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2024-09-06 00:08:37.934804: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-09-06 00:08:37.953090: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-09-06 00:08:37.974597: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-09-06 00:08:37.981013: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-09-06 00:08:37.996733: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-09-06 00:08:39.252831: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of πŸ€— Transformers. Use `eval_strategy` instead
warnings.warn(
09/06/2024 00:08:40 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/06/2024 00:08:40 - INFO - __main__ - Training/evaluation parameters TrainingArguments(
_n_gpu=1,
accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None, 'use_configured_state': False},
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
batch_eval_metrics=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_persistent_workers=False,
dataloader_pin_memory=True,
dataloader_prefetch_factor=None,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
dispatch_batches=None,
do_eval=True,
do_predict=True,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_do_concat_batches=True,
eval_on_start=False,
eval_steps=None,
eval_strategy=epoch,
eval_use_gather_object=False,
evaluation_strategy=epoch,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
gradient_accumulation_steps=2,
gradient_checkpointing=False,
gradient_checkpointing_kwargs=None,
greater_is_better=True,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_num_input_tokens_seen=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=5e-05,
length_column_name=length,
load_best_model_at_end=True,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=/content/dissertation/scripts/ner/output/tb,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=500,
logging_strategy=steps,
lr_scheduler_kwargs={},
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=f1,
mp_parameters=,
neftune_noise_alpha=None,
no_cuda=False,
num_train_epochs=10.0,
optim=adamw_torch,
optim_args=None,
optim_target_modules=None,
output_dir=/content/dissertation/scripts/ner/output,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=32,
prediction_loss_only=False,
push_to_hub=True,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=['tensorboard'],
restore_callback_states_from_checkpoint=False,
resume_from_checkpoint=None,
run_name=/content/dissertation/scripts/ner/output,
save_on_each_node=False,
save_only_model=False,
save_safetensors=True,
save_steps=500,
save_strategy=epoch,
save_total_limit=None,
seed=42,
skip_memory_metrics=True,
split_batches=None,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torch_empty_cache_steps=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
)
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[INFO|configuration_utils.py:733] 2024-09-06 00:08:59,880 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
[INFO|configuration_utils.py:800] 2024-09-06 00:08:59,883 >> Model config RobertaConfig {
"_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
"architectures": [
"RobertaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"finetuning_task": "ner",
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "O",
"1": "B-ENFERMEDAD",
"2": "I-ENFERMEDAD",
"3": "B-PROCEDIMIENTO",
"4": "I-PROCEDIMIENTO",
"5": "B-SINTOMA",
"6": "I-SINTOMA",
"7": "B-FARMACO",
"8": "I-FARMACO"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"B-ENFERMEDAD": 1,
"B-FARMACO": 7,
"B-PROCEDIMIENTO": 3,
"B-SINTOMA": 5,
"I-ENFERMEDAD": 2,
"I-FARMACO": 8,
"I-PROCEDIMIENTO": 4,
"I-SINTOMA": 6,
"O": 0
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.44.2",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50262
}
[INFO|configuration_utils.py:733] 2024-09-06 00:09:00,140 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
[INFO|configuration_utils.py:800] 2024-09-06 00:09:00,140 >> Model config RobertaConfig {
"_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
"architectures": [
"RobertaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.44.2",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50262
}
[INFO|tokenization_utils_base.py:2269] 2024-09-06 00:09:00,153 >> loading file vocab.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/vocab.json
[INFO|tokenization_utils_base.py:2269] 2024-09-06 00:09:00,153 >> loading file merges.txt from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/merges.txt
[INFO|tokenization_utils_base.py:2269] 2024-09-06 00:09:00,153 >> loading file tokenizer.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-06 00:09:00,153 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-06 00:09:00,153 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/special_tokens_map.json
[INFO|tokenization_utils_base.py:2269] 2024-09-06 00:09:00,153 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/tokenizer_config.json
[INFO|configuration_utils.py:733] 2024-09-06 00:09:00,153 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
[INFO|configuration_utils.py:800] 2024-09-06 00:09:00,154 >> Model config RobertaConfig {
"_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
"architectures": [
"RobertaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.44.2",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50262
}
/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
warnings.warn(
[INFO|configuration_utils.py:733] 2024-09-06 00:09:00,236 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
[INFO|configuration_utils.py:800] 2024-09-06 00:09:00,238 >> Model config RobertaConfig {
"_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
"architectures": [
"RobertaForMaskedLM"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"gradient_checkpointing": false,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"transformers_version": "4.44.2",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50262
}
[INFO|modeling_utils.py:3678] 2024-09-06 00:09:00,573 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/pytorch_model.bin
[INFO|modeling_utils.py:4497] 2024-09-06 00:09:00,651 >> Some weights of the model checkpoint at PlanTL-GOB-ES/bsc-bio-ehr-es were not used when initializing RobertaForTokenClassification: ['lm_head.bias', 'lm_head.decoder.bias', 'lm_head.decoder.weight', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight']
- This IS expected if you are initializing RobertaForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing RobertaForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
[WARNING|modeling_utils.py:4509] 2024-09-06 00:09:00,651 >> Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at PlanTL-GOB-ES/bsc-bio-ehr-es and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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/content/dissertation/scripts/ner/run_ner_train.py:397: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library πŸ€— Evaluate: https://huggingface.co/docs/evaluate
metric = load_metric("seqeval", trust_remote_code=True)
[INFO|trainer.py:811] 2024-09-06 00:09:07,817 >> The following columns in the training set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
[INFO|trainer.py:2134] 2024-09-06 00:09:08,371 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-06 00:09:08,371 >> Num examples = 34,604
[INFO|trainer.py:2136] 2024-09-06 00:09:08,371 >> Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-06 00:09:08,371 >> Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-06 00:09:08,371 >> Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-06 00:09:08,371 >> Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-06 00:09:08,371 >> Total optimization steps = 5,410
[INFO|trainer.py:2143] 2024-09-06 00:09:08,372 >> Number of trainable parameters = 124,059,657
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4.76it/s] 4%|▍ | 226/5410 [00:57<18:19, 4.72it/s] 4%|▍ | 227/5410 [00:57<18:07, 4.77it/s] 4%|▍ | 228/5410 [00:58<19:53, 4.34it/s] 4%|▍ | 229/5410 [00:58<20:59, 4.11it/s] 4%|▍ | 230/5410 [00:58<20:43, 4.17it/s] 4%|▍ | 231/5410 [00:58<19:19, 4.47it/s] 4%|▍ | 232/5410 [00:59<20:24, 4.23it/s] 4%|▍ | 233/5410 [00:59<19:32, 4.41it/s] 4%|▍ | 234/5410 [00:59<20:52, 4.13it/s] 4%|▍ | 235/5410 [00:59<19:19, 4.46it/s] 4%|▍ | 236/5410 [01:00<20:38, 4.18it/s] 4%|▍ | 237/5410 [01:00<20:05, 4.29it/s] 4%|▍ | 238/5410 [01:00<21:14, 4.06it/s] 4%|▍ | 239/5410 [01:00<20:24, 4.22it/s] 4%|▍ | 240/5410 [01:01<20:00, 4.31it/s] 4%|▍ | 241/5410 [01:01<32:09, 2.68it/s] 4%|▍ | 242/5410 [01:02<30:23, 2.83it/s] 4%|▍ | 243/5410 [01:02<30:24, 2.83it/s] 5%|▍ | 244/5410 [01:02<27:11, 3.17it/s] 5%|▍ | 245/5410 [01:02<25:58, 3.31it/s] 5%|▍ | 246/5410 [01:03<25:30, 3.37it/s] 5%|▍ | 247/5410 [01:03<26:02, 3.31it/s] 5%|▍ | 248/5410 [01:03<27:39, 3.11it/s] 5%|▍ | 249/5410 [01:04<24:09, 3.56it/s] 5%|▍ | 250/5410 [01:04<21:10, 4.06it/s] 5%|▍ | 251/5410 [01:04<21:25, 4.01it/s] 5%|▍ | 252/5410 [01:04<20:45, 4.14it/s] 5%|▍ | 253/5410 [01:04<21:34, 3.98it/s] 5%|▍ | 254/5410 [01:05<20:53, 4.11it/s] 5%|▍ | 255/5410 [01:05<20:27, 4.20it/s] 5%|▍ | 256/5410 [01:05<20:24, 4.21it/s] 5%|▍ | 257/5410 [01:05<20:09, 4.26it/s] 5%|▍ | 258/5410 [01:06<19:00, 4.52it/s] 5%|▍ | 259/5410 [01:06<18:36, 4.61it/s] 5%|▍ | 260/5410 [01:06<20:09, 4.26it/s] 5%|▍ | 261/5410 [01:06<22:16, 3.85it/s] 5%|▍ | 262/5410 [01:07<21:29, 3.99it/s] 5%|▍ | 263/5410 [01:07<22:15, 3.86it/s] 5%|▍ | 264/5410 [01:07<22:15, 3.85it/s] 5%|▍ | 265/5410 [01:07<22:25, 3.82it/s] 5%|▍ | 266/5410 [01:08<21:33, 3.98it/s] 5%|▍ | 267/5410 [01:08<20:28, 4.19it/s] 5%|▍ | 268/5410 [01:08<20:06, 4.26it/s] 5%|▍ | 269/5410 [01:08<22:14, 3.85it/s] 5%|▍ | 270/5410 [01:09<21:09, 4.05it/s] 5%|β–Œ | 271/5410 [01:09<19:16, 4.44it/s] 5%|β–Œ | 272/5410 [01:09<22:35, 3.79it/s] 5%|β–Œ | 273/5410 [01:09<20:40, 4.14it/s] 5%|β–Œ | 274/5410 [01:09<19:25, 4.41it/s] 5%|β–Œ | 275/5410 [01:10<18:52, 4.54it/s] 5%|β–Œ | 276/5410 [01:10<18:20, 4.66it/s] 5%|β–Œ | 277/5410 [01:10<17:55, 4.77it/s] 5%|β–Œ | 278/5410 [01:10<19:54, 4.30it/s] 5%|β–Œ | 279/5410 [01:11<19:16, 4.44it/s] 5%|β–Œ | 280/5410 [01:11<21:40, 3.95it/s] 5%|β–Œ | 281/5410 [01:11<21:06, 4.05it/s] 5%|β–Œ | 282/5410 [01:12<24:46, 3.45it/s] 5%|β–Œ | 283/5410 [01:12<23:25, 3.65it/s] 5%|β–Œ | 284/5410 [01:12<21:42, 3.94it/s] 5%|β–Œ | 285/5410 [01:12<22:28, 3.80it/s] 5%|β–Œ | 286/5410 [01:12<20:07, 4.24it/s] 5%|β–Œ | 287/5410 [01:13<21:38, 3.95it/s] 5%|β–Œ | 288/5410 [01:13<21:41, 3.93it/s] 5%|β–Œ | 289/5410 [01:13<21:05, 4.05it/s] 5%|β–Œ | 290/5410 [01:13<21:43, 3.93it/s] 5%|β–Œ | 291/5410 [01:14<21:16, 4.01it/s] 5%|β–Œ | 292/5410 [01:14<22:53, 3.73it/s] 5%|β–Œ | 293/5410 [01:14<23:09, 3.68it/s] 5%|β–Œ | 294/5410 [01:15<22:52, 3.73it/s] 5%|β–Œ | 295/5410 [01:15<22:31, 3.78it/s] 5%|β–Œ | 296/5410 [01:15<21:38, 3.94it/s] 5%|β–Œ | 297/5410 [01:15<20:29, 4.16it/s] 6%|β–Œ | 298/5410 [01:15<19:46, 4.31it/s] 6%|β–Œ | 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6%|β–Œ | 323/5410 [01:22<20:25, 4.15it/s] 6%|β–Œ | 324/5410 [01:22<20:05, 4.22it/s] 6%|β–Œ | 325/5410 [01:22<18:12, 4.66it/s] 6%|β–Œ | 326/5410 [01:22<18:52, 4.49it/s] 6%|β–Œ | 327/5410 [01:23<19:50, 4.27it/s] 6%|β–Œ | 328/5410 [01:23<18:32, 4.57it/s] 6%|β–Œ | 329/5410 [01:23<18:52, 4.49it/s] 6%|β–Œ | 330/5410 [01:23<18:56, 4.47it/s] 6%|β–Œ | 331/5410 [01:24<20:49, 4.07it/s] 6%|β–Œ | 332/5410 [01:24<20:11, 4.19it/s] 6%|β–Œ | 333/5410 [01:24<20:41, 4.09it/s] 6%|β–Œ | 334/5410 [01:24<20:59, 4.03it/s] 6%|β–Œ | 335/5410 [01:25<21:19, 3.97it/s] 6%|β–Œ | 336/5410 [01:25<20:56, 4.04it/s] 6%|β–Œ | 337/5410 [01:25<21:43, 3.89it/s] 6%|β–Œ | 338/5410 [01:25<24:03, 3.51it/s] 6%|β–‹ | 339/5410 [01:26<24:53, 3.39it/s] 6%|β–‹ | 340/5410 [01:26<24:13, 3.49it/s] 6%|β–‹ | 341/5410 [01:26<23:02, 3.67it/s] 6%|β–‹ | 342/5410 [01:27<23:50, 3.54it/s] 6%|β–‹ | 343/5410 [01:27<26:01, 3.24it/s] 6%|β–‹ | 344/5410 [01:27<22:43, 3.72it/s] 6%|β–‹ | 345/5410 [01:27<24:18, 3.47it/s] 6%|β–‹ | 346/5410 [01:28<23:45, 3.55it/s] 6%|β–‹ | 347/5410 [01:28<21:22, 3.95it/s] 6%|β–‹ | 348/5410 [01:28<20:16, 4.16it/s] 6%|β–‹ | 349/5410 [01:28<21:42, 3.88it/s] 6%|β–‹ | 350/5410 [01:29<20:03, 4.20it/s] 6%|β–‹ | 351/5410 [01:29<22:11, 3.80it/s] 7%|β–‹ | 352/5410 [01:29<21:50, 3.86it/s] 7%|β–‹ | 353/5410 [01:29<20:18, 4.15it/s] 7%|β–‹ | 354/5410 [01:30<20:35, 4.09it/s] 7%|β–‹ | 355/5410 [01:30<22:38, 3.72it/s] 7%|β–‹ | 356/5410 [01:30<24:41, 3.41it/s] 7%|β–‹ | 357/5410 [01:31<23:58, 3.51it/s] 7%|β–‹ | 358/5410 [01:31<25:43, 3.27it/s] 7%|β–‹ | 359/5410 [01:31<24:02, 3.50it/s] 7%|β–‹ | 360/5410 [01:31<23:11, 3.63it/s] 7%|β–‹ | 361/5410 [01:32<22:19, 3.77it/s] 7%|β–‹ | 362/5410 [01:32<23:53, 3.52it/s] 7%|β–‹ | 363/5410 [01:32<22:17, 3.77it/s] 7%|β–‹ | 364/5410 [01:32<21:41, 3.88it/s] 7%|β–‹ | 365/5410 [01:33<21:30, 3.91it/s] 7%|β–‹ | 366/5410 [01:33<19:34, 4.29it/s] 7%|β–‹ | 367/5410 [01:33<19:13, 4.37it/s] 7%|β–‹ | 368/5410 [01:33<19:50, 4.23it/s] 7%|β–‹ | 369/5410 [01:34<18:53, 4.45it/s] 7%|β–‹ | 370/5410 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8%|β–Š | 418/5410 [01:46<21:16, 3.91it/s] 8%|β–Š | 419/5410 [01:46<20:46, 4.00it/s] 8%|β–Š | 420/5410 [01:47<20:30, 4.05it/s] 8%|β–Š | 421/5410 [01:47<22:46, 3.65it/s] 8%|β–Š | 422/5410 [01:47<19:57, 4.17it/s] 8%|β–Š | 423/5410 [01:47<18:42, 4.44it/s] 8%|β–Š | 424/5410 [01:48<17:53, 4.65it/s] 8%|β–Š | 425/5410 [01:48<17:43, 4.69it/s] 8%|β–Š | 426/5410 [01:48<18:12, 4.56it/s] 8%|β–Š | 427/5410 [01:48<17:29, 4.75it/s] 8%|β–Š | 428/5410 [01:48<18:22, 4.52it/s] 8%|β–Š | 429/5410 [01:49<21:50, 3.80it/s] 8%|β–Š | 430/5410 [01:49<23:28, 3.54it/s] 8%|β–Š | 431/5410 [01:49<23:45, 3.49it/s] 8%|β–Š | 432/5410 [01:50<21:21, 3.89it/s] 8%|β–Š | 433/5410 [01:50<22:22, 3.71it/s] 8%|β–Š | 434/5410 [01:50<20:57, 3.96it/s] 8%|β–Š | 435/5410 [01:50<20:26, 4.06it/s] 8%|β–Š | 436/5410 [01:51<20:08, 4.12it/s] 8%|β–Š | 437/5410 [01:51<18:59, 4.37it/s] 8%|β–Š | 438/5410 [01:51<20:03, 4.13it/s] 8%|β–Š | 439/5410 [01:51<20:14, 4.09it/s] 8%|β–Š | 440/5410 [01:52<26:54, 3.08it/s] 8%|β–Š | 441/5410 [01:52<24:06, 3.44it/s] 8%|β–Š | 442/5410 [01:52<23:37, 3.50it/s] 8%|β–Š | 443/5410 [01:52<21:10, 3.91it/s] 8%|β–Š | 444/5410 [01:53<20:28, 4.04it/s] 8%|β–Š | 445/5410 [01:53<20:39, 4.01it/s] 8%|β–Š | 446/5410 [01:53<18:55, 4.37it/s] 8%|β–Š | 447/5410 [01:53<18:48, 4.40it/s] 8%|β–Š | 448/5410 [01:54<17:53, 4.62it/s] 8%|β–Š | 449/5410 [01:54<18:59, 4.35it/s] 8%|β–Š | 450/5410 [01:54<17:45, 4.66it/s] 8%|β–Š | 451/5410 [01:54<18:05, 4.57it/s] 8%|β–Š | 452/5410 [01:54<18:26, 4.48it/s] 8%|β–Š | 453/5410 [01:55<18:09, 4.55it/s] 8%|β–Š | 454/5410 [01:55<19:59, 4.13it/s] 8%|β–Š | 455/5410 [01:55<18:57, 4.36it/s] 8%|β–Š | 456/5410 [01:55<19:55, 4.14it/s] 8%|β–Š | 457/5410 [01:56<19:56, 4.14it/s] 8%|β–Š | 458/5410 [01:56<20:43, 3.98it/s] 8%|β–Š | 459/5410 [01:56<20:32, 4.02it/s] 9%|β–Š | 460/5410 [01:56<19:30, 4.23it/s] 9%|β–Š | 461/5410 [01:57<20:35, 4.01it/s] 9%|β–Š | 462/5410 [01:57<19:14, 4.29it/s] 9%|β–Š | 463/5410 [01:57<20:20, 4.05it/s] 9%|β–Š | 464/5410 [01:57<20:06, 4.10it/s] 9%|β–Š | 465/5410 [01:58<18:18, 4.50it/s] 9%|β–Š | 466/5410 [01:58<19:26, 4.24it/s] 9%|β–Š | 467/5410 [01:58<19:22, 4.25it/s] 9%|β–Š | 468/5410 [01:58<18:21, 4.49it/s] 9%|β–Š | 469/5410 [01:59<19:02, 4.32it/s] 9%|β–Š | 470/5410 [01:59<18:01, 4.57it/s] 9%|β–Š | 471/5410 [01:59<18:19, 4.49it/s] 9%|β–Š | 472/5410 [01:59<18:22, 4.48it/s] 9%|β–Š | 473/5410 [01:59<19:14, 4.28it/s] 9%|β–‰ | 474/5410 [02:00<17:52, 4.60it/s] 9%|β–‰ | 475/5410 [02:00<17:45, 4.63it/s] 9%|β–‰ | 476/5410 [02:00<16:43, 4.92it/s] 9%|β–‰ | 477/5410 [02:00<15:59, 5.14it/s] 9%|β–‰ | 478/5410 [02:00<17:10, 4.78it/s] 9%|β–‰ | 479/5410 [02:01<21:36, 3.80it/s] 9%|β–‰ | 480/5410 [02:01<19:27, 4.22it/s] 9%|β–‰ | 481/5410 [02:01<18:47, 4.37it/s] 9%|β–‰ | 482/5410 [02:01<20:52, 3.93it/s] 9%|β–‰ | 483/5410 [02:02<19:55, 4.12it/s] 9%|β–‰ | 484/5410 [02:02<20:17, 4.05it/s] 9%|β–‰ | 485/5410 [02:02<22:35, 3.63it/s] 9%|β–‰ | 486/5410 [02:03<20:42, 3.96it/s] 9%|β–‰ | 487/5410 [02:03<18:48, 4.36it/s] 9%|β–‰ | 488/5410 [02:03<17:18, 4.74it/s] 9%|β–‰ | 489/5410 [02:03<18:11, 4.51it/s] 9%|β–‰ | 490/5410 [02:03<17:36, 4.66it/s] 9%|β–‰ | 491/5410 [02:03<17:10, 4.77it/s] 9%|β–‰ | 492/5410 [02:04<17:35, 4.66it/s] 9%|β–‰ | 493/5410 [02:04<19:26, 4.22it/s] 9%|β–‰ | 494/5410 [02:04<18:42, 4.38it/s] 9%|β–‰ | 495/5410 [02:05<21:45, 3.76it/s] 9%|β–‰ | 496/5410 [02:05<22:22, 3.66it/s] 9%|β–‰ | 497/5410 [02:05<20:36, 3.97it/s] 9%|β–‰ | 498/5410 [02:05<21:18, 3.84it/s] 9%|β–‰ | 499/5410 [02:06<19:06, 4.28it/s] 9%|β–‰ | 500/5410 [02:06<18:29, 4.43it/s] 9%|β–‰ | 500/5410 [02:06<18:29, 4.43it/s] 9%|β–‰ | 501/5410 [02:06<18:43, 4.37it/s] 9%|β–‰ | 502/5410 [02:06<18:29, 4.42it/s] 9%|β–‰ | 503/5410 [02:06<17:26, 4.69it/s] 9%|β–‰ | 504/5410 [02:07<17:33, 4.66it/s] 9%|β–‰ | 505/5410 [02:07<17:53, 4.57it/s] 9%|β–‰ | 506/5410 [02:07<18:29, 4.42it/s] 9%|β–‰ | 507/5410 [02:07<18:50, 4.34it/s] 9%|β–‰ | 508/5410 [02:07<17:35, 4.64it/s] 9%|β–‰ | 509/5410 [02:08<16:20, 5.00it/s] 9%|β–‰ | 510/5410 [02:08<17:54, 4.56it/s] 9%|β–‰ | 511/5410 [02:08<18:53, 4.32it/s] 9%|β–‰ | 512/5410 [02:08<20:06, 4.06it/s] 9%|β–‰ | 513/5410 [02:09<18:22, 4.44it/s] 10%|β–‰ | 514/5410 [02:09<19:14, 4.24it/s] 10%|β–‰ | 515/5410 [02:09<19:57, 4.09it/s] 10%|β–‰ | 516/5410 [02:09<21:51, 3.73it/s] 10%|β–‰ | 517/5410 [02:10<21:01, 3.88it/s] 10%|β–‰ | 518/5410 [02:10<19:54, 4.10it/s] 10%|β–‰ | 519/5410 [02:10<19:24, 4.20it/s] 10%|β–‰ | 520/5410 [02:10<21:01, 3.88it/s] 10%|β–‰ | 521/5410 [02:11<23:02, 3.54it/s] 10%|β–‰ | 522/5410 [02:11<21:22, 3.81it/s] 10%|β–‰ | 523/5410 [02:11<21:17, 3.83it/s] 10%|β–‰ | 524/5410 [02:11<20:15, 4.02it/s] 10%|β–‰ | 525/5410 [02:12<19:22, 4.20it/s] 10%|β–‰ | 526/5410 [02:12<19:22, 4.20it/s] 10%|β–‰ | 527/5410 [02:12<19:21, 4.21it/s] 10%|β–‰ | 528/5410 [02:12<18:40, 4.36it/s] 10%|β–‰ | 529/5410 [02:13<19:47, 4.11it/s] 10%|β–‰ | 530/5410 [02:13<18:33, 4.38it/s] 10%|β–‰ | 531/5410 [02:13<19:05, 4.26it/s] 10%|β–‰ | 532/5410 [02:13<18:18, 4.44it/s] 10%|β–‰ | 533/5410 [02:14<19:46, 4.11it/s] 10%|β–‰ | 534/5410 [02:14<19:08, 4.25it/s] 10%|β–‰ | 535/5410 [02:14<17:33, 4.63it/s] 10%|β–‰ | 536/5410 [02:14<20:17, 4.00it/s] 10%|β–‰ | 537/5410 [02:15<19:11, 4.23it/s] 10%|β–‰ | 538/5410 [02:15<19:15, 4.22it/s] 10%|β–‰ | 539/5410 [02:15<19:19, 4.20it/s] 10%|β–‰ | 540/5410 [02:16<32:00, 2.54it/s] 10%|β–ˆ | 541/5410 [02:16<25:28, 3.18it/s][INFO|trainer.py:811] 2024-09-06 00:11:24,745 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`, you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-06 00:11:24,747 >>
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-06 00:11:24,747 >> Num examples = 6810
[INFO|trainer.py:3824] 2024-09-06 00:11:24,747 >> Batch size = 8
{'loss': 0.3191, 'grad_norm': 1.4528056383132935, 'learning_rate': 4.537892791127542e-05, 'epoch': 0.92}
0%| | 0/852 [00:00<?, ?it/s]
1%| | 10/852 [00:00<00:08, 94.22it/s]
2%|▏ | 20/852 [00:00<00:09, 84.30it/s]
3%|β–Ž | 29/852 [00:00<00:09, 82.68it/s]
4%|▍ | 38/852 [00:00<00:09, 81.67it/s]
6%|β–Œ | 47/852 [00:00<00:09, 82.09it/s]
7%|β–‹ | 56/852 [00:00<00:09, 83.32it/s]
8%|β–Š | 65/852 [00:00<00:09, 81.98it/s]
9%|β–Š | 74/852 [00:00<00:09, 80.45it/s]
10%|β–‰ | 83/852 [00:01<00:09, 79.95it/s]
11%|β–ˆ | 92/852 [00:01<00:09, 80.23it/s]
12%|β–ˆβ– | 101/852 [00:01<00:09, 79.90it/s]
13%|β–ˆβ–Ž | 109/852 [00:01<00:09, 79.80it/s]
14%|β–ˆβ– | 118/852 [00:01<00:09, 80.74it/s]
15%|β–ˆβ– | 127/852 [00:01<00:09, 76.94it/s]
16%|β–ˆβ–Œ | 135/852 [00:01<00:09, 77.57it/s]
17%|β–ˆβ–‹ | 143/852 [00:01<00:09, 77.84it/s]
18%|β–ˆβ–Š | 151/852 [00:01<00:09, 77.26it/s]
19%|β–ˆβ–‰ | 160/852 [00:01<00:08, 79.83it/s]
20%|β–ˆβ–‰ | 169/852 [00:02<00:08, 80.18it/s]
21%|β–ˆβ–ˆ | 178/852 [00:02<00:08, 80.61it/s]
22%|β–ˆβ–ˆβ– | 187/852 [00:02<00:08, 81.27it/s]
23%|β–ˆβ–ˆβ–Ž | 196/852 [00:02<00:08, 81.22it/s]
24%|β–ˆβ–ˆβ– | 205/852 [00:02<00:07, 82.14it/s]
25%|β–ˆβ–ˆβ–Œ | 214/852 [00:02<00:08, 78.56it/s]
26%|β–ˆβ–ˆβ–Œ | 223/852 [00:02<00:07, 79.93it/s]
27%|β–ˆβ–ˆβ–‹ | 232/852 [00:02<00:07, 81.20it/s]
28%|β–ˆβ–ˆβ–Š | 241/852 [00:03<00:07, 78.11it/s]
29%|β–ˆβ–ˆβ–‰ | 250/852 [00:03<00:07, 79.54it/s]
30%|β–ˆβ–ˆβ–ˆ | 259/852 [00:03<00:07, 80.76it/s]
31%|β–ˆβ–ˆβ–ˆβ– | 268/852 [00:03<00:07, 80.54it/s]
33%|β–ˆβ–ˆβ–ˆβ–Ž | 277/852 [00:03<00:07, 81.44it/s]
34%|β–ˆβ–ˆβ–ˆβ–Ž | 286/852 [00:03<00:06, 82.18it/s]
35%|β–ˆβ–ˆβ–ˆβ– | 295/852 [00:03<00:06, 80.52it/s]
36%|β–ˆβ–ˆβ–ˆβ–Œ | 304/852 [00:03<00:06, 81.59it/s]
37%|β–ˆβ–ˆβ–ˆβ–‹ | 313/852 [00:03<00:06, 79.63it/s]
38%|β–ˆβ–ˆβ–ˆβ–Š | 322/852 [00:03<00:06, 81.30it/s]
39%|β–ˆβ–ˆβ–ˆβ–‰ | 331/852 [00:04<00:06, 80.35it/s]
40%|β–ˆβ–ˆβ–ˆβ–‰ | 340/852 [00:04<00:06, 80.39it/s]
41%|β–ˆβ–ˆβ–ˆβ–ˆ | 349/852 [00:04<00:06, 80.42it/s]
42%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 358/852 [00:04<00:06, 78.16it/s]
43%|β–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 367/852 [00:04<00:06, 78.26it/s]
44%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 375/852 [00:04<00:06, 78.36it/s]
45%|β–ˆβ–ˆβ–ˆβ–ˆβ– | 383/852 [00:04<00:06, 77.78it/s]
46%|β–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 392/852 [00:04<00:05, 79.06it/s]
47%|β–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 400/852 [00:04<00:05, 78.80it/s]
48%|β–ˆβ–ˆβ–ˆβ–ˆβ–Š | 408/852 [00:05<00:05, 76.89it/s]
49%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 417/852 [00:05<00:05, 78.38it/s]
50%|β–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 425/852 [00:05<00:05, 77.18it/s]
51%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 434/852 [00:05<00:05, 78.90it/s]
52%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 443/852 [00:05<00:05, 79.74it/s]
53%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 452/852 [00:05<00:04, 80.60it/s]
54%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 461/852 [00:05<00:04, 79.04it/s]
55%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 469/852 [00:05<00:04, 78.87it/s]
56%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 477/852 [00:05<00:04, 76.18it/s]
57%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 485/852 [00:06<00:04, 76.30it/s]
58%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 494/852 [00:06<00:04, 78.74it/s]
59%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 503/852 [00:06<00:04, 80.84it/s]
60%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 512/852 [00:06<00:04, 81.29it/s]
61%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 521/852 [00:06<00:04, 81.02it/s]
62%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 530/852 [00:06<00:04, 80.47it/s]
63%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 539/852 [00:06<00:03, 81.51it/s]
64%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 548/852 [00:06<00:03, 81.69it/s]
65%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 557/852 [00:06<00:03, 78.97it/s]
66%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 566/852 [00:07<00:03, 80.53it/s]
67%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 575/852 [00:07<00:03, 80.72it/s]
69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 584/852 [00:07<00:03, 78.77it/s]
69%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 592/852 [00:07<00:03, 78.82it/s]
70%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 600/852 [00:07<00:03, 78.94it/s]
71%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 608/852 [00:07<00:03, 79.16it/s]
72%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 616/852 [00:07<00:03, 77.17it/s]
73%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 625/852 [00:07<00:02, 78.54it/s]
74%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 633/852 [00:07<00:02, 77.76it/s]
75%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 641/852 [00:08<00:02, 73.90it/s]
76%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 650/852 [00:08<00:02, 75.98it/s]
77%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 658/852 [00:08<00:02, 77.10it/s]
78%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 666/852 [00:08<00:02, 76.85it/s]
79%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 674/852 [00:08<00:02, 76.57it/s]
80%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 682/852 [00:08<00:02, 76.91it/s]
81%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 690/852 [00:08<00:02, 77.56it/s]
82%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 699/852 [00:08<00:01, 78.82it/s]
83%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž | 707/852 [00:08<00:01, 78.02it/s]
84%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ– | 716/852 [00:09<00:01, 78.90it/s]
85%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 725/852 [00:09<00:01, 79.76it/s]
86%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ | 734/852 [00:09<00:01, 81.48it/s]
87%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹ | 743/852 [00:09<00:01, 81.95it/s]
88%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š | 752/852 [00:09<00:01, 82.60it/s]
89%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰ | 761/852 [00:09<00:01, 83.75it/s]
90%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ | 770/852 [00:09<00:00, 82.01it/s]
91%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 779/852 [00:09<00:00, 81.56it/s]
92%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 788/852 [00:09<00:00, 80.52it/s]
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 797/852 [00:10<00:00, 80.46it/s]
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 806/852 [00:10<00:00, 81.77it/s]
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 815/852 [00:10<00:00, 80.65it/s]
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 824/852 [00:10<00:00, 81.60it/s]
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 833/852 [00:10<00:00, 82.33it/s]
99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 842/852 [00:10<00:00, 80.74it/s]
100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 851/852 [00:10<00:00, 81.01it/s]/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
 10%|β–ˆ | 541/5410 [02:30<25:28, 3.18it/s]
100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 852/852 [00:14<00:00, 81.01it/s]
[INFO|trainer.py:3503] 2024-09-06 00:11:39,040 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-541
[INFO|configuration_utils.py:472] 2024-09-06 00:11:39,042 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-541/config.json
[INFO|modeling_utils.py:2799] 2024-09-06 00:11:40,402 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-541/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-06 00:11:40,403 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-541/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-06 00:11:40,403 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-541/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-06 00:11:43,153 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-06 00:11:43,153 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
10%|β–ˆ | 542/5410 [02:35<7:54:05, 5.84s/it] 10%|β–ˆ | 543/5410 [02:35<5:37:39, 4.16s/it] 10%|β–ˆ | 544/5410 [02:35<4:02:49, 2.99s/it] 10%|β–ˆ | 545/5410 [02:35<2:55:18, 2.16s/it] 10%|β–ˆ | 546/5410 [02:36<2:07:58, 1.58s/it] 10%|β–ˆ | 547/5410 [02:36<1:35:06, 1.17s/it] 10%|β–ˆ | 548/5410 [02:36<1:12:46, 1.11it/s] 10%|β–ˆ | 549/5410 [02:36<55:36, 1.46it/s] 10%|β–ˆ | 550/5410 [02:37<46:04, 1.76it/s] 10%|β–ˆ | 551/5410 [02:37<37:52, 2.14it/s] 10%|β–ˆ | 552/5410 [02:37<37:27, 2.16it/s] 10%|β–ˆ | 553/5410 [02:37<30:36, 2.65it/s] 10%|β–ˆ | 554/5410 [02:38<28:47, 2.81it/s] 10%|β–ˆ | 555/5410 [02:38<25:27, 3.18it/s] 10%|β–ˆ | 556/5410 [02:38<23:10, 3.49it/s] 10%|β–ˆ | 557/5410 [02:38<23:03, 3.51it/s] 10%|β–ˆ | 558/5410 [02:39<24:37, 3.28it/s] 10%|β–ˆ | 559/5410 [02:39<22:29, 3.59it/s] 10%|β–ˆ | 560/5410 [02:39<20:36, 3.92it/s] 10%|β–ˆ | 561/5410 [02:39<19:01, 4.25it/s] 10%|β–ˆ | 562/5410 [02:40<18:26, 4.38it/s] 10%|β–ˆ | 563/5410 [02:40<17:21, 4.65it/s] 10%|β–ˆ | 564/5410 [02:40<18:23, 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| 654/5410 [03:02<19:30, 4.06it/s] 12%|β–ˆβ– | 655/5410 [03:03<18:31, 4.28it/s] 12%|β–ˆβ– | 656/5410 [03:03<18:31, 4.28it/s] 12%|β–ˆβ– | 657/5410 [03:03<16:53, 4.69it/s] 12%|β–ˆβ– | 658/5410 [03:03<16:36, 4.77it/s] 12%|β–ˆβ– | 659/5410 [03:03<15:48, 5.01it/s] 12%|β–ˆβ– | 660/5410 [03:04<15:56, 4.97it/s] 12%|β–ˆβ– | 661/5410 [03:04<19:09, 4.13it/s] 12%|β–ˆβ– | 662/5410 [03:04<20:22, 3.89it/s] 12%|β–ˆβ– | 663/5410 [03:04<20:21, 3.89it/s] 12%|β–ˆβ– | 664/5410 [03:05<18:44, 4.22it/s] 12%|β–ˆβ– | 665/5410 [03:05<18:17, 4.32it/s] 12%|β–ˆβ– | 666/5410 [03:05<18:15, 4.33it/s] 12%|β–ˆβ– | 667/5410 [03:05<17:32, 4.50it/s] 12%|β–ˆβ– | 668/5410 [03:06<20:56, 3.78it/s] 12%|β–ˆβ– | 669/5410 [03:06<20:08, 3.92it/s] 12%|β–ˆβ– | 670/5410 [03:06<20:21, 3.88it/s] 12%|β–ˆβ– | 671/5410 [03:06<18:36, 4.24it/s] 12%|β–ˆβ– | 672/5410 [03:07<18:06, 4.36it/s] 12%|β–ˆβ– | 673/5410 [03:07<18:48, 4.20it/s] 12%|β–ˆβ– | 674/5410 [03:07<17:31, 4.50it/s] 12%|β–ˆβ– | 675/5410 [03:07<18:23, 4.29it/s] 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