train_samples_per_second': 1.35, 'train_steps_per_second': 0.338, 'train_loss': 0.3591941669170673, 'epoch': 5.0} ***** train metrics ***** epoch = 5.0 train_loss = 0.3592 train_runtime = 2 days, 5:28:46.82 train_samples = 52001 train_samples_per_second = 1.35 train_steps_per_second = 0.338 07/06/2024 02:31:01 - INFO - __main__ - *** Evaluate *** [INFO|trainer.py:710] 2024-07-06 02:31:01,729 >> The following columns in the evaluation set don't have a corresponding argument in `BertWithDAForSentenceLabelingTopicSegmentation.forward` and have been ignored: example_id, sentences. If example_id, sentences are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-07-06 02:31:01,735 >> ***** Running Evaluation ***** [INFO|trainer.py:2966] 2024-07-06 02:31:01,735 >> Num examples = 7725 [INFO|trainer.py:2969] 2024-07-06 02:31:01,735 >> Batch size = 4 0%| | 0/1932 [00:00> The following columns in the test set don't have a corresponding argument in `BertWithDAForSentenceLabelingTopicSegmentation.forward` and have been ignored: example_id, sentences. If example_id, sentences are not expected by `BertWithDAForSentenceLabelingTopicSegmentation.forward`, you can safely ignore this message. [INFO|trainer.py:2964] 2024-07-06 03:02:23,690 >> ***** Running Prediction ***** [INFO|trainer.py:2966] 2024-07-06 03:02:23,690 >> Num examples = 15307 [INFO|trainer.py:2969] 2024-07-06 03:02:23,690 >> Batch size = 4 num predict samples: 15307 num predict examples: 3906 0%| | 0/3827 [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-large-portuguese-cased-finetune-wiki_section_city/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-07-03_20:57:35/predict_wiki_section_max_seq512_ts_score_lt.txt' 0 Run postprocess_predictions to get sent level metric Save sent level metric