2024-09-09 17:11:27.358412: 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 17:11:27.376768: 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 17:11:27.398608: 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 17:11:27.405177: 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 17:11:27.421047: 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 17:11:28.723015: 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 17:11:30 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/09/2024 17:11:30 - 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,
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 17:11:47,158 >> 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 17:11:47,162 >> 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 17:11:47,414 >> 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 17:11:47,414 >> 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 17:11:47,414 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-09 17:11:47,414 >> 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 17:11:47,414 >> 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 17:11:47,477 >> 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 17:11:47,532 >> 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 17:11:47,532 >> 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 17:11:54,612 >> The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags. If tokens, id, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:2134] 2024-09-09 17:11:55,175 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-09 17:11:55,175 >>   Num examples = 28,875
[INFO|trainer.py:2136] 2024-09-09 17:11:55,175 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-09 17:11:55,176 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-09 17:11:55,176 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-09 17:11:55,176 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-09 17:11:55,176 >>   Total optimization steps = 4,510
[INFO|trainer.py:2143] 2024-09-09 17:11:55,176 >>   Number of trainable parameters = 109,339,395

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 10%|█         | 451/4510 [03:43<37:21,  1.81it/s][INFO|trainer.py:811] 2024-09-09 17:15:39,097 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags. If tokens, id, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 17:15:39,100 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 17:15:39,100 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 17:15:39,100 >>   Batch size = 8


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                                                 [INFO|trainer.py:3503] 2024-09-09 17:15:56,538 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-451
[INFO|configuration_utils.py:472] 2024-09-09 17:15:56,539 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-451/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:15:57,451 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-451/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:15:57,452 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-451/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:15:57,452 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-451/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:16:03,920 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:16:03,921 >> 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 17:19:47,130 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 17:19:47,130 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 17:19:47,131 >>   Batch size = 8
{'eval_loss': 0.005117920693010092, 'eval_precision': 0.9325726141078838, 'eval_recall': 0.8702807357212003, 'eval_f1': 0.900350525788683, 'eval_accuracy': 0.9983575694624772, 'eval_runtime': 17.4335, 'eval_samples_per_second': 389.938, 'eval_steps_per_second': 48.757, 'epoch': 1.0}
{'loss': 0.0116, 'grad_norm': 0.20482830703258514, 'learning_rate': 4.445676274944568e-05, 'epoch': 1.11}


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                                                 [INFO|trainer.py:3503] 2024-09-09 17:20:04,599 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-903
[INFO|configuration_utils.py:472] 2024-09-09 17:20:04,600 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-903/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:20:05,463 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-903/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:20:05,464 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-903/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:20:05,464 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-903/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:20:12,933 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:20:12,934 >> 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 17:23:56,809 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 17:23:56,809 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 17:23:56,809 >>   Batch size = 8
{'eval_loss': 0.004852809943258762, 'eval_precision': 0.9065776930409915, 'eval_recall': 0.920619554695063, 'eval_f1': 0.9135446685878963, 'eval_accuracy': 0.9985328486107504, 'eval_runtime': 17.467, 'eval_samples_per_second': 389.192, 'eval_steps_per_second': 48.663, 'epoch': 2.0}
{'loss': 0.0034, 'grad_norm': 0.3147585690021515, 'learning_rate': 3.8913525498891355e-05, 'epoch': 2.21}


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                                                 [INFO|trainer.py:3503] 2024-09-09 17:24:14,252 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1354
[INFO|configuration_utils.py:472] 2024-09-09 17:24:14,254 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1354/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:24:15,118 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1354/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:24:15,119 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1354/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:24:15,119 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1354/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:24:20,579 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:24:20,579 >> 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 17:28:04,546 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 17:28:04,546 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 17:28:04,546 >>   Batch size = 8
{'eval_loss': 0.005565761122852564, 'eval_precision': 0.898961284230406, 'eval_recall': 0.9215876089060987, 'eval_f1': 0.910133843212237, 'eval_accuracy': 0.9983900285640094, 'eval_runtime': 17.4422, 'eval_samples_per_second': 389.744, 'eval_steps_per_second': 48.732, 'epoch': 3.0}
{'loss': 0.0018, 'grad_norm': 0.10508228093385696, 'learning_rate': 3.337028824833703e-05, 'epoch': 3.32}


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                                                 [INFO|trainer.py:3503] 2024-09-09 17:28:22,235 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1806
[INFO|configuration_utils.py:472] 2024-09-09 17:28:22,236 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1806/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:28:23,441 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1806/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:28:23,442 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1806/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:28:23,443 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1806/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:28:25,618 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:28:25,619 >> 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 17:32:09,447 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 17:32:09,447 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 17:32:09,447 >>   Batch size = 8
{'eval_loss': 0.00661922711879015, 'eval_precision': 0.909437559580553, 'eval_recall': 0.9235237173281704, 'eval_f1': 0.9164265129682997, 'eval_accuracy': 0.9984874058686055, 'eval_runtime': 17.6875, 'eval_samples_per_second': 384.339, 'eval_steps_per_second': 48.056, 'epoch': 4.0}
{'loss': 0.0011, 'grad_norm': 0.16867227852344513, 'learning_rate': 2.7827050997782704e-05, 'epoch': 4.43}


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                                                 [INFO|trainer.py:3503] 2024-09-09 17:32:26,858 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2257
[INFO|configuration_utils.py:472] 2024-09-09 17:32:26,859 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2257/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:32:27,728 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2257/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:32:27,729 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2257/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:32:27,729 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2257/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:32:33,752 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:32:33,752 >> 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 17:36:16,076 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 17:36:16,077 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 17:36:16,077 >>   Batch size = 8
{'eval_loss': 0.0055653550662100315, 'eval_precision': 0.9082308420056765, 'eval_recall': 0.9293320425943853, 'eval_f1': 0.9186602870813397, 'eval_accuracy': 0.9986237340950402, 'eval_runtime': 17.4102, 'eval_samples_per_second': 390.461, 'eval_steps_per_second': 48.822, 'epoch': 5.0}
{'loss': 0.0007, 'grad_norm': 0.060708414763212204, 'learning_rate': 2.2283813747228384e-05, 'epoch': 5.54}


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                                                 [INFO|trainer.py:3503] 2024-09-09 17:36:33,532 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2709
[INFO|configuration_utils.py:472] 2024-09-09 17:36:33,534 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2709/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:36:34,441 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2709/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:36:34,442 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2709/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:36:34,442 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2709/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:36:37,289 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:36:37,290 >> 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 17:40:18,757 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 17:40:18,757 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 17:40:18,757 >>   Batch size = 8
{'eval_loss': 0.006837355904281139, 'eval_precision': 0.9144800777453839, 'eval_recall': 0.9109390125847048, 'eval_f1': 0.9127061105722599, 'eval_accuracy': 0.9985328486107504, 'eval_runtime': 17.4499, 'eval_samples_per_second': 389.572, 'eval_steps_per_second': 48.711, 'epoch': 6.0}
{'loss': 0.0005, 'grad_norm': 0.001121490728110075, 'learning_rate': 1.674057649667406e-05, 'epoch': 6.64}


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                                                 [INFO|trainer.py:3503] 2024-09-09 17:40:36,224 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3160
[INFO|configuration_utils.py:472] 2024-09-09 17:40:36,226 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3160/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:40:37,082 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3160/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:40:37,083 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3160/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:40:37,083 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3160/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:40:39,215 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:40:39,216 >> 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 17:44:23,693 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 17:44:23,693 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 17:44:23,693 >>   Batch size = 8
{'eval_loss': 0.0075966897420585155, 'eval_precision': 0.887962962962963, 'eval_recall': 0.9283639883833494, 'eval_f1': 0.9077141504969238, 'eval_accuracy': 0.9983705531030901, 'eval_runtime': 17.4659, 'eval_samples_per_second': 389.215, 'eval_steps_per_second': 48.666, 'epoch': 7.0}
{'loss': 0.0003, 'grad_norm': 0.010231947526335716, 'learning_rate': 1.1197339246119735e-05, 'epoch': 7.75}


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                                                 [INFO|trainer.py:3503] 2024-09-09 17:44:41,154 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3612
[INFO|configuration_utils.py:472] 2024-09-09 17:44:41,156 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3612/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:44:42,013 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3612/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:44:42,014 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3612/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:44:42,014 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3612/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:44:47,343 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:44:47,343 >> 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 17:48:31,505 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 17:48:31,505 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 17:48:31,505 >>   Batch size = 8
{'eval_loss': 0.008048146031796932, 'eval_precision': 0.909437559580553, 'eval_recall': 0.9235237173281704, 'eval_f1': 0.9164265129682997, 'eval_accuracy': 0.9985653077122826, 'eval_runtime': 17.4601, 'eval_samples_per_second': 389.346, 'eval_steps_per_second': 48.683, 'epoch': 8.0}
{'loss': 0.0002, 'grad_norm': 0.003443971276283264, 'learning_rate': 5.65410199556541e-06, 'epoch': 8.86}


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                                                 [INFO|trainer.py:3503] 2024-09-09 17:48:48,925 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4063
[INFO|configuration_utils.py:472] 2024-09-09 17:48:48,926 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4063/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:48:49,798 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4063/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:48:49,799 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4063/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:48:49,799 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4063/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:48:56,993 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:48:56,993 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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100%|██████████| 4510/4510 [40:42<00:00,  2.23it/s][INFO|trainer.py:3503] 2024-09-09 17:52:37,852 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4510
[INFO|configuration_utils.py:472] 2024-09-09 17:52:37,853 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4510/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:52:38,771 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4510/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:52:38,772 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4510/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:52:38,773 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4510/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:52:45,587 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:52:45,587 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-09 17:52:45,643 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags. If tokens, id, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 17:52:45,648 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 17:52:45,648 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 17:52:45,648 >>   Batch size = 8
{'eval_loss': 0.007848693057894707, 'eval_precision': 0.9161849710982659, 'eval_recall': 0.920619554695063, 'eval_f1': 0.9183969097054564, 'eval_accuracy': 0.9985847831732018, 'eval_runtime': 17.4184, 'eval_samples_per_second': 390.276, 'eval_steps_per_second': 48.799, 'epoch': 9.0}
{'loss': 0.0001, 'grad_norm': 0.0008140078862197697, 'learning_rate': 1.1086474501108648e-07, 'epoch': 9.97}


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                                                 [INFO|trainer.py:3503] 2024-09-09 17:53:03,478 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4510
[INFO|configuration_utils.py:472] 2024-09-09 17:53:03,480 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4510/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:53:04,797 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4510/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:53:04,800 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4510/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:53:04,800 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4510/special_tokens_map.json
[INFO|trainer.py:2394] 2024-09-09 17:53:06,832 >> 

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


[INFO|trainer.py:2632] 2024-09-09 17:53:06,832 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-4510 (score: 0.9242643511818619).

                                                   

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[INFO|trainer.py:4283] 2024-09-09 17:53:07,002 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-09 17:53:10,989 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 17:53:10,991 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:53:12,201 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:53:12,202 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:53:12,203 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3503] 2024-09-09 17:53:12,216 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 17:53:12,218 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:53:13,356 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:53:13,357 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:53:13,357 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.007998762652277946, 'eval_precision': 0.9211538461538461, 'eval_recall': 0.9273959341723137, 'eval_f1': 0.9242643511818619, 'eval_accuracy': 0.9986302259153467, 'eval_runtime': 17.8292, 'eval_samples_per_second': 381.284, 'eval_steps_per_second': 47.675, 'epoch': 9.99}
{'train_runtime': 2471.8222, 'train_samples_per_second': 116.817, 'train_steps_per_second': 1.825, 'train_loss': 0.002201940788383577, 'epoch': 9.99}

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***** train metrics *****
  epoch                    =     9.9889
  total_flos               = 14986687GF
  train_loss               =     0.0022
  train_runtime            = 0:41:11.82
  train_samples            =      28875
  train_samples_per_second =    116.817
  train_steps_per_second   =      1.825
09/09/2024 17:53:18 - INFO - __main__ -   *** Evaluate ***
[INFO|trainer.py:811] 2024-09-09 17:53:18,787 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags. If tokens, id, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 17:53:18,790 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 17:53:18,790 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-09 17:53:18,790 >>   Batch size = 8

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***** eval metrics *****
  epoch                   =     9.9889
  eval_accuracy           =     0.9986
  eval_f1                 =     0.9243
  eval_loss               =      0.008
  eval_precision          =     0.9212
  eval_recall             =     0.9274
  eval_runtime            = 0:00:17.46
  eval_samples            =       6798
  eval_samples_per_second =    389.319
  eval_steps_per_second   =     48.679
09/09/2024 17:53:36 - INFO - __main__ -   *** Predict ***
[INFO|trainer.py:811] 2024-09-09 17:53:36,253 >> The following columns in the test set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags. If tokens, id, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 17:53:36,256 >> 
***** Running Prediction *****
[INFO|trainer.py:3821] 2024-09-09 17:53:36,256 >>   Num examples = 14605
[INFO|trainer.py:3824] 2024-09-09 17:53:36,256 >>   Batch size = 8

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[INFO|trainer.py:3503] 2024-09-09 17:54:09,716 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 17:54:09,717 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 17:54:10,903 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 17:54:10,904 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 17:54:10,904 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
  predict_accuracy           =     0.9981
  predict_f1                 =     0.8922
  predict_loss               =     0.0127
  predict_precision          =     0.8768
  predict_recall             =     0.9082
  predict_runtime            = 0:00:32.79
  predict_samples_per_second =    445.297
  predict_steps_per_second   =     55.674

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