2024-09-09 15:31:47.031697: 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`.
2024-09-09 15:31:47.049445: 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
2024-09-09 15:31:47.070590: 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
2024-09-09 15:31:47.076917: 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
2024-09-09 15:31:47.092460: 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.
2024-09-09 15:31:48.374361: 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 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
09/09/2024 15:31:50 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/09/2024 15:31:50 - 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] 2024-09-09 15:32:08,877 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/config.json
[INFO|configuration_utils.py:800] 2024-09-09 15:32:08,881 >> Model config BertConfig {
  "_name_or_path": "IVN-RIN/bioBIT",
  "architectures": [
    "BertForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "finetuning_task": "ner",
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "id2label": {
    "0": "O",
    "1": "B-FARMACO",
    "2": "I-FARMACO"
  },
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "label2id": {
    "B-FARMACO": 1,
    "I-FARMACO": 2,
    "O": 0
  },
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "position_embedding_type": "absolute",
  "torch_dtype": "float32",
  "transformers_version": "4.44.2",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 31102
}

[INFO|tokenization_utils_base.py:2269] 2024-09-09 15:32:11,992 >> loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/vocab.txt
[INFO|tokenization_utils_base.py:2269] 2024-09-09 15:32:11,992 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/tokenizer.json
[INFO|tokenization_utils_base.py:2269] 2024-09-09 15:32:11,992 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-09 15:32:11,992 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/special_tokens_map.json
[INFO|tokenization_utils_base.py:2269] 2024-09-09 15:32:11,992 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/tokenizer_config.json
/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|modeling_utils.py:3678] 2024-09-09 15:32:38,680 >> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--IVN-RIN--bioBIT/snapshots/83755ed79ee254c11854e9f54a53679557271018/model.safetensors
[INFO|modeling_utils.py:4497] 2024-09-09 15:32:38,735 >> Some weights of the model checkpoint at IVN-RIN/bioBIT were not used when initializing BertForTokenClassification: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight']
- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification 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] 2024-09-09 15:32:38,735 >> Some weights of BertForTokenClassification were not initialized from the model checkpoint at IVN-RIN/bioBIT 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:811] 2024-09-09 15:32:46,022 >> The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:2134] 2024-09-09 15:32:46,585 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-09 15:32:46,586 >>   Num examples = 31,053
[INFO|trainer.py:2136] 2024-09-09 15:32:46,586 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-09 15:32:46,586 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-09 15:32:46,586 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-09 15:32:46,586 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-09 15:32:46,586 >>   Total optimization steps = 4,850
[INFO|trainer.py:2143] 2024-09-09 15:32:46,586 >>   Number of trainable parameters = 109,339,395

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 10%|█         | 485/4850 [04:07<34:54,  2.08it/s][INFO|trainer.py:811] 2024-09-09 15:36:53,788 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 15:36:53,790 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 15:36:53,790 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 15:36:53,790 >>   Batch size = 8


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                                                 [INFO|trainer.py:3503] 2024-09-09 15:37:11,317 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-485
[INFO|configuration_utils.py:472] 2024-09-09 15:37:11,319 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-485/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 15:37:12,236 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-485/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 15:37:12,237 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-485/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 15:37:12,237 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-485/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 15:37:17,259 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 15:37:17,259 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 15:41:21,871 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 15:41:21,871 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 15:41:21,871 >>   Batch size = 8
{'eval_loss': 0.004271956626325846, 'eval_precision': 0.9145631067961165, 'eval_recall': 0.9119070667957405, 'eval_f1': 0.9132331555986428, 'eval_accuracy': 0.9984030122046221, 'eval_runtime': 17.5218, 'eval_samples_per_second': 387.973, 'eval_steps_per_second': 48.511, 'epoch': 1.0}
{'loss': 0.0127, 'grad_norm': 0.35554030537605286, 'learning_rate': 4.484536082474227e-05, 'epoch': 1.03}


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                                                 [INFO|trainer.py:3503] 2024-09-09 15:41:39,396 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-971
[INFO|configuration_utils.py:472] 2024-09-09 15:41:39,397 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-971/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 15:41:40,266 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-971/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 15:41:40,267 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-971/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 15:41:40,268 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-971/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 15:41:44,968 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 15:41:44,968 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 15:45:50,899 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 15:45:50,899 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 15:45:50,899 >>   Batch size = 8
{'eval_loss': 0.005422212183475494, 'eval_precision': 0.9161793372319688, 'eval_recall': 0.9099709583736689, 'eval_f1': 0.9130645944633317, 'eval_accuracy': 0.9984679304076863, 'eval_runtime': 17.524, 'eval_samples_per_second': 387.925, 'eval_steps_per_second': 48.505, 'epoch': 2.0}
{'loss': 0.0033, 'grad_norm': 0.2354772835969925, 'learning_rate': 3.9690721649484535e-05, 'epoch': 2.06}


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                                                 [INFO|trainer.py:3503] 2024-09-09 15:46:08,381 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1456
[INFO|configuration_utils.py:472] 2024-09-09 15:46:08,382 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1456/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 15:46:09,229 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1456/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 15:46:09,230 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1456/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 15:46:09,230 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1456/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 15:46:14,445 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 15:46:14,446 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 15:50:20,570 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 15:50:20,570 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 15:50:20,571 >>   Batch size = 8
{'eval_loss': 0.0059292372316122055, 'eval_precision': 0.9209756097560976, 'eval_recall': 0.9138431752178122, 'eval_f1': 0.9173955296404277, 'eval_accuracy': 0.9985912749935082, 'eval_runtime': 17.4807, 'eval_samples_per_second': 388.886, 'eval_steps_per_second': 48.625, 'epoch': 3.0}
{'loss': 0.0016, 'grad_norm': 0.026833873242139816, 'learning_rate': 3.4536082474226805e-05, 'epoch': 3.09}


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                                                 [INFO|trainer.py:3503] 2024-09-09 15:50:38,376 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1942
[INFO|configuration_utils.py:472] 2024-09-09 15:50:38,378 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1942/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 15:50:39,273 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1942/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 15:50:39,274 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1942/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 15:50:39,274 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1942/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 15:50:43,589 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 15:50:43,590 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 15:54:49,431 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 15:54:49,431 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 15:54:49,431 >>   Batch size = 8
{'eval_loss': 0.006239830516278744, 'eval_precision': 0.9018867924528302, 'eval_recall': 0.925459825750242, 'eval_f1': 0.9135212613473483, 'eval_accuracy': 0.9985523240716697, 'eval_runtime': 17.8043, 'eval_samples_per_second': 381.818, 'eval_steps_per_second': 47.741, 'epoch': 4.0}
{'loss': 0.001, 'grad_norm': 0.1513855904340744, 'learning_rate': 2.9381443298969075e-05, 'epoch': 4.12}


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                                                 [INFO|trainer.py:3503] 2024-09-09 15:55:06,895 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2427
[INFO|configuration_utils.py:472] 2024-09-09 15:55:06,897 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2427/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 15:55:07,767 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2427/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 15:55:07,768 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2427/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 15:55:07,768 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2427/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 15:55:15,553 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 15:55:15,553 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 15:59:20,932 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 15:59:20,933 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 15:59:20,933 >>   Batch size = 8
{'eval_loss': 0.006359434220939875, 'eval_precision': 0.918111753371869, 'eval_recall': 0.9225556631171346, 'eval_f1': 0.920328343795268, 'eval_accuracy': 0.9986042586341211, 'eval_runtime': 17.4628, 'eval_samples_per_second': 389.284, 'eval_steps_per_second': 48.675, 'epoch': 5.0}
{'loss': 0.0006, 'grad_norm': 0.007741741370409727, 'learning_rate': 2.422680412371134e-05, 'epoch': 5.15}


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                                                 [INFO|trainer.py:3503] 2024-09-09 15:59:38,461 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2913
[INFO|configuration_utils.py:472] 2024-09-09 15:59:38,463 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2913/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 15:59:39,327 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2913/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 15:59:39,328 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2913/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 15:59:39,328 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2913/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 15:59:45,953 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 15:59:45,954 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 16:03:52,317 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 16:03:52,317 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 16:03:52,317 >>   Batch size = 8
{'eval_loss': 0.00812861043959856, 'eval_precision': 0.9202334630350194, 'eval_recall': 0.9157792836398838, 'eval_f1': 0.9180009704027171, 'eval_accuracy': 0.9985523240716697, 'eval_runtime': 17.5273, 'eval_samples_per_second': 387.852, 'eval_steps_per_second': 48.496, 'epoch': 6.0}
{'loss': 0.0004, 'grad_norm': 0.06708419322967529, 'learning_rate': 1.9072164948453608e-05, 'epoch': 6.18}


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                                                 [INFO|trainer.py:3503] 2024-09-09 16:04:09,791 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3398
[INFO|configuration_utils.py:472] 2024-09-09 16:04:09,792 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3398/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 16:04:10,672 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3398/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 16:04:10,673 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3398/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 16:04:10,673 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3398/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 16:04:14,896 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 16:04:14,897 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 16:08:21,921 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 16:08:21,921 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 16:08:21,921 >>   Batch size = 8
{'eval_loss': 0.008457792922854424, 'eval_precision': 0.918111753371869, 'eval_recall': 0.9225556631171346, 'eval_f1': 0.920328343795268, 'eval_accuracy': 0.9985653077122826, 'eval_runtime': 17.4726, 'eval_samples_per_second': 389.067, 'eval_steps_per_second': 48.648, 'epoch': 7.0}
{'loss': 0.0003, 'grad_norm': 0.0032746351789683104, 'learning_rate': 1.3917525773195878e-05, 'epoch': 7.21}


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                                                 [INFO|trainer.py:3503] 2024-09-09 16:08:39,442 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3884
[INFO|configuration_utils.py:472] 2024-09-09 16:08:39,443 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3884/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 16:08:40,313 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3884/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 16:08:40,314 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3884/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 16:08:40,314 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3884/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 16:08:45,699 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 16:08:45,700 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 16:12:51,543 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 16:12:51,543 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 16:12:51,543 >>   Batch size = 8
{'eval_loss': 0.00870645884424448, 'eval_precision': 0.9215116279069767, 'eval_recall': 0.920619554695063, 'eval_f1': 0.9210653753026635, 'eval_accuracy': 0.9985847831732018, 'eval_runtime': 17.5192, 'eval_samples_per_second': 388.031, 'eval_steps_per_second': 48.518, 'epoch': 8.0}
{'loss': 0.0002, 'grad_norm': 0.0013693332439288497, 'learning_rate': 8.762886597938144e-06, 'epoch': 8.24}


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                                                 [INFO|trainer.py:3503] 2024-09-09 16:13:09,302 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4369
[INFO|configuration_utils.py:472] 2024-09-09 16:13:09,303 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4369/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 16:13:10,174 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4369/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 16:13:10,175 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4369/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 16:13:10,175 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4369/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 16:13:14,893 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 16:13:14,893 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|configuration_utils.py:472] 2024-09-09 16:17:18,700 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4850/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 16:17:19,585 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4850/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 16:17:19,586 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4850/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 16:17:19,586 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4850/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 16:17:27,095 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 16:17:27,096 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-09 16:17:27,110 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 16:17:27,113 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 16:17:27,113 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 16:17:27,113 >>   Batch size = 8
{'eval_loss': 0.008967469446361065, 'eval_precision': 0.9093484419263456, 'eval_recall': 0.9322362052274927, 'eval_f1': 0.9206500956022945, 'eval_accuracy': 0.9985198649701377, 'eval_runtime': 17.7573, 'eval_samples_per_second': 382.828, 'eval_steps_per_second': 47.868, 'epoch': 9.0}
{'loss': 0.0001, 'grad_norm': 0.0005869244341738522, 'learning_rate': 3.608247422680412e-06, 'epoch': 9.27}


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                                                 [INFO|trainer.py:3503] 2024-09-09 16:17:44,591 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4850
[INFO|configuration_utils.py:472] 2024-09-09 16:17:44,592 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4850/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 16:17:45,893 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4850/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 16:17:45,896 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4850/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 16:17:45,896 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4850/special_tokens_map.json
[INFO|trainer.py:2394] 2024-09-09 16:17:47,777 >> 

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


[INFO|trainer.py:2632] 2024-09-09 16:17:47,777 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-4850 (score: 0.9230769230769231).

                                                   

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[INFO|trainer.py:4283] 2024-09-09 16:17:47,929 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-09 16:18:03,045 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 16:18:03,047 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 16:18:04,208 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 16:18:04,209 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 16:18:04,209 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3503] 2024-09-09 16:18:04,223 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 16:18:04,224 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 16:18:06,636 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 16:18:06,638 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 16:18:06,638 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.009366312995553017, 'eval_precision': 0.9169054441260746, 'eval_recall': 0.9293320425943853, 'eval_f1': 0.9230769230769231, 'eval_accuracy': 0.9986302259153467, 'eval_runtime': 17.4762, 'eval_samples_per_second': 388.987, 'eval_steps_per_second': 48.638, 'epoch': 9.99}
{'train_runtime': 2701.3395, 'train_samples_per_second': 114.954, 'train_steps_per_second': 1.795, 'train_loss': 0.002068036902580679, 'epoch': 9.99}

events.out.tfevents.1725895966.0a1c9bec2a53.65267.0:   0%|          | 0.00/12.1k [00:00<?, ?B/s]
events.out.tfevents.1725895966.0a1c9bec2a53.65267.0: 100%|██████████| 12.1k/12.1k [00:00<00:00, 36.8kB/s]
***** train metrics *****
  epoch                    =     9.9897
  total_flos               = 16517987GF
  train_loss               =     0.0021
  train_runtime            = 0:45:01.33
  train_samples            =      31053
  train_samples_per_second =    114.954
  train_steps_per_second   =      1.795
09/09/2024 16:18:12 - INFO - __main__ -   *** Evaluate ***
[INFO|trainer.py:811] 2024-09-09 16:18:12,179 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 16:18:12,182 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 16:18:12,182 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 16:18:12,182 >>   Batch size = 8

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***** eval metrics *****
  epoch                   =     9.9897
  eval_accuracy           =     0.9986
  eval_f1                 =     0.9231
  eval_loss               =     0.0094
  eval_precision          =     0.9169
  eval_recall             =     0.9293
  eval_runtime            = 0:00:17.51
  eval_samples            =       6798
  eval_samples_per_second =    388.236
  eval_steps_per_second   =     48.544
09/09/2024 16:18:29 - INFO - __main__ -   *** Predict ***
[INFO|trainer.py:811] 2024-09-09 16:18:29,694 >> The following columns in the test set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 16:18:29,696 >> 
***** Running Prediction *****
[INFO|trainer.py:3821] 2024-09-09 16:18:29,696 >>   Num examples = 14605
[INFO|trainer.py:3824] 2024-09-09 16:18:29,696 >>   Batch size = 8

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[INFO|trainer.py:3503] 2024-09-09 16:19:03,621 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 16:19:03,622 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 16:19:04,830 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 16:19:04,831 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 16:19:04,832 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
  predict_accuracy           =     0.9982
  predict_f1                 =     0.8965
  predict_loss               =     0.0127
  predict_precision          =     0.8773
  predict_recall             =     0.9166
  predict_runtime            = 0:00:33.03
  predict_samples_per_second =    442.053
  predict_steps_per_second   =     55.268

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events.out.tfevents.1725898709.0a1c9bec2a53.65267.1: 100%|██████████| 560/560 [00:00<00:00, 2.20kB/s]