06/19/2024 22:00:21 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: False
06/19/2024 22:00:21 - 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=False,
do_predict=True,
do_train=False,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=no,
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=1,
gradient_checkpointing=False,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
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=False,
local_rank=-1,
log_level=passive,
log_level_replica=passive,
log_on_each_node=True,
logging_dir=/projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41/runs/Jun19_22-00-21_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=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_hf,
optim_args=None,
output_dir=/projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41,
overwrite_output_dir=False,
past_index=-1,
per_device_eval_batch_size=4,
per_device_train_batch_size=8,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=/projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41,
save_on_each_node=False,
save_steps=500,
save_strategy=steps,
save_total_limit=None,
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
06/19/2024 22:00:21 - INFO - datasets.builder - No config specified, defaulting to the single config: wiki_section_city/WikiSectionCityForDocumentSegmentation
Using custom data configuration WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad
06/19/2024 22:00:21 - 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
06/19/2024 22:00:21 - 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)
06/19/2024 22:00:21 - 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...
06/19/2024 22:00:21 - 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
06/19/2024 22:00:21 - INFO - datasets.download.download_manager - Downloading took 0.0 min
Checksum Computation took 0.0 min
06/19/2024 22:00:21 - INFO - datasets.download.download_manager - Checksum Computation took 0.0 min
Generating train split
06/19/2024 22:00:21 - INFO - datasets.builder - Generating train split

Generating train split: 0 examples [00:00, ? examples/s]
Generating train split: 1 examples [00:00,  1.88 examples/s]
Generating train split: 174 examples [00:00, 366.96 examples/s]
Generating train split: 332 examples [00:00, 647.83 examples/s]
Generating train split: 497 examples [00:00, 889.10 examples/s]
Generating train split: 684 examples [00:00, 1141.68 examples/s]
Generating train split: 873 examples [00:01, 1341.00 examples/s]
Generating train split: 1086 examples [00:01, 1212.55 examples/s]
Generating train split: 1257 examples [00:01, 1324.98 examples/s]
Generating train split: 1459 examples [00:01, 1496.72 examples/s]
Generating train split: 1642 examples [00:01, 1581.27 examples/s]
Generating train split: 1815 examples [00:01, 1615.68 examples/s]
Generating train split: 2000 examples [00:01, 1338.66 examples/s]
Generating train split: 2170 examples [00:01, 1423.52 examples/s]
Generating train split: 2420 examples [00:02, 1504.76 examples/s]
Generating train split: 2632 examples [00:02, 1471.35 examples/s]
Generating train split: 2843 examples [00:02, 1619.36 examples/s]
Generating train split: 3074 examples [00:02, 1345.54 examples/s]
Generating train split: 3246 examples [00:02, 1419.60 examples/s]
Generating train split: 3444 examples [00:02, 1548.34 examples/s]
Generating train split: 3631 examples [00:02, 1622.73 examples/s]
Generating train split: 3807 examples [00:02, 1647.77 examples/s]
Generating train split: 4000 examples [00:03, 1339.79 examples/s]
Generating train split: 4221 examples [00:03, 1378.81 examples/s]
Generating train split: 4394 examples [00:03, 1450.17 examples/s]
Generating train split: 4581 examples [00:03, 1551.81 examples/s]
Generating train split: 4746 examples [00:03, 1577.15 examples/s]
Generating train split: 4915 examples [00:03, 1601.50 examples/s]
Generating train split: 5083 examples [00:03, 1406.14 examples/s]
Generating train split: 5256 examples [00:03, 1480.60 examples/s]
Generating train split: 5424 examples [00:04, 1525.76 examples/s]
Generating train split: 5615 examples [00:04, 1626.54 examples/s]
Generating train split: 5847 examples [00:04, 1819.32 examples/s]
Generating train split: 6096 examples [00:04, 1628.07 examples/s]
Generating train split: 6318 examples [00:04, 1772.44 examples/s]
Generating train split: 6511 examples [00:04, 1812.82 examples/s]
Generating train split: 6751 examples [00:04, 1732.66 examples/s]
Generating train split: 7000 examples [00:05, 1480.66 examples/s]
Generating train split: 7170 examples [00:05, 1525.90 examples/s]
Generating train split: 7335 examples [00:05, 1552.06 examples/s]
Generating train split: 7520 examples [00:05, 1614.80 examples/s]
Generating train split: 7698 examples [00:05, 1655.31 examples/s]
Generating train split: 7949 examples [00:05, 1657.55 examples/s]
Generating train split: 8177 examples [00:05, 1419.31 examples/s]
Generating train split: 8352 examples [00:05, 1484.50 examples/s]
Generating train split: 8512 examples [00:06, 1501.58 examples/s]
Generating train split: 8729 examples [00:06, 1667.42 examples/s]
Generating train split: 8910 examples [00:06, 1705.12 examples/s]
Generating train split: 9165 examples [00:06, 1438.58 examples/s]
Generating train split: 9330 examples [00:06, 1483.22 examples/s]
Generating train split: 9501 examples [00:06, 1533.76 examples/s]
Generating train split: 9668 examples [00:06, 1565.15 examples/s]
Generating train split: 9880 examples [00:06, 1712.02 examples/s]
Generating train split: 10074 examples [00:07, 1392.32 examples/s]
Generating train split: 10320 examples [00:07, 1464.61 examples/s]
Generating train split: 10484 examples [00:07, 1500.61 examples/s]
Generating train split: 10706 examples [00:07, 1488.11 examples/s]
Generating train split: 10890 examples [00:07, 1561.42 examples/s]
Generating train split: 11088 examples [00:07, 1344.40 examples/s]
Generating train split: 11272 examples [00:07, 1451.97 examples/s]
Generating train split: 11444 examples [00:07, 1510.27 examples/s]
Generating train split: 11637 examples [00:08, 1616.70 examples/s]
Generating train split: 11810 examples [00:08, 1644.38 examples/s]
Generating train split: 11996 examples [00:08, 1703.68 examples/s]
Generating train split: 12185 examples [00:08, 1474.44 examples/s]
Generating train split: 12353 examples [00:08, 1522.07 examples/s]
Generating train split: 12557 examples [00:08, 1656.48 examples/s]
Generating train split: 12739 examples [00:08, 1699.51 examples/s]
Generating train split: 12918 examples [00:08, 1721.07 examples/s]
Generating train split: 13182 examples [00:09, 1494.64 examples/s]
Generating train split: 13355 examples [00:09, 1549.45 examples/s]
Generating train split: 13579 examples [00:09, 1719.46 examples/s]
Generating train split: 13672 examples [00:09, 1464.90 examples/s]
Generating validation split
topic_segmentation
06/19/2024 22:00:31 - INFO - datasets.builder - Generating validation split

Generating validation split: 0 examples [00:00, ? examples/s]
Generating validation split: 69 examples [00:00, 579.82 examples/s]
Generating validation split: 218 examples [00:00, 1069.82 examples/s]
Generating validation split: 373 examples [00:00, 1276.30 examples/s]
Generating validation split: 520 examples [00:00, 1341.50 examples/s]
Generating validation split: 703 examples [00:00, 1510.53 examples/s]
Generating validation split: 881 examples [00:00, 1597.28 examples/s]
Generating validation split: 1071 examples [00:00, 1270.73 examples/s]
Generating validation split: 1274 examples [00:00, 1464.39 examples/s]
Generating validation split: 1467 examples [00:01, 1587.66 examples/s]
Generating validation split: 1713 examples [00:01, 1601.55 examples/s]
Generating validation split: 1906 examples [00:01, 1677.42 examples/s]
Generating validation split: 1950 examples [00:01, 1426.26 examples/s]
Generating test split
topic_segmentation
06/19/2024 22:00:32 - INFO - datasets.builder - Generating test split

Generating test split: 0 examples [00:00, ? examples/s]
Generating test split: 1 examples [00:00,  7.47 examples/s]
Generating test split: 159 examples [00:00, 810.44 examples/s]
Generating test split: 302 examples [00:00, 1066.26 examples/s]
Generating test split: 477 examples [00:00, 1316.18 examples/s]
Generating test split: 663 examples [00:00, 1501.87 examples/s]
Generating test split: 826 examples [00:00, 1538.20 examples/s]
Generating test split: 1000 examples [00:00, 1257.60 examples/s]
Generating test split: 1151 examples [00:00, 1322.06 examples/s]
Generating test split: 1322 examples [00:01, 1425.72 examples/s]
Generating test split: 1495 examples [00:01, 1508.46 examples/s]
Generating test split: 1683 examples [00:01, 1610.97 examples/s]
Generating test split: 1891 examples [00:01, 1745.28 examples/s]
Generating test split: 2077 examples [00:01, 1408.22 examples/s]
Generating test split: 2256 examples [00:01, 1500.50 examples/s]
Generating test split: 2427 examples [00:01, 1543.24 examples/s]
Generating test split: 2619 examples [00:01, 1644.22 examples/s]
Generating test split: 2837 examples [00:01, 1571.16 examples/s]
Generating test split: 3071 examples [00:02, 1378.60 examples/s]
Generating test split: 3265 examples [00:02, 1501.05 examples/s]
Generating test split: 3458 examples [00:02, 1601.68 examples/s]
Generating test split: 3718 examples [00:02, 1630.09 examples/s]
Generating test split: 3897 examples [00:02, 1666.47 examples/s]
Generating test split: 3904 examples [00:02, 1445.18 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
06/19/2024 22:00:35 - 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:658] 2024-06-19 22:00:36,128 >> loading configuration file /projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41/config.json
[INFO|configuration_utils.py:712] 2024-06-19 22:00:36,132 >> Model config BertConfig {
  "_name_or_path": "/projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41",
  "architectures": [
    "BertWithDAForSentenceLabelingTopicSegmentation"
  ],
  "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,
  "id2label": {
    "0": "B-EOP",
    "1": "O"
  },
  "ignore_mismatched_sizes": false,
  "initializer_range": 0.02,
  "intermediate_size": 4096,
  "label2id": {
    "B-EOP": 0,
    "O": 1
  },
  "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,
  "torch_dtype": "float32",
  "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": 29795,
  "weight_label_zero": 0.5
}

[INFO|tokenization_utils_base.py:1800] 2024-06-19 22:00:36,145 >> loading file vocab.txt
[INFO|tokenization_utils_base.py:1800] 2024-06-19 22:00:36,145 >> loading file tokenizer.json
[INFO|tokenization_utils_base.py:1800] 2024-06-19 22:00:36,145 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:1800] 2024-06-19 22:00:36,145 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:1800] 2024-06-19 22:00:36,145 >> loading file tokenizer_config.json
[INFO|modeling_utils.py:2272] 2024-06-19 22:00:36,225 >> loading weights file /projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41/pytorch_model.bin
[INFO|modeling_utils.py:2857] 2024-06-19 22:01:00,370 >> All model checkpoint weights were used when initializing BertWithDAForSentenceLabelingTopicSegmentation.

[INFO|modeling_utils.py:2865] 2024-06-19 22:01:00,370 >> All the weights of BertWithDAForSentenceLabelingTopicSegmentation were initialized from the model checkpoint at /projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41.
If your task is similar to the task the model of the checkpoint was trained on, you can already use BertWithDAForSentenceLabelingTopicSegmentation for predictions without further training.
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_test_00000_of_00005.arrow
labels_are_int:  False
label_to_id:  {'B-EOP': 0, 'O': 1}
config:  BertConfig {
  "_name_or_path": "/projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41",
  "architectures": [
    "BertWithDAForSentenceLabelingTopicSegmentation"
  ],
  "attention_probs_dropout_prob": 0.1,
  "cache_dir": null,
  "cl_anchor_level": "eop_matrix",
  "cl_loss_weight": 0.0,
  "cl_negative_k": 1,
  "cl_positive_k": 1,
  "cl_temp": 1,
  "classifier_dropout": null,
  "config_name": null,
  "directionality": "bidi",
  "do_cssl": false,
  "do_da_ts": false,
  "do_tssp": false,
  "finetuning_task": "topic_segment",
  "focal_loss_gamma": 0.0,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 1024,
  "id2label": {
    "0": "B-EOP",
    "1": "O"
  },
  "ignore_mismatched_sizes": false,
  "initializer_range": 0.02,
  "intermediate_size": 4096,
  "label2id": {
    "B-EOP": 0,
    "O": 1
  },
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_name_or_path": "/projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41",
  "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,
  "torch_dtype": "float32",
  "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": 0.0,
  "type_vocab_size": 2,
  "use_auth_token": false,
  "use_cache": true,
  "vocab_size": 29795,
  "weight_label_zero": 0.5
}

model_type:  bert
final max_seq_length:  512
label_list:  ['B-EOP', 'O']
06/19/2024 22:01:00 - 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_test_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_test_00001_of_00005.arrow
06/19/2024 22:01:00 - 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_test_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_test_00002_of_00005.arrow
06/19/2024 22:01:00 - 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_test_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_test_00003_of_00005.arrow
06/19/2024 22:01:00 - 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_test_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_test_00004_of_00005.arrow
06/19/2024 22:01:00 - 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_test_00004_of_00005.arrow
Spawning 5 processes
06/19/2024 22:01:00 - INFO - datasets.arrow_dataset - Spawning 5 processes

Running tokenizer on prediction dataset (num_proc=5):   0%|          | 0/3904 [00:00<?, ? examples/s]
Running tokenizer on prediction dataset (num_proc=5):   0%|          | 0/3904 [00:08<?, ? examples/s]
0
06/19/2024 22:01:19 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: False
06/19/2024 22:01:19 - 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=False,
do_predict=True,
do_train=False,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=no,
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=1,
gradient_checkpointing=False,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
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=False,
local_rank=-1,
log_level=passive,
log_level_replica=passive,
log_on_each_node=True,
logging_dir=/projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41/runs/Jun19_22-01-19_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=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=3.0,
optim=adamw_hf,
optim_args=None,
output_dir=/projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41,
overwrite_output_dir=False,
past_index=-1,
per_device_eval_batch_size=4,
per_device_train_batch_size=8,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=[],
resume_from_checkpoint=None,
run_name=/projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41,
save_on_each_node=False,
save_steps=500,
save_strategy=steps,
save_total_limit=None,
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
06/19/2024 22:01:19 - INFO - datasets.builder - No config specified, defaulting to the single config: wiki_section_city/WikiSectionCityForDocumentSegmentation
Using custom data configuration WikiSectionCityForDocumentSegmentation-15c5ff72c77600ad
06/19/2024 22:01:19 - 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
06/19/2024 22:01:19 - 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)
06/19/2024 22:01:19 - 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...
06/19/2024 22:01:19 - 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
06/19/2024 22:01:19 - INFO - datasets.download.download_manager - Downloading took 0.0 min
Checksum Computation took 0.0 min
06/19/2024 22:01:19 - INFO - datasets.download.download_manager - Checksum Computation took 0.0 min
Generating train split
06/19/2024 22:01:19 - INFO - datasets.builder - Generating train split

Generating train split: 0 examples [00:00, ? examples/s]
Generating train split: 1 examples [00:00,  1.86 examples/s]
Generating train split: 164 examples [00:00, 344.11 examples/s]
Generating train split: 327 examples [00:00, 640.15 examples/s]
Generating train split: 487 examples [00:00, 873.61 examples/s]
Generating train split: 676 examples [00:00, 1135.01 examples/s]
Generating train split: 865 examples [00:01, 1332.98 examples/s]
Generating train split: 1085 examples [00:01, 1203.80 examples/s]
Generating train split: 1249 examples [00:01, 1302.58 examples/s]
Generating train split: 1444 examples [00:01, 1462.41 examples/s]
Generating train split: 1623 examples [00:01, 1548.04 examples/s]
Generating train split: 1873 examples [00:01, 1581.85 examples/s]
Generating train split: 2088 examples [00:01, 1354.26 examples/s]
Generating train split: 2312 examples [00:02, 1394.05 examples/s]
Generating train split: 2488 examples [00:02, 1474.50 examples/s]
Generating train split: 2715 examples [00:02, 1477.61 examples/s]
Generating train split: 2901 examples [00:02, 1564.50 examples/s]
Generating train split: 3075 examples [00:02, 1305.09 examples/s]
Generating train split: 3244 examples [00:02, 1387.65 examples/s]
Generating train split: 3439 examples [00:02, 1520.39 examples/s]
Generating train split: 3612 examples [00:02, 1573.60 examples/s]
Generating train split: 3783 examples [00:03, 1607.63 examples/s]
Generating train split: 4000 examples [00:03, 1339.28 examples/s]
Generating train split: 4170 examples [00:03, 1418.57 examples/s]
Generating train split: 4362 examples [00:03, 1541.76 examples/s]
Generating train split: 4561 examples [00:03, 1657.36 examples/s]
Generating train split: 4740 examples [00:03, 1670.80 examples/s]
Generating train split: 4920 examples [00:03, 1704.66 examples/s]
Generating train split: 5191 examples [00:03, 1556.63 examples/s]
Generating train split: 5381 examples [00:04, 1638.00 examples/s]
Generating train split: 5553 examples [00:04, 1658.23 examples/s]
Generating train split: 5781 examples [00:04, 1823.54 examples/s]
Generating train split: 5979 examples [00:04, 1854.55 examples/s]
Generating train split: 6222 examples [00:04, 1685.10 examples/s]
Generating train split: 6410 examples [00:04, 1721.16 examples/s]
Generating train split: 6649 examples [00:04, 1673.17 examples/s]
Generating train split: 6887 examples [00:04, 1638.36 examples/s]
Generating train split: 7083 examples [00:05, 1427.36 examples/s]
Generating train split: 7245 examples [00:05, 1469.27 examples/s]
Generating train split: 7409 examples [00:05, 1508.98 examples/s]
Generating train split: 7593 examples [00:05, 1584.40 examples/s]
Generating train split: 7762 examples [00:05, 1609.11 examples/s]
Generating train split: 7937 examples [00:05, 1641.42 examples/s]
Generating train split: 8168 examples [00:05, 1348.83 examples/s]
Generating train split: 8322 examples [00:05, 1391.53 examples/s]
Generating train split: 8551 examples [00:06, 1432.31 examples/s]
Generating train split: 8771 examples [00:06, 1602.43 examples/s]
Generating train split: 8952 examples [00:06, 1646.20 examples/s]
Generating train split: 9160 examples [00:06, 1403.59 examples/s]
Generating train split: 9318 examples [00:06, 1438.63 examples/s]
Generating train split: 9489 examples [00:06, 1503.82 examples/s]
Generating train split: 9651 examples [00:06, 1526.44 examples/s]
Generating train split: 9857 examples [00:06, 1665.31 examples/s]
Generating train split: 10070 examples [00:07, 1373.33 examples/s]
Generating train split: 10315 examples [00:07, 1442.57 examples/s]
Generating train split: 10479 examples [00:07, 1485.39 examples/s]
Generating train split: 10704 examples [00:07, 1483.83 examples/s]
Generating train split: 10894 examples [00:07, 1577.27 examples/s]
Generating train split: 11092 examples [00:07, 1361.95 examples/s]
Generating train split: 11277 examples [00:07, 1468.39 examples/s]
Generating train split: 11445 examples [00:08, 1516.88 examples/s]
Generating train split: 11632 examples [00:08, 1600.38 examples/s]
Generating train split: 11807 examples [00:08, 1633.90 examples/s]
Generating train split: 11993 examples [00:08, 1693.04 examples/s]
Generating train split: 12178 examples [00:08, 1428.25 examples/s]
Generating train split: 12338 examples [00:08, 1445.53 examples/s]
Generating train split: 12543 examples [00:08, 1600.38 examples/s]
Generating train split: 12734 examples [00:08, 1673.56 examples/s]
Generating train split: 12919 examples [00:08, 1718.61 examples/s]
Generating train split: 13173 examples [00:09, 1473.04 examples/s]
Generating train split: 13349 examples [00:09, 1539.35 examples/s]
Generating train split: 13549 examples [00:09, 1642.87 examples/s]
Generating train split: 13672 examples [00:09, 1454.41 examples/s]
Generating validation split
topic_segmentation
06/19/2024 22:01:28 - INFO - datasets.builder - Generating validation split

Generating validation split: 0 examples [00:00, ? examples/s]
Generating validation split: 69 examples [00:00, 608.54 examples/s]
Generating validation split: 217 examples [00:00, 1089.66 examples/s]
Generating validation split: 367 examples [00:00, 1264.36 examples/s]
Generating validation split: 524 examples [00:00, 1378.19 examples/s]
Generating validation split: 703 examples [00:00, 1519.31 examples/s]
Generating validation split: 869 examples [00:00, 1565.22 examples/s]
Generating validation split: 1068 examples [00:00, 1252.69 examples/s]
Generating validation split: 1271 examples [00:00, 1448.10 examples/s]
Generating validation split: 1464 examples [00:01, 1575.39 examples/s]
Generating validation split: 1713 examples [00:01, 1595.15 examples/s]
Generating validation split: 1906 examples [00:01, 1673.40 examples/s]
Generating validation split: 1950 examples [00:01, 1431.59 examples/s]
Generating test split
topic_segmentation
06/19/2024 22:01:30 - INFO - datasets.builder - Generating test split

Generating test split: 0 examples [00:00, ? examples/s]
Generating test split: 1 examples [00:00,  8.19 examples/s]
Generating test split: 155 examples [00:00, 825.12 examples/s]
Generating test split: 295 examples [00:00, 1071.91 examples/s]
Generating test split: 465 examples [00:00, 1308.99 examples/s]
Generating test split: 648 examples [00:00, 1489.93 examples/s]
Generating test split: 814 examples [00:00, 1542.01 examples/s]
Generating test split: 986 examples [00:00, 1591.45 examples/s]
Generating test split: 1152 examples [00:00, 1267.31 examples/s]
Generating test split: 1321 examples [00:01, 1376.15 examples/s]
Generating test split: 1495 examples [00:01, 1471.51 examples/s]
Generating test split: 1682 examples [00:01, 1579.17 examples/s]
Generating test split: 1882 examples [00:01, 1693.42 examples/s]
Generating test split: 2077 examples [00:01, 1402.70 examples/s]
Generating test split: 2257 examples [00:01, 1499.42 examples/s]
Generating test split: 2425 examples [00:01, 1540.54 examples/s]
Generating test split: 2609 examples [00:01, 1616.79 examples/s]
Generating test split: 2835 examples [00:01, 1568.50 examples/s]
Generating test split: 3000 examples [00:02, 1364.80 examples/s]
Generating test split: 3162 examples [00:02, 1425.57 examples/s]
Generating test split: 3367 examples [00:02, 1584.11 examples/s]
Generating test split: 3589 examples [00:02, 1523.09 examples/s]
Generating test split: 3752 examples [00:02, 1546.15 examples/s]
Generating test split: 3904 examples [00:02, 1420.90 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
06/19/2024 22:01:32 - 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:658] 2024-06-19 22:01:33,637 >> loading configuration file /projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41/config.json
[INFO|configuration_utils.py:712] 2024-06-19 22:01:33,641 >> Model config BertConfig {
  "_name_or_path": "/projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41",
  "architectures": [
    "BertWithDAForSentenceLabelingTopicSegmentation"
  ],
  "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,
  "id2label": {
    "0": "B-EOP",
    "1": "O"
  },
  "ignore_mismatched_sizes": false,
  "initializer_range": 0.02,
  "intermediate_size": 4096,
  "label2id": {
    "B-EOP": 0,
    "O": 1
  },
  "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,
  "torch_dtype": "float32",
  "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": 29795,
  "weight_label_zero": 0.5
}

[INFO|tokenization_utils_base.py:1800] 2024-06-19 22:01:33,643 >> loading file vocab.txt
[INFO|tokenization_utils_base.py:1800] 2024-06-19 22:01:33,643 >> loading file tokenizer.json
[INFO|tokenization_utils_base.py:1800] 2024-06-19 22:01:33,643 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:1800] 2024-06-19 22:01:33,643 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:1800] 2024-06-19 22:01:33,643 >> loading file tokenizer_config.json
[INFO|modeling_utils.py:2272] 2024-06-19 22:01:33,668 >> loading weights file /projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41/pytorch_model.bin
[INFO|modeling_utils.py:2857] 2024-06-19 22:01:37,435 >> All model checkpoint weights were used when initializing BertWithDAForSentenceLabelingTopicSegmentation.

[INFO|modeling_utils.py:2865] 2024-06-19 22:01:37,436 >> All the weights of BertWithDAForSentenceLabelingTopicSegmentation were initialized from the model checkpoint at /projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41.
If your task is similar to the task the model of the checkpoint was trained on, you can already use BertWithDAForSentenceLabelingTopicSegmentation for predictions without further training.
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_test_00000_of_00005.arrow
labels_are_int:  False
label_to_id:  {'B-EOP': 0, 'O': 1}
config:  BertConfig {
  "_name_or_path": "/projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41",
  "architectures": [
    "BertWithDAForSentenceLabelingTopicSegmentation"
  ],
  "attention_probs_dropout_prob": 0.1,
  "cache_dir": null,
  "cl_anchor_level": "eop_matrix",
  "cl_loss_weight": 0.0,
  "cl_negative_k": 1,
  "cl_positive_k": 1,
  "cl_temp": 1,
  "classifier_dropout": null,
  "config_name": null,
  "directionality": "bidi",
  "do_cssl": false,
  "do_da_ts": false,
  "do_tssp": false,
  "finetuning_task": "topic_segment",
  "focal_loss_gamma": 0.0,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 1024,
  "id2label": {
    "0": "B-EOP",
    "1": "O"
  },
  "ignore_mismatched_sizes": false,
  "initializer_range": 0.02,
  "intermediate_size": 4096,
  "label2id": {
    "B-EOP": 0,
    "O": 1
  },
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_name_or_path": "/projeto/SpokenNLP/emnlp2023-topic_segmentation/output/neuralmind/bert-large-portuguese-cased-finetune-wiki_section_disease/seed42-seq512-lr5e-05-epoch5-bs4-ts1.0-tssp1.0-cl0.5-2024-05-30_21:01:41",
  "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,
  "torch_dtype": "float32",
  "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": 0.0,
  "type_vocab_size": 2,
  "use_auth_token": false,
  "use_cache": true,
  "vocab_size": 29795,
  "weight_label_zero": 0.5
}

model_type:  bert
final max_seq_length:  512
label_list:  ['B-EOP', 'O']
06/19/2024 22:01:37 - 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_test_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_test_00001_of_00005.arrow
06/19/2024 22:01:37 - 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_test_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_test_00002_of_00005.arrow
06/19/2024 22:01:37 - 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_test_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_test_00003_of_00005.arrow
06/19/2024 22:01:37 - 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_test_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_test_00004_of_00005.arrow
06/19/2024 22:01:37 - 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_test_00004_of_00005.arrow
Spawning 5 processes
06/19/2024 22:01:37 - INFO - datasets.arrow_dataset - Spawning 5 processes

Running tokenizer on prediction dataset (num_proc=5):   0%|          | 0/3904 [00:00<?, ? examples/s]Caching processed dataset 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_test_00004_of_00005.arrow
06/19/2024 22:01:58 - INFO - datasets.arrow_dataset - Caching processed dataset 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_test_00004_of_00005.arrow
Caching processed dataset 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_test_00002_of_00005.arrow
06/19/2024 22:01:59 - INFO - datasets.arrow_dataset - Caching processed dataset 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_test_00002_of_00005.arrow
Caching processed dataset 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_test_00001_of_00005.arrow
06/19/2024 22:02:00 - INFO - datasets.arrow_dataset - Caching processed dataset 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_test_00001_of_00005.arrow
Caching processed dataset 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_test_00003_of_00005.arrow
06/19/2024 22:02:00 - INFO - datasets.arrow_dataset - Caching processed dataset 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_test_00003_of_00005.arrow
Caching processed dataset 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_test_00000_of_00005.arrow
06/19/2024 22:02:01 - INFO - datasets.arrow_dataset - Caching processed dataset 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_test_00000_of_00005.arrow

Running tokenizer on prediction dataset (num_proc=5):  20%|█▉        | 780/3904 [00:31<02:05, 24.91 examples/s]
Running tokenizer on prediction dataset (num_proc=5):  60%|█████▉    | 2342/3904 [00:32<00:16, 92.11 examples/s]
Running tokenizer on prediction dataset (num_proc=5):  80%|███████▉  | 3123/3904 [00:32<00:05, 136.98 examples/s]
Running tokenizer on prediction dataset (num_proc=5): 100%|██████████| 3904/3904 [00:33<00:00, 193.46 examples/s]
Running tokenizer on prediction dataset (num_proc=5): 100%|██████████| 3904/3904 [00:38<00:00, 101.32 examples/s]
Concatenating 5 shards
06/19/2024 22:02:16 - INFO - datasets.arrow_dataset - Concatenating 5 shards
06/19/2024 22:02:19 - INFO - __main__ - *** Predict test set ***
./src/ts_sentence_seq_labeling.py:1017: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate
  metric = load_metric(data_args.metric_name)
/home/ext_maiara_frodrigues2000_gmail_/.pyenv/versions/spokennlp/lib/python3.8/site-packages/datasets/load.py:855: FutureWarning: The repository for seqeval contains custom code which must be executed to correctly load the metric. You can inspect the repository content at ./src/metrics/seqeval.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 metric from the next major release of `datasets`.
  warnings.warn(
[INFO|trainer.py:710] 2024-06-19 22:02:21,579 >> 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] 2024-06-19 22:02:21,586 >> ***** Running Prediction *****
[INFO|trainer.py:2966] 2024-06-19 22:02:21,586 >>   Num examples = 15271
[INFO|trainer.py:2969] 2024-06-19 22:02:21,586 >>   Batch size = 4
num predict samples:  15271
num predict examples:  3904

  0%|          | 0/3818 [00:00<?, ?it/s]
  0%|          | 2/3818 [00:00<12:30,  5.08it/s]
  0%|          | 3/3818 [00:00<17:54,  3.55it/s]
  0%|          | 4/3818 [00:01<20:32,  3.09it/s]
  0%|          | 5/3818 [00:01<22:04,  2.88it/s]
  0%|          | 6/3818 [00:01<23:08,  2.74it/s]
  0%|          | 7/3818 [00:02<23:44,  2.68it/s]
  0%|          | 8/3818 [00:02<24:11,  2.63it/s]
  0%|          | 9/3818 [00:03<24:28,  2.59it/s]
  0%|          | 10/3818 [00:03<24:36,  2.58it/s]
  0%|          | 11/3818 [00:03<24:46,  2.56it/s]
  0%|          | 12/3818 [00:04<24:55,  2.54it/s]
  0%|          | 13/3818 [00:04<24:59,  2.54it/s]
  0%|          | 14/3818 [00:05<25:07,  2.52it/s]
  0%|          | 15/3818 [00:05<25:08,  2.52it/s]
  0%|          | 16/3818 [00:05<25:07,  2.52it/s]
  0%|          | 17/3818 [00:06<25:14,  2.51it/s]
  0%|          | 18/3818 [00:06<25:11,  2.51it/s]
  0%|          | 19/3818 [00:07<25:13,  2.51it/s]
  1%|          | 20/3818 [00:07<25:15,  2.51it/s]
  1%|          | 21/3818 [00:07<25:13,  2.51it/s]
  1%|          | 22/3818 [00:08<25:10,  2.51it/s]
  1%|          | 23/3818 [00:08<25:17,  2.50it/s]
  1%|          | 24/3818 [00:09<25:18,  2.50it/s]
  1%|          | 25/3818 [00:09<25:19,  2.50it/s]
  1%|          | 26/3818 [00:09<25:21,  2.49it/s]
  1%|          | 27/3818 [00:10<25:17,  2.50it/s]
  1%|          | 28/3818 [00:10<25:19,  2.49it/s]
  1%|          | 29/3818 [00:11<25:21,  2.49it/s]
  1%|          | 30/3818 [00:11<25:18,  2.49it/s]
  1%|          | 31/3818 [00:11<25:18,  2.49it/s]
  1%|          | 32/3818 [00:12<25:21,  2.49it/s]
  1%|          | 33/3818 [00:12<25:20,  2.49it/s]
  1%|          | 34/3818 [00:13<25:16,  2.49it/s]
  1%|          | 35/3818 [00:13<25:17,  2.49it/s]
  1%|          | 36/3818 [00:13<25:25,  2.48it/s]
  1%|          | 37/3818 [00:14<25:23,  2.48it/s]
  1%|          | 38/3818 [00:14<25:25,  2.48it/s]
  1%|          | 39/3818 [00:15<25:24,  2.48it/s]
  1%|          | 40/3818 [00:15<25:23,  2.48it/s]
  1%|          | 41/3818 [00:15<25:26,  2.47it/s]
  1%|          | 42/3818 [00:16<25:25,  2.47it/s]
  1%|          | 43/3818 [00:16<25:27,  2.47it/s]
  1%|          | 44/3818 [00:17<25:25,  2.47it/s]
  1%|          | 45/3818 [00:17<25:25,  2.47it/s]
  1%|          | 46/3818 [00:18<25:30,  2.46it/s]
  1%|          | 47/3818 [00:18<25:25,  2.47it/s]
  1%|▏         | 48/3818 [00:18<25:25,  2.47it/s]
  1%|▏         | 49/3818 [00:19<25:26,  2.47it/s]
  1%|▏         | 50/3818 [00:19<25:22,  2.47it/s]
  1%|▏         | 51/3818 [00:20<25:27,  2.47it/s]
  1%|▏         | 52/3818 [00:20<25:27,  2.46it/s]
  1%|▏         | 53/3818 [00:20<25:28,  2.46it/s]
  1%|▏         | 54/3818 [00:21<25:22,  2.47it/s]
  1%|▏         | 55/3818 [00:21<25:19,  2.48it/s]
  1%|▏         | 56/3818 [00:22<25:19,  2.48it/s]
  1%|▏         | 57/3818 [00:22<25:18,  2.48it/s]
  2%|▏         | 58/3818 [00:22<25:18,  2.48it/s]
  2%|▏         | 59/3818 [00:23<25:19,  2.47it/s]
  2%|▏         | 60/3818 [00:23<25:19,  2.47it/s]
  2%|▏         | 61/3818 [00:24<25:18,  2.47it/s]
  2%|▏         | 62/3818 [00:24<25:21,  2.47it/s]
  2%|▏         | 63/3818 [00:24<25:22,  2.47it/s]
  2%|▏         | 64/3818 [00:25<25:20,  2.47it/s]
  2%|▏         | 65/3818 [00:25<25:21,  2.47it/s]
  2%|▏         | 66/3818 [00:26<25:17,  2.47it/s]
  2%|▏         | 67/3818 [00:26<25:19,  2.47it/s]
  2%|▏         | 68/3818 [00:26<25:18,  2.47it/s]
  2%|▏         | 69/3818 [00:27<25:13,  2.48it/s]
  2%|▏         | 70/3818 [00:27<25:19,  2.47it/s]
  2%|▏         | 71/3818 [00:28<25:20,  2.46it/s]
  2%|▏         | 72/3818 [00:28<25:19,  2.46it/s]
  2%|▏         | 73/3818 [00:28<25:27,  2.45it/s]
  2%|▏         | 74/3818 [00:29<25:23,  2.46it/s]
  2%|▏         | 75/3818 [00:29<25:25,  2.45it/s]
  2%|▏         | 76/3818 [00:30<25:23,  2.46it/s]
  2%|▏         | 77/3818 [00:30<25:18,  2.46it/s]
  2%|▏         | 78/3818 [00:30<25:21,  2.46it/s]
  2%|▏         | 79/3818 [00:31<25:19,  2.46it/s]
  2%|▏         | 80/3818 [00:31<25:24,  2.45it/s]
  2%|▏         | 81/3818 [00:32<25:24,  2.45it/s]
  2%|▏         | 82/3818 [00:32<25:24,  2.45it/s]
  2%|▏         | 83/3818 [00:33<25:24,  2.45it/s]
  2%|▏         | 84/3818 [00:33<25:20,  2.46it/s]
  2%|▏         | 85/3818 [00:33<25:20,  2.45it/s]
  2%|▏         | 86/3818 [00:34<25:24,  2.45it/s]
  2%|▏         | 87/3818 [00:34<25:25,  2.45it/s]
  2%|▏         | 88/3818 [00:35<25:26,  2.44it/s]
  2%|▏         | 89/3818 [00:35<25:26,  2.44it/s]
  2%|▏         | 90/3818 [00:35<25:28,  2.44it/s]
  2%|▏         | 91/3818 [00:36<25:28,  2.44it/s]
  2%|▏         | 92/3818 [00:36<25:26,  2.44it/s]
  2%|▏         | 93/3818 [00:37<25:27,  2.44it/s]
  2%|▏         | 94/3818 [00:37<25:25,  2.44it/s]
  2%|▏         | 95/3818 [00:37<25:28,  2.44it/s]
  3%|▎         | 96/3818 [00:38<25:31,  2.43it/s]
  3%|▎         | 97/3818 [00:38<25:37,  2.42it/s]
  3%|▎         | 98/3818 [00:39<25:35,  2.42it/s]
  3%|▎         | 99/3818 [00:39<25:32,  2.43it/s]
  3%|▎         | 100/3818 [00:40<25:28,  2.43it/s]
  3%|▎         | 101/3818 [00:40<25:33,  2.42it/s]
  3%|▎         | 102/3818 [00:40<25:30,  2.43it/s]
  3%|▎         | 103/3818 [00:41<25:25,  2.44it/s]
  3%|▎         | 104/3818 [00:41<25:26,  2.43it/s]
  3%|▎         | 105/3818 [00:42<25:27,  2.43it/s]
  3%|▎         | 106/3818 [00:42<25:24,  2.44it/s]
  3%|▎         | 107/3818 [00:42<25:30,  2.43it/s]
  3%|▎         | 108/3818 [00:43<25:27,  2.43it/s]
  3%|▎         | 109/3818 [00:43<25:29,  2.43it/s]
  3%|▎         | 110/3818 [00:44<25:29,  2.42it/s]
  3%|▎         | 111/3818 [00:44<25:28,  2.42it/s]
  3%|▎         | 112/3818 [00:44<25:33,  2.42it/s]
  3%|▎         | 113/3818 [00:45<25:30,  2.42it/s]
  3%|▎         | 114/3818 [00:45<25:25,  2.43it/s]
  3%|▎         | 115/3818 [00:46<25:30,  2.42it/s]
  3%|▎         | 116/3818 [00:46<25:30,  2.42it/s]
  3%|▎         | 117/3818 [00:47<25:34,  2.41it/s]
  3%|▎         | 118/3818 [00:47<25:34,  2.41it/s]
  3%|▎         | 119/3818 [00:47<25:33,  2.41it/s]
  3%|▎         | 120/3818 [00:48<25:36,  2.41it/s]
  3%|▎         | 121/3818 [00:48<25:30,  2.42it/s]
  3%|▎         | 122/3818 [00:49<25:31,  2.41it/s]
  3%|▎         | 123/3818 [00:49<25:32,  2.41it/s]
  3%|▎         | 124/3818 [00:49<25:30,  2.41it/s]
  3%|▎         | 125/3818 [00:50<25:32,  2.41it/s]
  3%|▎         | 126/3818 [00:50<25:29,  2.41it/s]
  3%|▎         | 127/3818 [00:51<25:35,  2.40it/s]
  3%|▎         | 128/3818 [00:51<25:33,  2.41it/s]
  3%|▎         | 129/3818 [00:52<25:35,  2.40it/s]
  3%|▎         | 130/3818 [00:52<25:34,  2.40it/s]
  3%|▎         | 131/3818 [00:52<25:36,  2.40it/s]
  3%|▎         | 132/3818 [00:53<25:39,  2.39it/s]
  3%|▎         | 133/3818 [00:53<25:36,  2.40it/s]
  4%|▎         | 134/3818 [00:54<25:42,  2.39it/s]
  4%|▎         | 135/3818 [00:54<25:39,  2.39it/s]
  4%|▎         | 136/3818 [00:54<25:41,  2.39it/s]
  4%|▎         | 137/3818 [00:55<25:39,  2.39it/s]
  4%|▎         | 138/3818 [00:55<25:38,  2.39it/s]
  4%|▎         | 139/3818 [00:56<25:40,  2.39it/s]
  4%|▎         | 140/3818 [00:56<25:39,  2.39it/s]
  4%|▎         | 141/3818 [00:57<25:36,  2.39it/s]
  4%|▎         | 142/3818 [00:57<25:40,  2.39it/s]
  4%|▎         | 143/3818 [00:57<25:42,  2.38it/s]
  4%|▍         | 144/3818 [00:58<25:40,  2.38it/s]
  4%|▍         | 145/3818 [00:58<25:36,  2.39it/s]
  4%|▍         | 146/3818 [00:59<25:37,  2.39it/s]
  4%|▍         | 147/3818 [00:59<25:39,  2.38it/s]
  4%|▍         | 148/3818 [00:59<25:41,  2.38it/s]
  4%|▍         | 149/3818 [01:00<25:40,  2.38it/s]
  4%|▍         | 150/3818 [01:00<25:40,  2.38it/s]
  4%|▍         | 151/3818 [01:01<25:41,  2.38it/s]
  4%|▍         | 152/3818 [01:01<25:42,  2.38it/s]
  4%|▍         | 153/3818 [01:02<25:43,  2.38it/s]
  4%|▍         | 154/3818 [01:02<25:41,  2.38it/s]
  4%|▍         | 155/3818 [01:02<25:42,  2.38it/s]
  4%|▍         | 156/3818 [01:03<25:37,  2.38it/s]
  4%|▍         | 157/3818 [01:03<25:33,  2.39it/s]
  4%|▍         | 158/3818 [01:04<25:29,  2.39it/s]
  4%|▍         | 159/3818 [01:04<25:24,  2.40it/s]
  4%|▍         | 160/3818 [01:04<25:22,  2.40it/s]
  4%|▍         | 161/3818 [01:05<25:20,  2.40it/s]
  4%|▍         | 162/3818 [01:05<25:23,  2.40it/s]
  4%|▍         | 163/3818 [01:06<25:19,  2.41it/s]
  4%|▍         | 164/3818 [01:06<25:23,  2.40it/s]
  4%|▍         | 165/3818 [01:07<25:20,  2.40it/s]
  4%|▍         | 166/3818 [01:07<25:21,  2.40it/s]
  4%|▍         | 167/3818 [01:07<25:23,  2.40it/s]
  4%|▍         | 168/3818 [01:08<25:21,  2.40it/s]
  4%|▍         | 169/3818 [01:08<25:24,  2.39it/s]
  4%|▍         | 170/3818 [01:09<25:22,  2.40it/s]
  4%|▍         | 171/3818 [01:09<25:19,  2.40it/s]
  5%|▍         | 172/3818 [01:09<25:18,  2.40it/s]
  5%|▍         | 173/3818 [01:10<25:16,  2.40it/s]
  5%|▍         | 174/3818 [01:10<25:19,  2.40it/s]
  5%|▍         | 175/3818 [01:11<25:21,  2.39it/s]
  5%|▍         | 176/3818 [01:11<25:16,  2.40it/s]
  5%|▍         | 177/3818 [01:12<25:21,  2.39it/s]
  5%|▍         | 178/3818 [01:12<25:23,  2.39it/s]
  5%|▍         | 179/3818 [01:12<25:22,  2.39it/s]
  5%|▍         | 180/3818 [01:13<25:22,  2.39it/s]
  5%|▍         | 181/3818 [01:13<25:19,  2.39it/s]
  5%|▍         | 182/3818 [01:14<25:21,  2.39it/s]
  5%|▍         | 183/3818 [01:14<25:21,  2.39it/s]
  5%|▍         | 184/3818 [01:15<25:21,  2.39it/s]
  5%|▍         | 185/3818 [01:15<25:25,  2.38it/s]
  5%|▍         | 186/3818 [01:15<25:23,  2.38it/s]
  5%|▍         | 187/3818 [01:16<25:26,  2.38it/s]
  5%|▍         | 188/3818 [01:16<25:25,  2.38it/s]
  5%|▍         | 189/3818 [01:17<25:25,  2.38it/s]
  5%|▍         | 190/3818 [01:17<25:26,  2.38it/s]
  5%|▌         | 191/3818 [01:17<25:22,  2.38it/s]
  5%|▌         | 192/3818 [01:18<25:25,  2.38it/s]
  5%|▌         | 193/3818 [01:18<25:27,  2.37it/s]
  5%|▌         | 194/3818 [01:19<25:26,  2.37it/s]
  5%|▌         | 195/3818 [01:19<25:29,  2.37it/s]
  5%|▌         | 196/3818 [01:20<25:27,  2.37it/s]
  5%|▌         | 197/3818 [01:20<25:26,  2.37it/s]
  5%|▌         | 198/3818 [01:20<25:24,  2.37it/s]
  5%|▌         | 199/3818 [01:21<25:26,  2.37it/s]
  5%|▌         | 200/3818 [01:21<25:28,  2.37it/s]
  5%|▌         | 201/3818 [01:22<25:31,  2.36it/s]
  5%|▌         | 202/3818 [01:22<25:30,  2.36it/s]
  5%|▌         | 203/3818 [01:23<25:30,  2.36it/s]
  5%|▌         | 204/3818 [01:23<25:31,  2.36it/s]
  5%|▌         | 205/3818 [01:23<25:31,  2.36it/s]
  5%|▌         | 206/3818 [01:24<25:28,  2.36it/s]
  5%|▌         | 207/3818 [01:24<25:30,  2.36it/s]
  5%|▌         | 208/3818 [01:25<25:28,  2.36it/s]
  5%|▌         | 209/3818 [01:25<25:29,  2.36it/s]
  6%|▌         | 210/3818 [01:25<25:28,  2.36it/s]
  6%|▌         | 211/3818 [01:26<25:29,  2.36it/s]
  6%|▌         | 212/3818 [01:26<25:29,  2.36it/s]
  6%|▌         | 213/3818 [01:27<25:29,  2.36it/s]
  6%|▌         | 214/3818 [01:27<25:26,  2.36it/s]
  6%|▌         | 215/3818 [01:28<25:26,  2.36it/s]
  6%|▌         | 216/3818 [01:28<25:25,  2.36it/s]
  6%|▌         | 217/3818 [01:28<25:26,  2.36it/s]
  6%|▌         | 218/3818 [01:29<25:24,  2.36it/s]
  6%|▌         | 219/3818 [01:29<25:23,  2.36it/s]
  6%|▌         | 220/3818 [01:30<25:24,  2.36it/s]
  6%|▌         | 221/3818 [01:30<25:25,  2.36it/s]
  6%|▌         | 222/3818 [01:31<25:27,  2.35it/s]
  6%|▌         | 223/3818 [01:31<25:23,  2.36it/s]
  6%|▌         | 224/3818 [01:31<25:26,  2.35it/s]
  6%|▌         | 225/3818 [01:32<25:24,  2.36it/s]
  6%|▌         | 226/3818 [01:32<25:21,  2.36it/s]
  6%|▌         | 227/3818 [01:33<25:24,  2.36it/s]
  6%|▌         | 228/3818 [01:33<25:21,  2.36it/s]
  6%|▌         | 229/3818 [01:34<25:24,  2.35it/s]
  6%|▌         | 230/3818 [01:34<25:22,  2.36it/s]
  6%|▌         | 231/3818 [01:34<25:24,  2.35it/s]
  6%|▌         | 232/3818 [01:35<25:22,  2.36it/s]
  6%|▌         | 233/3818 [01:35<25:21,  2.36it/s]
  6%|▌         | 234/3818 [01:36<25:25,  2.35it/s]
  6%|▌         | 235/3818 [01:36<25:24,  2.35it/s]
  6%|▌         | 236/3818 [01:37<25:27,  2.34it/s]
  6%|▌         | 237/3818 [01:37<25:28,  2.34it/s]
  6%|▌         | 238/3818 [01:37<25:26,  2.35it/s]
  6%|▋         | 239/3818 [01:38<25:29,  2.34it/s]
  6%|▋         | 240/3818 [01:38<25:26,  2.34it/s]
  6%|▋         | 241/3818 [01:39<25:29,  2.34it/s]
  6%|▋         | 242/3818 [01:39<25:28,  2.34it/s]
  6%|▋         | 243/3818 [01:40<25:26,  2.34it/s]
  6%|▋         | 244/3818 [01:40<25:26,  2.34it/s]
  6%|▋         | 245/3818 [01:40<25:26,  2.34it/s]
  6%|▋         | 246/3818 [01:41<25:27,  2.34it/s]
  6%|▋         | 247/3818 [01:41<25:29,  2.34it/s]
  6%|▋         | 248/3818 [01:42<25:29,  2.33it/s]
  7%|▋         | 249/3818 [01:42<25:32,  2.33it/s]
  7%|▋         | 250/3818 [01:43<25:32,  2.33it/s]
  7%|▋         | 251/3818 [01:43<25:33,  2.33it/s]
  7%|▋         | 252/3818 [01:43<25:36,  2.32it/s]
  7%|▋         | 253/3818 [01:44<25:34,  2.32it/s]
  7%|▋         | 254/3818 [01:44<25:33,  2.32it/s]
  7%|▋         | 255/3818 [01:45<25:31,  2.33it/s]
  7%|▋         | 256/3818 [01:45<25:30,  2.33it/s]
  7%|▋         | 257/3818 [01:46<25:32,  2.32it/s]
  7%|▋         | 258/3818 [01:46<25:33,  2.32it/s]
  7%|▋         | 259/3818 [01:46<25:34,  2.32it/s]
  7%|▋         | 260/3818 [01:47<25:33,  2.32it/s]
  7%|▋         | 261/3818 [01:47<25:31,  2.32it/s]
  7%|▋         | 262/3818 [01:48<25:30,  2.32it/s]
  7%|▋         | 263/3818 [01:48<25:32,  2.32it/s]
  7%|▋         | 264/3818 [01:49<25:31,  2.32it/s]
  7%|▋         | 265/3818 [01:49<25:34,  2.32it/s]
  7%|▋         | 266/3818 [01:49<25:32,  2.32it/s]
  7%|▋         | 267/3818 [01:50<25:31,  2.32it/s]
  7%|▋         | 268/3818 [01:50<25:35,  2.31it/s]
  7%|▋         | 269/3818 [01:51<25:30,  2.32it/s]
  7%|▋         | 270/3818 [01:51<25:33,  2.31it/s]
  7%|▋         | 271/3818 [01:52<25:33,  2.31it/s]
  7%|▋         | 272/3818 [01:52<25:33,  2.31it/s]
  7%|▋         | 273/3818 [01:52<25:33,  2.31it/s]
  7%|▋         | 274/3818 [01:53<25:31,  2.31it/s]
  7%|▋         | 275/3818 [01:53<25:35,  2.31it/s]
  7%|▋         | 276/3818 [01:54<25:33,  2.31it/s]
  7%|▋         | 277/3818 [01:54<25:34,  2.31it/s]
  7%|▋         | 278/3818 [01:55<25:33,  2.31it/s]
  7%|▋         | 279/3818 [01:55<25:30,  2.31it/s]
  7%|▋         | 280/3818 [01:55<25:31,  2.31it/s]
  7%|▋         | 281/3818 [01:56<25:28,  2.31it/s]
  7%|▋         | 282/3818 [01:56<25:33,  2.31it/s]
  7%|▋         | 283/3818 [01:57<25:31,  2.31it/s]
  7%|▋         | 284/3818 [01:57<25:33,  2.30it/s]
  7%|▋         | 285/3818 [01:58<25:31,  2.31it/s]
  7%|▋         | 286/3818 [01:58<25:30,  2.31it/s]
  8%|▊         | 287/3818 [01:59<25:32,  2.30it/s]
  8%|▊         | 288/3818 [01:59<25:32,  2.30it/s]
  8%|▊         | 289/3818 [01:59<25:31,  2.30it/s]
  8%|▊         | 290/3818 [02:00<25:29,  2.31it/s]
  8%|▊         | 291/3818 [02:00<25:30,  2.31it/s]
  8%|▊         | 292/3818 [02:01<25:28,  2.31it/s]
  8%|▊         | 293/3818 [02:01<25:26,  2.31it/s]
  8%|▊         | 294/3818 [02:02<25:26,  2.31it/s]
  8%|▊         | 295/3818 [02:02<25:24,  2.31it/s]
  8%|▊         | 296/3818 [02:02<25:28,  2.30it/s]
  8%|▊         | 297/3818 [02:03<25:28,  2.30it/s]
  8%|▊         | 298/3818 [02:03<25:26,  2.31it/s]
  8%|▊         | 299/3818 [02:04<25:24,  2.31it/s]
  8%|▊         | 300/3818 [02:04<25:28,  2.30it/s]
  8%|▊         | 301/3818 [02:05<25:29,  2.30it/s]
  8%|▊         | 302/3818 [02:05<25:29,  2.30it/s]
  8%|▊         | 303/3818 [02:05<25:30,  2.30it/s]
  8%|▊         | 304/3818 [02:06<25:33,  2.29it/s]
  8%|▊         | 305/3818 [02:06<25:31,  2.29it/s]
  8%|▊         | 306/3818 [02:07<25:32,  2.29it/s]
  8%|▊         | 307/3818 [02:07<25:33,  2.29it/s]
  8%|▊         | 308/3818 [02:08<25:30,  2.29it/s]
  8%|▊         | 309/3818 [02:08<25:33,  2.29it/s]
  8%|▊         | 310/3818 [02:09<25:33,  2.29it/s]
  8%|▊         | 311/3818 [02:09<25:30,  2.29it/s]
  8%|▊         | 312/3818 [02:09<25:32,  2.29it/s]
  8%|▊         | 313/3818 [02:10<25:30,  2.29it/s]
  8%|▊         | 314/3818 [02:10<25:30,  2.29it/s]
  8%|▊         | 315/3818 [02:11<25:30,  2.29it/s]
  8%|▊         | 316/3818 [02:11<25:27,  2.29it/s]
  8%|▊         | 317/3818 [02:12<25:32,  2.28it/s]
  8%|▊         | 318/3818 [02:12<25:31,  2.29it/s]
  8%|▊         | 319/3818 [02:12<25:29,  2.29it/s]
  8%|▊         | 320/3818 [02:13<25:30,  2.28it/s]
  8%|▊         | 321/3818 [02:13<25:30,  2.28it/s]
  8%|▊         | 322/3818 [02:14<25:23,  2.29it/s]
  8%|▊         | 323/3818 [02:14<25:25,  2.29it/s]
  8%|▊         | 324/3818 [02:15<25:29,  2.28it/s]
  9%|▊         | 325/3818 [02:15<25:27,  2.29it/s]
  9%|▊         | 326/3818 [02:16<25:38,  2.27it/s]
  9%|▊         | 327/3818 [02:16<25:31,  2.28it/s]
  9%|▊         | 328/3818 [02:16<25:32,  2.28it/s]
  9%|▊         | 329/3818 [02:17<25:27,  2.28it/s]
  9%|▊         | 330/3818 [02:17<25:31,  2.28it/s]
  9%|▊         | 331/3818 [02:18<25:33,  2.27it/s]
  9%|▊         | 332/3818 [02:18<25:30,  2.28it/s]
  9%|▊         | 333/3818 [02:19<25:31,  2.28it/s]
  9%|▊         | 334/3818 [02:19<25:28,  2.28it/s]
  9%|▉         | 335/3818 [02:19<25:32,  2.27it/s]
  9%|▉         | 336/3818 [02:20<25:31,  2.27it/s]
  9%|▉         | 337/3818 [02:20<25:29,  2.28it/s]
  9%|▉         | 338/3818 [02:21<25:28,  2.28it/s]
  9%|▉         | 339/3818 [02:21<25:27,  2.28it/s]
  9%|▉         | 340/3818 [02:22<25:29,  2.27it/s]
  9%|▉         | 341/3818 [02:22<25:28,  2.28it/s]
  9%|▉         | 342/3818 [02:23<25:30,  2.27it/s]
  9%|▉         | 343/3818 [02:23<25:27,  2.28it/s]
  9%|▉         | 344/3818 [02:23<25:31,  2.27it/s]
  9%|▉         | 345/3818 [02:24<25:30,  2.27it/s]
  9%|▉         | 346/3818 [02:24<25:29,  2.27it/s]
  9%|▉         | 347/3818 [02:25<25:31,  2.27it/s]
  9%|▉         | 348/3818 [02:25<25:29,  2.27it/s]
  9%|▉         | 349/3818 [02:26<25:32,  2.26it/s]
  9%|▉         | 350/3818 [02:26<25:31,  2.26it/s]
  9%|▉         | 351/3818 [02:27<25:29,  2.27it/s]
  9%|▉         | 352/3818 [02:27<25:30,  2.26it/s]
  9%|▉         | 353/3818 [02:27<25:29,  2.27it/s]
  9%|▉         | 354/3818 [02:28<25:29,  2.26it/s]
  9%|▉         | 355/3818 [02:28<25:28,  2.27it/s]
  9%|▉         | 356/3818 [02:29<25:26,  2.27it/s]
  9%|▉         | 357/3818 [02:29<25:26,  2.27it/s]
  9%|▉         | 358/3818 [02:30<25:24,  2.27it/s]
  9%|▉         | 359/3818 [02:30<25:25,  2.27it/s]
  9%|▉         | 360/3818 [02:30<25:25,  2.27it/s]
  9%|▉         | 361/3818 [02:31<25:26,  2.26it/s]
  9%|▉         | 362/3818 [02:31<25:23,  2.27it/s]
 10%|▉         | 363/3818 [02:32<25:26,  2.26it/s]
 10%|▉         | 364/3818 [02:32<25:29,  2.26it/s]
 10%|▉         | 365/3818 [02:33<25:28,  2.26it/s]
 10%|▉         | 366/3818 [02:33<25:27,  2.26it/s]
 10%|▉         | 367/3818 [02:34<25:25,  2.26it/s]
 10%|▉         | 368/3818 [02:34<25:25,  2.26it/s]
 10%|▉         | 369/3818 [02:34<25:26,  2.26it/s]
 10%|▉         | 370/3818 [02:35<25:24,  2.26it/s]
 10%|▉         | 371/3818 [02:35<25:25,  2.26it/s]
 10%|▉         | 372/3818 [02:36<25:23,  2.26it/s]
 10%|▉         | 373/3818 [02:36<25:26,  2.26it/s]
 10%|▉         | 374/3818 [02:37<25:25,  2.26it/s]
 10%|▉         | 375/3818 [02:37<25:24,  2.26it/s]
 10%|▉         | 376/3818 [02:38<25:26,  2.25it/s]
 10%|▉         | 377/3818 [02:38<25:26,  2.25it/s]
 10%|▉         | 378/3818 [02:38<25:25,  2.25it/s]
 10%|▉         | 379/3818 [02:39<25:23,  2.26it/s]
 10%|▉         | 380/3818 [02:39<25:26,  2.25it/s]
 10%|▉         | 381/3818 [02:40<25:26,  2.25it/s]
 10%|█         | 382/3818 [02:40<25:23,  2.26it/s]
 10%|█         | 383/3818 [02:41<25:27,  2.25it/s]
 10%|█         | 384/3818 [02:41<25:29,  2.25it/s]
 10%|█         | 385/3818 [02:42<25:29,  2.24it/s]
 10%|█         | 386/3818 [02:42<25:25,  2.25it/s]
 10%|█         | 387/3818 [02:42<25:27,  2.25it/s]
 10%|█         | 388/3818 [02:43<25:26,  2.25it/s]
 10%|█         | 389/3818 [02:43<25:23,  2.25it/s]
 10%|█         | 390/3818 [02:44<25:21,  2.25it/s]
 10%|█         | 391/3818 [02:44<25:21,  2.25it/s]
 10%|█         | 392/3818 [02:45<25:21,  2.25it/s]
 10%|█         | 393/3818 [02:45<25:17,  2.26it/s]
 10%|█         | 394/3818 [02:46<25:24,  2.25it/s]
 10%|█         | 395/3818 [02:46<25:22,  2.25it/s]
 10%|█         | 396/3818 [02:46<25:22,  2.25it/s]
 10%|█         | 397/3818 [02:47<25:19,  2.25it/s]
 10%|█         | 398/3818 [02:47<25:20,  2.25it/s]
 10%|█         | 399/3818 [02:48<25:17,  2.25it/s]
 10%|█         | 400/3818 [02:48<25:20,  2.25it/s]
 11%|█         | 401/3818 [02:49<25:21,  2.25it/s]
 11%|█         | 402/3818 [02:49<25:18,  2.25it/s]
 11%|█         | 403/3818 [02:50<25:19,  2.25it/s]
 11%|█         | 404/3818 [02:50<25:14,  2.25it/s]
 11%|█         | 405/3818 [02:50<25:18,  2.25it/s]
 11%|█         | 406/3818 [02:51<25:17,  2.25it/s]
 11%|█         | 407/3818 [02:51<25:19,  2.24it/s]
 11%|█         | 408/3818 [02:52<25:19,  2.24it/s]
 11%|█         | 409/3818 [02:52<25:18,  2.24it/s]
 11%|█         | 410/3818 [02:53<25:17,  2.25it/s]
 11%|█         | 411/3818 [02:53<25:19,  2.24it/s]
 11%|█         | 412/3818 [02:54<25:21,  2.24it/s]
 11%|█         | 413/3818 [02:54<25:19,  2.24it/s]
 11%|█         | 414/3818 [02:54<25:21,  2.24it/s]
 11%|█         | 415/3818 [02:55<25:20,  2.24it/s]
 11%|█         | 416/3818 [02:55<25:19,  2.24it/s]
 11%|█         | 417/3818 [02:56<25:21,  2.24it/s]
 11%|█         | 418/3818 [02:56<25:20,  2.24it/s]
 11%|█         | 419/3818 [02:57<25:16,  2.24it/s]
 11%|█         | 420/3818 [02:57<25:16,  2.24it/s]
 11%|█         | 421/3818 [02:58<25:18,  2.24it/s]
 11%|█         | 422/3818 [02:58<25:18,  2.24it/s]
 11%|█         | 423/3818 [02:59<25:20,  2.23it/s]
 11%|█         | 424/3818 [02:59<25:22,  2.23it/s]
 11%|█         | 425/3818 [02:59<25:18,  2.23it/s]
 11%|█         | 426/3818 [03:00<25:17,  2.23it/s]
 11%|█         | 427/3818 [03:00<25:16,  2.24it/s]
 11%|█         | 428/3818 [03:01<25:05,  2.25it/s]
 11%|█         | 429/3818 [03:01<25:03,  2.25it/s]
 11%|█▏        | 430/3818 [03:02<24:59,  2.26it/s]
 11%|█▏        | 431/3818 [03:02<25:00,  2.26it/s]
 11%|█▏        | 432/3818 [03:02<24:56,  2.26it/s]
 11%|█▏        | 433/3818 [03:03<24:55,  2.26it/s]
 11%|█▏        | 434/3818 [03:03<24:52,  2.27it/s]
 11%|█▏        | 435/3818 [03:04<24:51,  2.27it/s]
 11%|█▏        | 436/3818 [03:04<24:49,  2.27it/s]
 11%|█▏        | 437/3818 [03:05<24:48,  2.27it/s]
 11%|█▏        | 438/3818 [03:05<24:48,  2.27it/s]
 11%|█▏        | 439/3818 [03:06<24:50,  2.27it/s]
 12%|█▏        | 440/3818 [03:06<24:47,  2.27it/s]
 12%|█▏        | 441/3818 [03:06<24:44,  2.28it/s]
 12%|█▏        | 442/3818 [03:07<24:42,  2.28it/s]
 12%|█▏        | 443/3818 [03:07<24:47,  2.27it/s]
 12%|█▏        | 444/3818 [03:08<24:47,  2.27it/s]
 12%|█▏        | 445/3818 [03:08<24:47,  2.27it/s]
 12%|█▏        | 446/3818 [03:09<24:45,  2.27it/s]
 12%|█▏        | 447/3818 [03:09<24:47,  2.27it/s]
 12%|█▏        | 448/3818 [03:10<24:47,  2.27it/s]
 12%|█▏        | 449/3818 [03:10<24:45,  2.27it/s]
 12%|█▏        | 450/3818 [03:10<24:45,  2.27it/s]
 12%|█▏        | 451/3818 [03:11<24:46,  2.27it/s]
 12%|█▏        | 452/3818 [03:11<24:46,  2.26it/s]
 12%|█▏        | 453/3818 [03:12<24:48,  2.26it/s]
 12%|█▏        | 454/3818 [03:12<24:48,  2.26it/s]
 12%|█▏        | 455/3818 [03:13<24:45,  2.26it/s]
 12%|█▏        | 456/3818 [03:13<24:45,  2.26it/s]
 12%|█▏        | 457/3818 [03:14<24:49,  2.26it/s]
 12%|█▏        | 458/3818 [03:14<24:46,  2.26it/s]
 12%|█▏        | 459/3818 [03:14<24:45,  2.26it/s]
 12%|█▏        | 460/3818 [03:15<24:43,  2.26it/s]
 12%|█▏        | 461/3818 [03:15<24:44,  2.26it/s]
 12%|█▏        | 462/3818 [03:16<24:44,  2.26it/s]
 12%|█▏        | 463/3818 [03:16<24:45,  2.26it/s]
 12%|█▏        | 464/3818 [03:17<24:44,  2.26it/s]
 12%|█▏        | 465/3818 [03:17<24:42,  2.26it/s]
 12%|█▏        | 466/3818 [03:18<24:43,  2.26it/s]
 12%|█▏        | 467/3818 [03:18<24:45,  2.26it/s]
 12%|█▏        | 468/3818 [03:18<24:44,  2.26it/s]
 12%|█▏        | 469/3818 [03:19<24:45,  2.25it/s]
 12%|█▏        | 470/3818 [03:19<24:41,  2.26it/s]
 12%|█▏        | 471/3818 [03:20<24:42,  2.26it/s]
 12%|█▏        | 472/3818 [03:20<24:42,  2.26it/s]
 12%|█▏        | 473/3818 [03:21<24:43,  2.26it/s]
 12%|█▏        | 474/3818 [03:21<24:44,  2.25it/s]
 12%|█▏        | 475/3818 [03:21<24:41,  2.26it/s]
 12%|█▏        | 476/3818 [03:22<24:42,  2.25it/s]
 12%|█▏        | 477/3818 [03:22<24:46,  2.25it/s]
 13%|█▎        | 478/3818 [03:23<24:45,  2.25it/s]
 13%|█▎        | 479/3818 [03:23<24:44,  2.25it/s]
 13%|█▎        | 480/3818 [03:24<24:42,  2.25it/s]
 13%|█▎        | 481/3818 [03:24<24:41,  2.25it/s]
 13%|█▎        | 482/3818 [03:25<24:40,  2.25it/s]
 13%|█▎        | 483/3818 [03:25<24:40,  2.25it/s]
 13%|█▎        | 484/3818 [03:25<24:40,  2.25it/s]
 13%|█▎        | 485/3818 [03:26<24:38,  2.25it/s]
 13%|█▎        | 486/3818 [03:26<24:37,  2.26it/s]
 13%|█▎        | 487/3818 [03:27<24:38,  2.25it/s]
 13%|█▎        | 488/3818 [03:27<24:36,  2.26it/s]
 13%|█▎        | 489/3818 [03:28<24:38,  2.25it/s]
 13%|█▎        | 490/3818 [03:28<24:37,  2.25it/s]
 13%|█▎        | 491/3818 [03:29<24:39,  2.25it/s]
 13%|█▎        | 492/3818 [03:29<24:36,  2.25it/s]
 13%|█▎        | 493/3818 [03:29<24:39,  2.25it/s]
 13%|█▎        | 494/3818 [03:30<24:35,  2.25it/s]
 13%|█▎        | 495/3818 [03:30<24:35,  2.25it/s]
 13%|█▎        | 496/3818 [03:31<24:35,  2.25it/s]
 13%|█▎        | 497/3818 [03:31<24:34,  2.25it/s]
 13%|█▎        | 498/3818 [03:32<24:37,  2.25it/s]
 13%|█▎        | 499/3818 [03:32<24:34,  2.25it/s]
 13%|█▎        | 500/3818 [03:33<24:38,  2.24it/s]
 13%|█▎        | 501/3818 [03:33<24:38,  2.24it/s]
 13%|█▎        | 502/3818 [03:33<24:38,  2.24it/s]
 13%|█▎        | 503/3818 [03:34<24:39,  2.24it/s]
 13%|█▎        | 504/3818 [03:34<24:36,  2.24it/s]
 13%|█▎        | 505/3818 [03:35<24:36,  2.24it/s]
 13%|█▎        | 506/3818 [03:35<24:36,  2.24it/s]
 13%|█▎        | 507/3818 [03:36<24:35,  2.24it/s]
 13%|█▎        | 508/3818 [03:36<24:33,  2.25it/s]
 13%|█▎        | 509/3818 [03:37<24:34,  2.24it/s]
 13%|█▎        | 510/3818 [03:37<24:32,  2.25it/s]
 13%|█▎        | 511/3818 [03:38<24:32,  2.25it/s]
 13%|█▎        | 512/3818 [03:38<24:33,  2.24it/s]
 13%|█▎        | 513/3818 [03:38<24:31,  2.25it/s]
 13%|█▎        | 514/3818 [03:39<24:29,  2.25it/s]
 13%|█▎        | 515/3818 [03:39<24:28,  2.25it/s]
 14%|█▎        | 516/3818 [03:40<24:31,  2.24it/s]
 14%|█▎        | 517/3818 [03:40<24:30,  2.25it/s]
 14%|█▎        | 518/3818 [03:41<24:28,  2.25it/s]
 14%|█▎        | 519/3818 [03:41<24:23,  2.25it/s]
 14%|█▎        | 520/3818 [03:42<24:27,  2.25it/s]
 14%|█▎        | 521/3818 [03:42<24:28,  2.25it/s]
 14%|█▎        | 522/3818 [03:42<24:28,  2.24it/s]
 14%|█▎        | 523/3818 [03:43<24:29,  2.24it/s]
 14%|█▎        | 524/3818 [03:43<24:28,  2.24it/s]
 14%|█▍        | 525/3818 [03:44<24:26,  2.25it/s]
 14%|█▍        | 526/3818 [03:44<24:27,  2.24it/s]
 14%|█▍        | 527/3818 [03:45<24:26,  2.24it/s]
 14%|█▍        | 528/3818 [03:45<24:26,  2.24it/s]
 14%|█▍        | 529/3818 [03:46<24:28,  2.24it/s]
 14%|█▍        | 530/3818 [03:46<24:27,  2.24it/s]
 14%|█▍        | 531/3818 [03:46<24:27,  2.24it/s]
 14%|█▍        | 532/3818 [03:47<24:23,  2.25it/s]
 14%|█▍        | 533/3818 [03:47<24:25,  2.24it/s]
 14%|█▍        | 534/3818 [03:48<24:26,  2.24it/s]
 14%|█▍        | 535/3818 [03:48<24:23,  2.24it/s]
 14%|█▍        | 536/3818 [03:49<24:26,  2.24it/s]
 14%|█▍        | 537/3818 [03:49<24:24,  2.24it/s]
 14%|█▍        | 538/3818 [03:50<24:23,  2.24it/s]
 14%|█▍        | 539/3818 [03:50<24:23,  2.24it/s]
 14%|█▍        | 540/3818 [03:50<24:26,  2.24it/s]
 14%|█▍        | 541/3818 [03:51<24:22,  2.24it/s]
 14%|█▍        | 542/3818 [03:51<24:21,  2.24it/s]
 14%|█▍        | 543/3818 [03:52<24:21,  2.24it/s]
 14%|█▍        | 544/3818 [03:52<24:21,  2.24it/s]
 14%|█▍        | 545/3818 [03:53<24:22,  2.24it/s]
 14%|█▍        | 546/3818 [03:53<24:21,  2.24it/s]
 14%|█▍        | 547/3818 [03:54<24:22,  2.24it/s]
 14%|█▍        | 548/3818 [03:54<24:21,  2.24it/s]
 14%|█▍        | 549/3818 [03:54<24:21,  2.24it/s]
 14%|█▍        | 550/3818 [03:55<24:19,  2.24it/s]
 14%|█▍        | 551/3818 [03:55<24:22,  2.23it/s]
 14%|█▍        | 552/3818 [03:56<24:22,  2.23it/s]
 14%|█▍        | 553/3818 [03:56<24:21,  2.23it/s]
 15%|█▍        | 554/3818 [03:57<24:23,  2.23it/s]
 15%|█▍        | 555/3818 [03:57<24:23,  2.23it/s]
 15%|█▍        | 556/3818 [03:58<24:20,  2.23it/s]
 15%|█▍        | 557/3818 [03:58<24:23,  2.23it/s]
 15%|█▍        | 558/3818 [03:58<24:23,  2.23it/s]
 15%|█▍        | 559/3818 [03:59<24:23,  2.23it/s]
 15%|█▍        | 560/3818 [03:59<24:20,  2.23it/s]
 15%|█▍        | 561/3818 [04:00<24:20,  2.23it/s]
 15%|█▍        | 562/3818 [04:00<24:23,  2.22it/s]
 15%|█▍        | 563/3818 [04:01<24:20,  2.23it/s]
 15%|█▍        | 564/3818 [04:01<24:24,  2.22it/s]
 15%|█▍        | 565/3818 [04:02<24:18,  2.23it/s]
 15%|█▍        | 566/3818 [04:02<24:21,  2.23it/s]
 15%|█▍        | 567/3818 [04:03<24:21,  2.22it/s]
 15%|█▍        | 568/3818 [04:03<24:22,  2.22it/s]
 15%|█▍        | 569/3818 [04:03<24:22,  2.22it/s]
 15%|█▍        | 570/3818 [04:04<24:20,  2.22it/s]
 15%|█▍        | 571/3818 [04:04<24:22,  2.22it/s]
 15%|█▍        | 572/3818 [04:05<24:18,  2.23it/s]
 15%|█▌        | 573/3818 [04:05<24:17,  2.23it/s]
 15%|█▌        | 574/3818 [04:06<24:16,  2.23it/s]
 15%|█▌        | 575/3818 [04:06<24:14,  2.23it/s]
 15%|█▌        | 576/3818 [04:07<24:18,  2.22it/s]
 15%|█▌        | 577/3818 [04:07<24:16,  2.23it/s]
 15%|█▌        | 578/3818 [04:07<24:17,  2.22it/s]
 15%|█▌        | 579/3818 [04:08<24:16,  2.22it/s]
 15%|█▌        | 580/3818 [04:08<24:14,  2.23it/s]
 15%|█▌        | 581/3818 [04:09<24:10,  2.23it/s]
 15%|█▌        | 582/3818 [04:09<24:10,  2.23it/s]
 15%|█▌        | 583/3818 [04:10<24:09,  2.23it/s]
 15%|█▌        | 584/3818 [04:10<24:10,  2.23it/s]
 15%|█▌        | 585/3818 [04:11<24:14,  2.22it/s]
 15%|█▌        | 586/3818 [04:11<24:13,  2.22it/s]
 15%|█▌        | 587/3818 [04:12<24:10,  2.23it/s]
 15%|█▌        | 588/3818 [04:12<24:10,  2.23it/s]
 15%|█▌        | 589/3818 [04:12<24:12,  2.22it/s]
 15%|█▌        | 590/3818 [04:13<24:11,  2.22it/s]
 15%|█▌        | 591/3818 [04:13<24:12,  2.22it/s]
 16%|█▌        | 592/3818 [04:14<24:08,  2.23it/s]
 16%|█▌        | 593/3818 [04:14<24:08,  2.23it/s]
 16%|█▌        | 594/3818 [04:15<24:09,  2.22it/s]
 16%|█▌        | 595/3818 [04:15<24:12,  2.22it/s]
 16%|█▌        | 596/3818 [04:16<24:07,  2.23it/s]
 16%|█▌        | 597/3818 [04:16<24:04,  2.23it/s]
 16%|█▌        | 598/3818 [04:16<24:06,  2.23it/s]
 16%|█▌        | 599/3818 [04:17<24:06,  2.23it/s]
 16%|█▌        | 600/3818 [04:17<24:05,  2.23it/s]
 16%|█▌        | 601/3818 [04:18<24:04,  2.23it/s]
 16%|█▌        | 602/3818 [04:18<24:04,  2.23it/s]
 16%|█▌        | 603/3818 [04:19<24:05,  2.22it/s]
 16%|█▌        | 604/3818 [04:19<24:11,  2.21it/s]
 16%|█▌        | 605/3818 [04:20<24:09,  2.22it/s]
 16%|█▌        | 606/3818 [04:20<24:10,  2.21it/s]
 16%|█▌        | 607/3818 [04:21<24:08,  2.22it/s]
 16%|█▌        | 608/3818 [04:21<24:08,  2.22it/s]
 16%|█▌        | 609/3818 [04:21<24:05,  2.22it/s]
 16%|█▌        | 610/3818 [04:22<24:02,  2.22it/s]
 16%|█▌        | 611/3818 [04:22<24:01,  2.22it/s]
 16%|█▌        | 612/3818 [04:23<24:04,  2.22it/s]
 16%|█▌        | 613/3818 [04:23<24:04,  2.22it/s]
 16%|█▌        | 614/3818 [04:24<24:04,  2.22it/s]
 16%|█▌        | 615/3818 [04:24<24:05,  2.22it/s]
 16%|█▌        | 616/3818 [04:25<24:00,  2.22it/s]
 16%|█▌        | 617/3818 [04:25<24:04,  2.22it/s]
 16%|█▌        | 618/3818 [04:25<24:06,  2.21it/s]
 16%|█▌        | 619/3818 [04:26<24:07,  2.21it/s]
 16%|█▌        | 620/3818 [04:26<24:05,  2.21it/s]
 16%|█▋        | 621/3818 [04:27<24:04,  2.21it/s]
 16%|█▋        | 622/3818 [04:27<24:02,  2.22it/s]
 16%|█▋        | 623/3818 [04:28<24:00,  2.22it/s]
 16%|█▋        | 624/3818 [04:28<24:03,  2.21it/s]
 16%|█▋        | 625/3818 [04:29<24:03,  2.21it/s]
 16%|█▋        | 626/3818 [04:29<24:05,  2.21it/s]
 16%|█▋        | 627/3818 [04:30<24:03,  2.21it/s]
 16%|█▋        | 628/3818 [04:30<24:03,  2.21it/s]
 16%|█▋        | 629/3818 [04:30<24:02,  2.21it/s]
 17%|█▋        | 630/3818 [04:31<23:59,  2.21it/s]
 17%|█▋        | 631/3818 [04:31<24:01,  2.21it/s]
 17%|█▋        | 632/3818 [04:32<24:00,  2.21it/s]
 17%|█▋        | 633/3818 [04:32<24:01,  2.21it/s]
 17%|█▋        | 634/3818 [04:33<23:58,  2.21it/s]
 17%|█▋        | 635/3818 [04:33<23:57,  2.21it/s]
 17%|█▋        | 636/3818 [04:34<23:59,  2.21it/s]
 17%|█▋        | 637/3818 [04:34<23:56,  2.21it/s]
 17%|█▋        | 638/3818 [04:35<23:52,  2.22it/s]
 17%|█▋        | 639/3818 [04:35<23:56,  2.21it/s]
 17%|█▋        | 640/3818 [04:35<23:54,  2.22it/s]
 17%|█▋        | 641/3818 [04:36<23:54,  2.21it/s]
 17%|█▋        | 642/3818 [04:36<23:55,  2.21it/s]
 17%|█▋        | 643/3818 [04:37<23:55,  2.21it/s]
 17%|█▋        | 644/3818 [04:37<23:56,  2.21it/s]
 17%|█▋        | 645/3818 [04:38<23:50,  2.22it/s]
 17%|█▋        | 646/3818 [04:38<23:47,  2.22it/s]
 17%|█▋        | 647/3818 [04:39<23:51,  2.22it/s]
 17%|█▋        | 648/3818 [04:39<23:52,  2.21it/s]
 17%|█▋        | 649/3818 [04:39<23:51,  2.21it/s]
 17%|█▋        | 650/3818 [04:40<23:53,  2.21it/s]
 17%|█▋        | 651/3818 [04:40<23:53,  2.21it/s]
 17%|█▋        | 652/3818 [04:41<23:50,  2.21it/s]
 17%|█▋        | 653/3818 [04:41<23:52,  2.21it/s]
 17%|█▋        | 654/3818 [04:42<23:49,  2.21it/s]
 17%|█▋        | 655/3818 [04:42<23:51,  2.21it/s]
 17%|█▋        | 656/3818 [04:43<23:49,  2.21it/s]
 17%|█▋        | 657/3818 [04:43<23:46,  2.22it/s]
 17%|█▋        | 658/3818 [04:44<23:47,  2.21it/s]
 17%|█▋        | 659/3818 [04:44<23:39,  2.22it/s]
 17%|█▋        | 660/3818 [04:44<23:44,  2.22it/s]
 17%|█▋        | 661/3818 [04:45<23:48,  2.21it/s]
 17%|█▋        | 662/3818 [04:45<23:48,  2.21it/s]
 17%|█▋        | 663/3818 [04:46<23:48,  2.21it/s]
 17%|█▋        | 664/3818 [04:46<23:46,  2.21it/s]
 17%|█▋        | 665/3818 [04:47<23:38,  2.22it/s]
 17%|█▋        | 666/3818 [04:47<23:38,  2.22it/s]
 17%|█▋        | 667/3818 [04:48<23:43,  2.21it/s]
 17%|█▋        | 668/3818 [04:48<23:43,  2.21it/s]
 18%|█▊        | 669/3818 [04:49<23:49,  2.20it/s]
 18%|█▊        | 670/3818 [04:49<23:43,  2.21it/s]
 18%|█▊        | 671/3818 [04:49<23:43,  2.21it/s]
 18%|█▊        | 672/3818 [04:50<23:45,  2.21it/s]
 18%|█▊        | 673/3818 [04:50<23:44,  2.21it/s]
 18%|█▊        | 674/3818 [04:51<23:47,  2.20it/s]
 18%|█▊        | 675/3818 [04:51<23:44,  2.21it/s]
 18%|█▊        | 676/3818 [04:52<23:45,  2.20it/s]
 18%|█▊        | 677/3818 [04:52<23:38,  2.21it/s]
 18%|█▊        | 678/3818 [04:53<23:39,  2.21it/s]
 18%|█▊        | 679/3818 [04:53<23:39,  2.21it/s]
 18%|█▊        | 680/3818 [04:54<23:37,  2.21it/s]
 18%|█▊        | 681/3818 [04:54<23:38,  2.21it/s]
 18%|█▊        | 682/3818 [04:54<23:35,  2.22it/s]
 18%|█▊        | 683/3818 [04:55<23:38,  2.21it/s]
 18%|█▊        | 684/3818 [04:55<23:39,  2.21it/s]
 18%|█▊        | 685/3818 [04:56<23:37,  2.21it/s]
 18%|█▊        | 686/3818 [04:56<23:37,  2.21it/s]
 18%|█▊        | 687/3818 [04:57<23:36,  2.21it/s]
 18%|█▊        | 688/3818 [04:57<23:39,  2.21it/s]
 18%|█▊        | 689/3818 [04:58<23:35,  2.21it/s]
 18%|█▊        | 690/3818 [04:58<23:37,  2.21it/s]
 18%|█▊        | 691/3818 [04:58<23:34,  2.21it/s]
 18%|█▊        | 692/3818 [04:59<23:32,  2.21it/s]
 18%|█▊        | 693/3818 [04:59<23:32,  2.21it/s]
 18%|█▊        | 694/3818 [05:00<23:33,  2.21it/s]
 18%|█▊        | 695/3818 [05:00<23:37,  2.20it/s]
 18%|█▊        | 696/3818 [05:01<23:35,  2.21it/s]
 18%|█▊        | 697/3818 [05:01<23:35,  2.21it/s]
 18%|█▊        | 698/3818 [05:02<23:34,  2.21it/s]
 18%|█▊        | 699/3818 [05:02<23:36,  2.20it/s]
 18%|█▊        | 700/3818 [05:03<23:59,  2.17it/s]
 18%|█▊        | 701/3818 [05:03<23:50,  2.18it/s]
 18%|█▊        | 702/3818 [05:03<23:47,  2.18it/s]
 18%|█▊        | 703/3818 [05:04<23:35,  2.20it/s]
 18%|█▊        | 704/3818 [05:04<23:40,  2.19it/s]
 18%|█▊        | 705/3818 [05:05<23:32,  2.20it/s]
 18%|█▊        | 706/3818 [05:05<23:35,  2.20it/s]
 19%|█▊        | 707/3818 [05:06<23:32,  2.20it/s]
 19%|█▊        | 708/3818 [05:06<23:33,  2.20it/s]
 19%|█▊        | 709/3818 [05:07<23:28,  2.21it/s]
 19%|█▊        | 710/3818 [05:07<23:29,  2.20it/s]
 19%|█▊        | 711/3818 [05:08<23:30,  2.20it/s]
 19%|█▊        | 712/3818 [05:08<23:32,  2.20it/s]
 19%|█▊        | 713/3818 [05:08<23:35,  2.19it/s]
 19%|█▊        | 714/3818 [05:09<23:47,  2.17it/s]
 19%|█▊        | 715/3818 [05:09<23:47,  2.17it/s]
 19%|█▉        | 716/3818 [05:10<23:34,  2.19it/s]
 19%|█▉        | 717/3818 [05:10<23:29,  2.20it/s]
 19%|█▉        | 718/3818 [05:11<23:31,  2.20it/s]
 19%|█▉        | 719/3818 [05:11<23:27,  2.20it/s]
 19%|█▉        | 720/3818 [05:12<23:28,  2.20it/s]
 19%|█▉        | 721/3818 [05:12<23:20,  2.21it/s]
 19%|█▉        | 722/3818 [05:13<23:34,  2.19it/s]
 19%|█▉        | 723/3818 [05:13<23:29,  2.20it/s]
 19%|█▉        | 724/3818 [05:14<23:27,  2.20it/s]
 19%|█▉        | 725/3818 [05:14<23:21,  2.21it/s]
 19%|█▉        | 726/3818 [05:14<23:25,  2.20it/s]
 19%|█▉        | 727/3818 [05:15<23:21,  2.21it/s]
 19%|█▉        | 728/3818 [05:15<23:25,  2.20it/s]
 19%|█▉        | 729/3818 [05:16<23:28,  2.19it/s]
 19%|█▉        | 730/3818 [05:16<23:26,  2.20it/s]
 19%|█▉        | 731/3818 [05:17<23:28,  2.19it/s]
 19%|█▉        | 732/3818 [05:17<23:24,  2.20it/s]
 19%|█▉        | 733/3818 [05:18<23:19,  2.20it/s]
 19%|█▉        | 734/3818 [05:18<23:17,  2.21it/s]
 19%|█▉        | 735/3818 [05:19<23:19,  2.20it/s]
 19%|█▉        | 736/3818 [05:19<23:20,  2.20it/s]
 19%|█▉        | 737/3818 [05:19<23:21,  2.20it/s]
 19%|█▉        | 738/3818 [05:20<23:16,  2.21it/s]
 19%|█▉        | 739/3818 [05:20<23:16,  2.21it/s]
 19%|█▉        | 740/3818 [05:21<23:17,  2.20it/s]
 19%|█▉        | 741/3818 [05:21<23:18,  2.20it/s]
 19%|█▉        | 742/3818 [05:22<23:17,  2.20it/s]
 19%|█▉        | 743/3818 [05:22<23:18,  2.20it/s]
 19%|█▉        | 744/3818 [05:23<24:32,  2.09it/s]
 20%|█▉        | 745/3818 [05:23<24:14,  2.11it/s]
 20%|█▉        | 746/3818 [05:24<24:00,  2.13it/s]
 20%|█▉        | 747/3818 [05:24<23:45,  2.15it/s]
 20%|█▉        | 748/3818 [05:24<23:36,  2.17it/s]
 20%|█▉        | 749/3818 [05:25<23:24,  2.18it/s]
 20%|█▉        | 750/3818 [05:25<23:29,  2.18it/s]
 20%|█▉        | 751/3818 [05:26<23:22,  2.19it/s]
 20%|█▉        | 752/3818 [05:26<23:24,  2.18it/s]
 20%|█▉        | 753/3818 [05:27<23:18,  2.19it/s]
 20%|█▉        | 754/3818 [05:27<23:16,  2.19it/s]
 20%|█▉        | 755/3818 [05:28<23:15,  2.20it/s]
 20%|█▉        | 756/3818 [05:28<23:13,  2.20it/s]
 20%|█▉        | 757/3818 [05:29<23:16,  2.19it/s]
 20%|█▉        | 758/3818 [05:29<23:10,  2.20it/s]
 20%|█▉        | 759/3818 [05:30<23:15,  2.19it/s]
 20%|█▉        | 760/3818 [05:30<23:11,  2.20it/s]
 20%|█▉        | 761/3818 [05:30<23:11,  2.20it/s]
 20%|█▉        | 762/3818 [05:31<23:09,  2.20it/s]
 20%|█▉        | 763/3818 [05:31<23:13,  2.19it/s]
 20%|██        | 764/3818 [05:32<23:15,  2.19it/s]
 20%|██        | 765/3818 [05:32<23:08,  2.20it/s]
 20%|██        | 766/3818 [05:33<23:21,  2.18it/s]
 20%|██        | 767/3818 [05:33<23:16,  2.18it/s]
 20%|██        | 768/3818 [05:34<23:12,  2.19it/s]
 20%|██        | 769/3818 [05:34<23:11,  2.19it/s]
 20%|██        | 770/3818 [05:35<23:09,  2.19it/s]
 20%|██        | 771/3818 [05:35<23:09,  2.19it/s]
 20%|██        | 772/3818 [05:35<23:09,  2.19it/s]
 20%|██        | 773/3818 [05:36<23:03,  2.20it/s]
 20%|██        | 774/3818 [05:36<23:04,  2.20it/s]
 20%|██        | 775/3818 [05:37<23:07,  2.19it/s]
 20%|██        | 776/3818 [05:37<23:05,  2.20it/s]
 20%|██        | 777/3818 [05:38<23:04,  2.20it/s]
 20%|██        | 778/3818 [05:38<23:07,  2.19it/s]
 20%|██        | 779/3818 [05:39<23:06,  2.19it/s]
 20%|██        | 780/3818 [05:39<23:05,  2.19it/s]
 20%|██        | 781/3818 [05:40<23:02,  2.20it/s]
 20%|██        | 782/3818 [05:40<23:05,  2.19it/s]
 21%|██        | 783/3818 [05:40<23:00,  2.20it/s]
 21%|██        | 784/3818 [05:41<23:00,  2.20it/s]
 21%|██        | 785/3818 [05:41<22:57,  2.20it/s]
 21%|██        | 786/3818 [05:42<22:56,  2.20it/s]
 21%|██        | 787/3818 [05:42<23:00,  2.20it/s]
 21%|██        | 788/3818 [05:43<22:59,  2.20it/s]
 21%|██        | 789/3818 [05:43<23:01,  2.19it/s]
 21%|██        | 790/3818 [05:44<22:59,  2.19it/s]
 21%|██        | 791/3818 [05:44<23:02,  2.19it/s]
 21%|██        | 792/3818 [05:45<22:58,  2.20it/s]
 21%|██        | 793/3818 [05:45<22:56,  2.20it/s]
 21%|██        | 794/3818 [05:45<22:55,  2.20it/s]
 21%|██        | 795/3818 [05:46<22:56,  2.20it/s]
 21%|██        | 796/3818 [05:46<22:57,  2.19it/s]
 21%|██        | 797/3818 [05:47<22:54,  2.20it/s]
 21%|██        | 798/3818 [05:47<22:54,  2.20it/s]
 21%|██        | 799/3818 [05:48<23:10,  2.17it/s]
 21%|██        | 800/3818 [05:48<23:09,  2.17it/s]
 21%|██        | 801/3818 [05:49<23:01,  2.18it/s]
 21%|██        | 802/3818 [05:49<22:57,  2.19it/s]
 21%|██        | 803/3818 [05:50<22:58,  2.19it/s]
 21%|██        | 804/3818 [05:50<22:55,  2.19it/s]
 21%|██        | 805/3818 [05:50<22:56,  2.19it/s]
 21%|██        | 806/3818 [05:51<22:55,  2.19it/s]
 21%|██        | 807/3818 [05:51<22:56,  2.19it/s]
 21%|██        | 808/3818 [05:52<22:53,  2.19it/s]
 21%|██        | 809/3818 [05:52<22:53,  2.19it/s]
 21%|██        | 810/3818 [05:53<22:51,  2.19it/s]
 21%|██        | 811/3818 [05:53<22:52,  2.19it/s]
 21%|██▏       | 812/3818 [05:54<22:50,  2.19it/s]
 21%|██▏       | 813/3818 [05:54<22:53,  2.19it/s]
 21%|██▏       | 814/3818 [05:55<22:51,  2.19it/s]
 21%|██▏       | 815/3818 [05:55<22:47,  2.20it/s]
 21%|██▏       | 816/3818 [05:56<22:50,  2.19it/s]
 21%|██▏       | 817/3818 [05:56<22:47,  2.19it/s]
 21%|██▏       | 818/3818 [05:56<22:47,  2.19it/s]
 21%|██▏       | 819/3818 [05:57<22:39,  2.21it/s]
 21%|██▏       | 820/3818 [05:57<22:44,  2.20it/s]
 22%|██▏       | 821/3818 [05:58<22:42,  2.20it/s]
 22%|██▏       | 822/3818 [05:58<22:41,  2.20it/s]
 22%|██▏       | 823/3818 [05:59<22:42,  2.20it/s]
 22%|██▏       | 824/3818 [05:59<22:46,  2.19it/s]
 22%|██▏       | 825/3818 [06:00<22:43,  2.19it/s]
 22%|██▏       | 826/3818 [06:00<22:46,  2.19it/s]
 22%|██▏       | 827/3818 [06:01<22:45,  2.19it/s]
 22%|██▏       | 828/3818 [06:01<22:42,  2.19it/s]
 22%|██▏       | 829/3818 [06:01<22:40,  2.20it/s]
 22%|██▏       | 830/3818 [06:02<22:34,  2.21it/s]
 22%|██▏       | 831/3818 [06:02<22:40,  2.20it/s]
 22%|██▏       | 832/3818 [06:03<22:44,  2.19it/s]
 22%|██▏       | 833/3818 [06:03<22:40,  2.19it/s]
 22%|██▏       | 834/3818 [06:04<22:41,  2.19it/s]
 22%|██▏       | 835/3818 [06:04<22:36,  2.20it/s]
 22%|██▏       | 836/3818 [06:05<22:37,  2.20it/s]
 22%|██▏       | 837/3818 [06:05<22:34,  2.20it/s]
 22%|██▏       | 838/3818 [06:06<22:38,  2.19it/s]
 22%|██▏       | 839/3818 [06:06<22:35,  2.20it/s]
 22%|██▏       | 840/3818 [06:06<22:34,  2.20it/s]
 22%|██▏       | 841/3818 [06:07<22:32,  2.20it/s]
 22%|██▏       | 842/3818 [06:07<22:33,  2.20it/s]
 22%|██▏       | 843/3818 [06:08<22:41,  2.19it/s]
 22%|██▏       | 844/3818 [06:08<22:35,  2.19it/s]
 22%|██▏       | 845/3818 [06:09<22:34,  2.20it/s]
 22%|██▏       | 846/3818 [06:09<22:29,  2.20it/s]
 22%|██▏       | 847/3818 [06:10<22:29,  2.20it/s]
 22%|██▏       | 848/3818 [06:10<22:29,  2.20it/s]
 22%|██▏       | 849/3818 [06:11<22:31,  2.20it/s]
 22%|██▏       | 850/3818 [06:11<22:26,  2.20it/s]
 22%|██▏       | 851/3818 [06:11<22:23,  2.21it/s]
 22%|██▏       | 852/3818 [06:12<22:22,  2.21it/s]
 22%|██▏       | 853/3818 [06:12<22:20,  2.21it/s]
 22%|██▏       | 854/3818 [06:13<22:33,  2.19it/s]
 22%|██▏       | 855/3818 [06:13<22:32,  2.19it/s]
 22%|██▏       | 856/3818 [06:14<22:29,  2.19it/s]
 22%|██▏       | 857/3818 [06:14<22:23,  2.20it/s]
 22%|██▏       | 858/3818 [06:15<22:29,  2.19it/s]
 22%|██▏       | 859/3818 [06:15<22:21,  2.20it/s]
 23%|██▎       | 860/3818 [06:16<22:23,  2.20it/s]
 23%|██▎       | 861/3818 [06:16<22:19,  2.21it/s]
 23%|██▎       | 862/3818 [06:16<22:20,  2.21it/s]
 23%|██▎       | 863/3818 [06:17<22:20,  2.21it/s]
 23%|██▎       | 864/3818 [06:17<22:21,  2.20it/s]
 23%|██▎       | 865/3818 [06:18<22:29,  2.19it/s]
 23%|██▎       | 866/3818 [06:18<22:29,  2.19it/s]
 23%|██▎       | 867/3818 [06:19<22:25,  2.19it/s]
 23%|██▎       | 868/3818 [06:19<22:23,  2.20it/s]
 23%|██▎       | 869/3818 [06:20<22:21,  2.20it/s]
 23%|██▎       | 870/3818 [06:20<22:20,  2.20it/s]
 23%|██▎       | 871/3818 [06:21<22:18,  2.20it/s]
 23%|██▎       | 872/3818 [06:21<22:13,  2.21it/s]
 23%|██▎       | 873/3818 [06:21<22:13,  2.21it/s]
 23%|██▎       | 874/3818 [06:22<22:09,  2.21it/s]
 23%|██▎       | 875/3818 [06:22<22:18,  2.20it/s]
 23%|██▎       | 876/3818 [06:23<22:15,  2.20it/s]
 23%|██▎       | 877/3818 [06:23<22:12,  2.21it/s]
 23%|██▎       | 878/3818 [06:24<22:14,  2.20it/s]
 23%|██▎       | 879/3818 [06:24<22:14,  2.20it/s]
 23%|██▎       | 880/3818 [06:25<22:14,  2.20it/s]
 23%|██▎       | 881/3818 [06:25<22:13,  2.20it/s]