2024-08-30 19:28:27.550322: 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`.
2024-08-30 19:28:27.568559: 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
2024-08-30 19:28:27.590657: 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
2024-08-30 19:28:27.597168: 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
2024-08-30 19:28:27.612945: 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.
2024-08-30 19:28:28.891790: 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:1494: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
08/30/2024 19:28:30 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
08/30/2024 19:28:30 - 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,
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,
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] 2024-08-30 19:28:42,399 >> 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] 2024-08-30 19:28:42,403 >> 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": "4.42.4",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|configuration_utils.py:733] 2024-08-30 19:28:42,500 >> 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] 2024-08-30 19:28:42,501 >> 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": "4.42.4",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|tokenization_utils_base.py:2161] 2024-08-30 19:28:42,511 >> 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:2161] 2024-08-30 19:28:42,511 >> 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:2161] 2024-08-30 19:28:42,511 >> loading file tokenizer.json from cache at None
[INFO|tokenization_utils_base.py:2161] 2024-08-30 19:28:42,511 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2161] 2024-08-30 19:28:42,511 >> 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:2161] 2024-08-30 19:28:42,511 >> 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] 2024-08-30 19:28:42,512 >> 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] 2024-08-30 19:28:42,512 >> 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": "4.42.4",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|configuration_utils.py:733] 2024-08-30 19:28:42,598 >> 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] 2024-08-30 19:28:42,599 >> 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": "4.42.4",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|modeling_utils.py:3556] 2024-08-30 19:28:42,784 >> 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:4354] 2024-08-30 19:28:42,921 >> 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:4366] 2024-08-30 19:28:42,921 >> 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 🤗 Evaluate: https://huggingface.co/docs/evaluate
  metric = load_metric("seqeval", trust_remote_code=True)
[INFO|trainer.py:805] 2024-08-30 19:28:48,762 >> The following columns in the training set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:2128] 2024-08-30 19:28:49,318 >> ***** Running training *****
[INFO|trainer.py:2129] 2024-08-30 19:28:49,318 >>   Num examples = 27,229
[INFO|trainer.py:2130] 2024-08-30 19:28:49,318 >>   Num Epochs = 10
[INFO|trainer.py:2131] 2024-08-30 19:28:49,318 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2134] 2024-08-30 19:28:49,318 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2135] 2024-08-30 19:28:49,318 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2136] 2024-08-30 19:28:49,318 >>   Total optimization steps = 4,250
[INFO|trainer.py:2137] 2024-08-30 19:28:49,319 >>   Number of trainable parameters = 124,059,657

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 10%|█         | 425/4250 [01:42<14:25,  4.42it/s][INFO|trainer.py:805] 2024-08-30 19:30:31,551 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3788] 2024-08-30 19:30:31,554 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 19:30:31,554 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 19:30:31,554 >>   Batch size = 8


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100%|█████████▉| 850/852 [00:10<00:00, 77.87it/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))

                                                  

                                                 

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                                                 [INFO|trainer.py:3478] 2024-08-30 19:30:46,291 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-425
[INFO|configuration_utils.py:472] 2024-08-30 19:30:46,293 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-425/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:30:47,803 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-425/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:30:47,804 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-425/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:30:47,805 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-425/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:30:50,896 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:30:50,896 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 19:32:33,061 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 19:32:33,061 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 19:32:33,061 >>   Batch size = 8
{'eval_loss': 0.3833567500114441, 'eval_precision': 0.29195422381409974, 'eval_recall': 0.7819372952737482, 'eval_f1': 0.4251637936518033, 'eval_accuracy': 0.8517239485355707, 'eval_runtime': 14.7317, 'eval_samples_per_second': 462.27, 'eval_steps_per_second': 57.835, 'epoch': 1.0}
{'loss': 0.3349, 'grad_norm': 1.4406206607818604, 'learning_rate': 4.411764705882353e-05, 'epoch': 1.18}


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100%|█████████▉| 849/852 [00:11<00:00, 77.97it/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))

                                                  

                                                 

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                                                 [INFO|trainer.py:3478] 2024-08-30 19:32:47,971 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-851
[INFO|configuration_utils.py:472] 2024-08-30 19:32:47,972 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-851/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:32:49,438 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-851/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:32:49,439 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-851/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:32:49,439 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-851/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:32:51,764 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:32:51,764 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 19:34:33,539 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 19:34:33,539 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 19:34:33,540 >>   Batch size = 8
{'eval_loss': 0.5730458498001099, 'eval_precision': 0.2681125621890547, 'eval_recall': 0.8069723912026205, 'eval_f1': 0.40249737425603915, 'eval_accuracy': 0.822140140332569, 'eval_runtime': 14.9036, 'eval_samples_per_second': 456.937, 'eval_steps_per_second': 57.167, 'epoch': 2.0}
{'loss': 0.1788, 'grad_norm': 2.988006591796875, 'learning_rate': 3.8235294117647055e-05, 'epoch': 2.35}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3478] 2024-08-30 19:34:48,300 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1276
[INFO|configuration_utils.py:472] 2024-08-30 19:34:48,301 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1276/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:34:49,333 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1276/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:34:49,334 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1276/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:34:49,334 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1276/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:34:51,410 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:34:51,410 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 19:36:32,983 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 19:36:32,983 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 19:36:32,983 >>   Batch size = 8
{'eval_loss': 0.5796028971672058, 'eval_precision': 0.2847990681421083, 'eval_recall': 0.8008890968647637, 'eval_f1': 0.42018044559013074, 'eval_accuracy': 0.8338185013799827, 'eval_runtime': 14.7541, 'eval_samples_per_second': 461.567, 'eval_steps_per_second': 57.747, 'epoch': 3.0}
{'loss': 0.1227, 'grad_norm': 1.1645787954330444, 'learning_rate': 3.235294117647059e-05, 'epoch': 3.53}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3478] 2024-08-30 19:36:47,852 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1702
[INFO|configuration_utils.py:472] 2024-08-30 19:36:47,854 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1702/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:36:48,889 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1702/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:36:48,890 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1702/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:36:48,890 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1702/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:36:50,975 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:36:50,975 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 19:38:32,458 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 19:38:32,458 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 19:38:32,458 >>   Batch size = 8
{'eval_loss': 0.6590859889984131, 'eval_precision': 0.2996455433560993, 'eval_recall': 0.8109499298081423, 'eval_f1': 0.43759863644971914, 'eval_accuracy': 0.8388097820863142, 'eval_runtime': 14.8634, 'eval_samples_per_second': 458.172, 'eval_steps_per_second': 57.322, 'epoch': 4.0}
{'loss': 0.0856, 'grad_norm': 1.1214195489883423, 'learning_rate': 2.647058823529412e-05, 'epoch': 4.7}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3478] 2024-08-30 19:38:47,240 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2127
[INFO|configuration_utils.py:472] 2024-08-30 19:38:47,241 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2127/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:38:48,258 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2127/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:38:48,259 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2127/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:38:48,259 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2127/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:38:50,369 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:38:50,370 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 19:40:31,955 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 19:40:31,956 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 19:40:31,956 >>   Batch size = 8
{'eval_loss': 0.6266195774078369, 'eval_precision': 0.32197630636422075, 'eval_recall': 0.8203088441740758, 'eval_f1': 0.4624414693662204, 'eval_accuracy': 0.8601754843670617, 'eval_runtime': 14.7766, 'eval_samples_per_second': 460.863, 'eval_steps_per_second': 57.659, 'epoch': 5.0}
{'loss': 0.0597, 'grad_norm': 1.1390776634216309, 'learning_rate': 2.058823529411765e-05, 'epoch': 5.88}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3478] 2024-08-30 19:40:46,777 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2553
[INFO|configuration_utils.py:472] 2024-08-30 19:40:46,778 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2553/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:40:47,795 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2553/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:40:47,796 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2553/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:40:47,796 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2553/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:40:49,975 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:40:49,975 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 19:42:31,733 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 19:42:31,733 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 19:42:31,733 >>   Batch size = 8
{'eval_loss': 0.7858611941337585, 'eval_precision': 0.3075490109110263, 'eval_recall': 0.8111839026672906, 'eval_f1': 0.44600244420145363, 'eval_accuracy': 0.8475977316105291, 'eval_runtime': 14.8163, 'eval_samples_per_second': 459.628, 'eval_steps_per_second': 57.504, 'epoch': 6.0}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3478] 2024-08-30 19:42:46,626 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2978
[INFO|configuration_utils.py:472] 2024-08-30 19:42:46,627 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2978/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:42:47,653 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2978/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:42:47,654 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2978/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:42:47,654 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2978/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:42:50,273 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:42:50,274 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 19:44:32,026 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 19:44:32,026 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 19:44:32,026 >>   Batch size = 8
{'eval_loss': 0.829707145690918, 'eval_precision': 0.3136796692432141, 'eval_recall': 0.8165652784277024, 'eval_f1': 0.45324675324675323, 'eval_accuracy': 0.8507970944841886, 'eval_runtime': 14.8867, 'eval_samples_per_second': 457.456, 'eval_steps_per_second': 57.232, 'epoch': 7.0}
{'loss': 0.0458, 'grad_norm': 0.5195357799530029, 'learning_rate': 1.4705882352941177e-05, 'epoch': 7.05}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3478] 2024-08-30 19:44:47,083 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3404
[INFO|configuration_utils.py:472] 2024-08-30 19:44:47,084 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3404/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:44:48,122 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3404/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:44:48,123 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3404/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:44:48,123 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3404/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:44:50,231 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:44:50,231 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 19:46:30,985 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 19:46:30,985 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 19:46:30,985 >>   Batch size = 8
{'eval_loss': 0.8468152284622192, 'eval_precision': 0.3134608509116911, 'eval_recall': 0.8205428170332242, 'eval_f1': 0.453628249902988, 'eval_accuracy': 0.8532206461889135, 'eval_runtime': 15.05, 'eval_samples_per_second': 452.493, 'eval_steps_per_second': 56.611, 'epoch': 8.0}
{'loss': 0.0343, 'grad_norm': 0.8723571300506592, 'learning_rate': 8.823529411764707e-06, 'epoch': 8.23}


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100%|█████████▉| 849/852 [00:10<00:00, 78.54it/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))

                                                   

                                                 

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                                                 [INFO|trainer.py:3478] 2024-08-30 19:46:45,654 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3829
[INFO|configuration_utils.py:472] 2024-08-30 19:46:45,655 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3829/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:46:46,695 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3829/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:46:46,696 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3829/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:46:46,696 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3829/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:46:52,859 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:46:52,859 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|configuration_utils.py:472] 2024-08-30 19:48:32,829 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4250/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:48:33,864 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4250/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:48:33,865 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4250/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:48:33,865 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4250/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:48:35,997 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:48:35,997 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:805] 2024-08-30 19:48:36,049 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3788] 2024-08-30 19:48:36,052 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 19:48:36,052 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 19:48:36,052 >>   Batch size = 8
{'eval_loss': 0.9241161346435547, 'eval_precision': 0.30851345390383766, 'eval_recall': 0.8182030884417407, 'eval_f1': 0.44807482862451153, 'eval_accuracy': 0.8493827838576352, 'eval_runtime': 14.662, 'eval_samples_per_second': 464.465, 'eval_steps_per_second': 58.109, 'epoch': 9.0}
{'loss': 0.0292, 'grad_norm': 0.6075822710990906, 'learning_rate': 2.9411764705882355e-06, 'epoch': 9.4}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3478] 2024-08-30 19:48:51,528 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4250
[INFO|configuration_utils.py:472] 2024-08-30 19:48:51,530 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4250/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:48:52,902 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4250/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:48:52,905 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4250/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:48:52,905 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4250/special_tokens_map.json
[INFO|trainer.py:2383] 2024-08-30 19:48:54,756 >> 

Training completed. Do not forget to share your model on huggingface.co/models =)


[INFO|trainer.py:2621] 2024-08-30 19:48:54,756 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-2127 (score: 0.4624414693662204).

                                                   

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[INFO|trainer.py:4239] 2024-08-30 19:48:54,956 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3478] 2024-08-30 19:48:56,764 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-08-30 19:48:56,766 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:48:58,154 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:48:58,155 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:48:58,156 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3478] 2024-08-30 19:48:58,210 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-08-30 19:48:58,212 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:48:59,637 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:48:59,639 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:48:59,639 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.938378632068634, 'eval_precision': 0.31002310289674784, 'eval_recall': 0.816331305568554, 'eval_f1': 0.44938176197836166, 'eval_accuracy': 0.8498565092616749, 'eval_runtime': 15.4697, 'eval_samples_per_second': 440.215, 'eval_steps_per_second': 55.075, 'epoch': 9.99}
{'train_runtime': 1205.6346, 'train_samples_per_second': 225.848, 'train_steps_per_second': 3.525, 'train_loss': 0.10639642311544979, 'epoch': 9.99}

events.out.tfevents.1725046129.6b97e535edda.6370.0:   0%|          | 0.00/12.2k [00:00<?, ?B/s]
events.out.tfevents.1725046129.6b97e535edda.6370.0: 100%|██████████| 12.2k/12.2k [00:00<00:00, 73.4kB/s]
***** train metrics *****
  epoch                    =     9.9882
  total_flos               = 11781054GF
  train_loss               =     0.1064
  train_runtime            = 0:20:05.63
  train_samples            =      27229
  train_samples_per_second =    225.848
  train_steps_per_second   =      3.525
08/30/2024 19:49:03 - INFO - __main__ -   *** Evaluate ***
[INFO|trainer.py:805] 2024-08-30 19:49:03,449 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3788] 2024-08-30 19:49:03,452 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 19:49:03,452 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 19:49:03,452 >>   Batch size = 8

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  _warn_prf(average, modifier, msg_start, len(result))

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***** eval metrics *****
  epoch                   =     9.9882
  eval_accuracy           =     0.8602
  eval_f1                 =     0.4624
  eval_loss               =     0.6266
  eval_precision          =      0.322
  eval_recall             =     0.8203
  eval_runtime            = 0:00:14.99
  eval_samples            =       6810
  eval_samples_per_second =     454.15
  eval_steps_per_second   =     56.819
08/30/2024 19:49:18 - INFO - __main__ -   *** Predict ***
[INFO|trainer.py:805] 2024-08-30 19:49:18,454 >> The following columns in the test set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3788] 2024-08-30 19:49:18,456 >> 
***** Running Prediction *****
[INFO|trainer.py:3790] 2024-08-30 19:49:18,456 >>   Num examples = 14614
[INFO|trainer.py:3793] 2024-08-30 19:49:18,456 >>   Batch size = 8

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[INFO|trainer.py:3478] 2024-08-30 19:49:49,416 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-08-30 19:49:49,418 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 19:49:50,781 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 19:49:50,782 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 19:49:50,782 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
  predict_accuracy           =     0.9511
  predict_f1                 =     0.6556
  predict_loss               =     0.1889
  predict_precision          =     0.5454
  predict_recall             =     0.8215
  predict_runtime            = 0:00:30.32
  predict_samples_per_second =    481.964
  predict_steps_per_second   =     60.254

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events.out.tfevents.1725047358.6b97e535edda.6370.1: 100%|██████████| 560/560 [00:00<00:00, 4.85kB/s]