2024-09-05 13:34:48.258223: 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`.
2024-09-05 13:34:48.275126: 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
2024-09-05 13:34:48.295825: 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
2024-09-05 13:34:48.302058: 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
2024-09-05 13:34:48.316617: 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.
2024-09-05 13:34:49.554072: 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(
09/05/2024 13:34:51 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/05/2024 13:34:51 - 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] 2024-09-05 13:35:02,046 >> 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] 2024-09-05 13:35:02,050 >> 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] 2024-09-05 13:35:02,109 >> 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] 2024-09-05 13:35:02,109 >> 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] 2024-09-05 13:35:02,109 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-05 13:35:02,109 >> 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] 2024-09-05 13:35:02,109 >> 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] 2024-09-05 13:35:02,174 >> 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] 2024-09-05 13:35:02,231 >> 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] 2024-09-05 13:35:02,231 >> 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] 2024-09-05 13:35:08,966 >> The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:2134] 2024-09-05 13:35:09,528 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-05 13:35:09,528 >>   Num examples = 30,642
[INFO|trainer.py:2136] 2024-09-05 13:35:09,528 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-05 13:35:09,528 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-05 13:35:09,528 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-05 13:35:09,528 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-05 13:35:09,528 >>   Total optimization steps = 4,790
[INFO|trainer.py:2143] 2024-09-05 13:35:09,529 >>   Number of trainable parameters = 109,339,395

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 10%|█         | 479/4790 [02:10<19:05,  3.76it/s][INFO|trainer.py:811] 2024-09-05 13:37:19,924 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 13:37:19,927 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 13:37:19,927 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 13:37:19,927 >>   Batch size = 8


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                                                 [INFO|trainer.py:3503] 2024-09-05 13:37:33,956 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-479
[INFO|configuration_utils.py:472] 2024-09-05 13:37:33,957 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-479/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:37:34,851 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-479/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:37:34,852 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-479/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:37:34,852 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-479/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:37:36,650 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:37:36,650 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-05 13:39:46,229 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 13:39:46,229 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 13:39:46,229 >>   Batch size = 8
{'eval_loss': 0.005215654149651527, 'eval_precision': 0.8498233215547704, 'eval_recall': 0.9312681510164569, 'eval_f1': 0.8886836027713626, 'eval_accuracy': 0.998338094001558, 'eval_runtime': 14.0241, 'eval_samples_per_second': 484.736, 'eval_steps_per_second': 60.61, 'epoch': 1.0}
{'loss': 0.0127, 'grad_norm': 0.063787080347538, 'learning_rate': 4.478079331941545e-05, 'epoch': 1.04}


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                                                 [INFO|trainer.py:3503] 2024-09-05 13:40:00,215 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-958
[INFO|configuration_utils.py:472] 2024-09-05 13:40:00,216 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-958/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:40:01,063 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-958/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:40:01,064 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-958/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:40:01,065 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-958/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:40:05,674 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:40:05,674 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-05 13:42:15,674 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 13:42:15,674 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 13:42:15,674 >>   Batch size = 8
{'eval_loss': 0.0056168013252317905, 'eval_precision': 0.9062801932367149, 'eval_recall': 0.9080348499515973, 'eval_f1': 0.90715667311412, 'eval_accuracy': 0.9983965203843158, 'eval_runtime': 13.9845, 'eval_samples_per_second': 486.11, 'eval_steps_per_second': 60.782, 'epoch': 2.0}
{'loss': 0.0035, 'grad_norm': 0.30002298951148987, 'learning_rate': 3.95615866388309e-05, 'epoch': 2.09}


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                                                 [INFO|trainer.py:3503] 2024-09-05 13:42:29,735 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1437
[INFO|configuration_utils.py:472] 2024-09-05 13:42:29,736 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1437/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:42:30,589 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1437/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:42:30,590 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1437/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:42:30,591 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1437/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:42:32,379 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:42:32,379 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-05 13:44:41,663 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 13:44:41,663 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 13:44:41,663 >>   Batch size = 8
{'eval_loss': 0.0046853721141815186, 'eval_precision': 0.9211295034079844, 'eval_recall': 0.9157792836398838, 'eval_f1': 0.9184466019417475, 'eval_accuracy': 0.9985393404310569, 'eval_runtime': 14.0593, 'eval_samples_per_second': 483.522, 'eval_steps_per_second': 60.458, 'epoch': 3.0}
{'loss': 0.002, 'grad_norm': 0.03608839586377144, 'learning_rate': 3.434237995824635e-05, 'epoch': 3.13}


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                                                 [INFO|trainer.py:3503] 2024-09-05 13:44:55,874 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1916
[INFO|configuration_utils.py:472] 2024-09-05 13:44:55,876 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1916/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:44:56,719 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1916/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:44:56,720 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1916/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:44:56,720 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1916/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:44:58,615 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:44:58,616 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-05 13:47:08,666 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 13:47:08,666 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 13:47:08,666 >>   Batch size = 8
{'eval_loss': 0.006538054905831814, 'eval_precision': 0.9027911453320501, 'eval_recall': 0.9080348499515973, 'eval_f1': 0.9054054054054055, 'eval_accuracy': 0.9984030122046221, 'eval_runtime': 14.21, 'eval_samples_per_second': 478.395, 'eval_steps_per_second': 59.817, 'epoch': 4.0}
{'loss': 0.0014, 'grad_norm': 0.002257826505228877, 'learning_rate': 2.9123173277661797e-05, 'epoch': 4.18}


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                                                 [INFO|trainer.py:3503] 2024-09-05 13:47:22,664 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2395
[INFO|configuration_utils.py:472] 2024-09-05 13:47:22,665 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2395/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:47:23,515 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2395/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:47:23,516 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2395/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:47:23,517 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2395/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:47:26,099 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:47:26,100 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-05 13:49:35,931 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 13:49:35,931 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 13:49:35,931 >>   Batch size = 8
{'eval_loss': 0.006093208212405443, 'eval_precision': 0.9193083573487032, 'eval_recall': 0.9264278799612778, 'eval_f1': 0.922854387656702, 'eval_accuracy': 0.9985847831732018, 'eval_runtime': 13.9969, 'eval_samples_per_second': 485.68, 'eval_steps_per_second': 60.728, 'epoch': 5.0}
{'loss': 0.0007, 'grad_norm': 0.0006600466440431774, 'learning_rate': 2.3903966597077245e-05, 'epoch': 5.22}


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                                                 [INFO|trainer.py:3503] 2024-09-05 13:49:49,950 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2874
[INFO|configuration_utils.py:472] 2024-09-05 13:49:49,951 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2874/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:49:50,805 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2874/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:49:50,806 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2874/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:49:50,806 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2874/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:49:52,602 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:49:52,603 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-05 13:52:02,612 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 13:52:02,612 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 13:52:02,612 >>   Batch size = 8
{'eval_loss': 0.006890583783388138, 'eval_precision': 0.9246231155778895, 'eval_recall': 0.8906098741529526, 'eval_f1': 0.9072978303747534, 'eval_accuracy': 0.9984289794858479, 'eval_runtime': 14.017, 'eval_samples_per_second': 484.982, 'eval_steps_per_second': 60.641, 'epoch': 6.0}
{'loss': 0.0004, 'grad_norm': 0.06641975790262222, 'learning_rate': 1.8684759916492694e-05, 'epoch': 6.26}


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                                                 [INFO|trainer.py:3503] 2024-09-05 13:52:16,622 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3353
[INFO|configuration_utils.py:472] 2024-09-05 13:52:16,624 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3353/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:52:17,485 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3353/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:52:17,486 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3353/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:52:17,486 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3353/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:52:19,483 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:52:19,484 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-05 13:54:28,828 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 13:54:28,828 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 13:54:28,828 >>   Batch size = 8
{'eval_loss': 0.007136390544474125, 'eval_precision': 0.898961284230406, 'eval_recall': 0.9215876089060987, 'eval_f1': 0.910133843212237, 'eval_accuracy': 0.9985003895092184, 'eval_runtime': 14.0087, 'eval_samples_per_second': 485.271, 'eval_steps_per_second': 60.677, 'epoch': 7.0}
{'loss': 0.0003, 'grad_norm': 0.0022020984906703234, 'learning_rate': 1.3465553235908144e-05, 'epoch': 7.31}


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                                                 [INFO|trainer.py:3503] 2024-09-05 13:54:42,776 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3832
[INFO|configuration_utils.py:472] 2024-09-05 13:54:42,777 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3832/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:54:43,631 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3832/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:54:43,632 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3832/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:54:43,632 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3832/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:54:45,590 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:54:45,591 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-05 13:56:54,949 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 13:56:54,949 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 13:56:54,949 >>   Batch size = 8
{'eval_loss': 0.007608836982399225, 'eval_precision': 0.9134980988593155, 'eval_recall': 0.9303000968054211, 'eval_f1': 0.9218225419664269, 'eval_accuracy': 0.9986172422747338, 'eval_runtime': 13.9459, 'eval_samples_per_second': 487.454, 'eval_steps_per_second': 60.95, 'epoch': 8.0}
{'loss': 0.0001, 'grad_norm': 0.0032535246573388577, 'learning_rate': 8.246346555323591e-06, 'epoch': 8.35}


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                                                 [INFO|trainer.py:3503] 2024-09-05 13:57:09,224 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4311
[INFO|configuration_utils.py:472] 2024-09-05 13:57:09,225 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4311/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:57:10,089 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4311/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:57:10,090 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4311/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:57:10,090 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4311/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:57:12,041 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:57:12,041 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|configuration_utils.py:472] 2024-09-05 13:59:22,065 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4790/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:59:22,943 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4790/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:59:22,944 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4790/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:59:22,944 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4790/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:59:27,606 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:59:27,606 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-05 13:59:27,620 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 13:59:27,623 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 13:59:27,623 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 13:59:27,624 >>   Batch size = 8
{'eval_loss': 0.007967105135321617, 'eval_precision': 0.9130019120458891, 'eval_recall': 0.9244917715392061, 'eval_f1': 0.9187109187109187, 'eval_accuracy': 0.9985653077122826, 'eval_runtime': 14.2735, 'eval_samples_per_second': 476.269, 'eval_steps_per_second': 59.551, 'epoch': 9.0}
{'loss': 0.0001, 'grad_norm': 0.00014514605572912842, 'learning_rate': 3.02713987473904e-06, 'epoch': 9.39}


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                                                 [INFO|trainer.py:3503] 2024-09-05 13:59:42,565 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4790
[INFO|configuration_utils.py:472] 2024-09-05 13:59:42,567 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4790/config.json
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[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:59:43,849 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4790/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:59:43,849 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4790/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:59:46,438 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:59:46,439 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:2394] 2024-09-05 13:59:46,453 >> 

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


[INFO|trainer.py:2632] 2024-09-05 13:59:46,453 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-2395 (score: 0.922854387656702).

                                                   

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[INFO|trainer.py:4283] 2024-09-05 13:59:46,629 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-05 13:59:49,476 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 13:59:49,477 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:59:50,974 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:59:50,975 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:59:50,975 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3503] 2024-09-05 13:59:50,989 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 13:59:50,990 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:59:52,191 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:59:52,192 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:59:52,192 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.008040270768105984, 'eval_precision': 0.9107312440645774, 'eval_recall': 0.9283639883833494, 'eval_f1': 0.919463087248322, 'eval_accuracy': 0.9986107504544274, 'eval_runtime': 14.9407, 'eval_samples_per_second': 455.0, 'eval_steps_per_second': 56.892, 'epoch': 10.0}
{'train_runtime': 1477.0977, 'train_samples_per_second': 207.447, 'train_steps_per_second': 3.243, 'train_loss': 0.002211083533992847, 'epoch': 10.0}
***** train metrics *****
  epoch                    =       10.0
  total_flos               = 16190030GF
  train_loss               =     0.0022
  train_runtime            = 0:24:37.09
  train_samples            =      30642
  train_samples_per_second =    207.447
  train_steps_per_second   =      3.243
09/05/2024 13:59:55 - INFO - __main__ -   *** Evaluate ***
[INFO|trainer.py:811] 2024-09-05 13:59:55,002 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 13:59:55,005 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 13:59:55,005 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 13:59:55,005 >>   Batch size = 8

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***** eval metrics *****
  epoch                   =       10.0
  eval_accuracy           =     0.9986
  eval_f1                 =     0.9229
  eval_loss               =     0.0061
  eval_precision          =     0.9193
  eval_recall             =     0.9264
  eval_runtime            = 0:00:13.97
  eval_samples            =       6798
  eval_samples_per_second =    486.294
  eval_steps_per_second   =     60.805
09/05/2024 14:00:08 - INFO - __main__ -   *** Predict ***
[INFO|trainer.py:811] 2024-09-05 14:00:08,986 >> The following columns in the test set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: ner_tags, id, tokens. If ner_tags, id, tokens are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 14:00:08,989 >> 
***** Running Prediction *****
[INFO|trainer.py:3821] 2024-09-05 14:00:08,989 >>   Num examples = 14605
[INFO|trainer.py:3824] 2024-09-05 14:00:08,989 >>   Batch size = 8

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[INFO|trainer.py:3503] 2024-09-05 14:00:36,834 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 14:00:36,836 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 14:00:37,998 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 14:00:37,999 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 14:00:38,000 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
  predict_accuracy           =     0.9981
  predict_f1                 =       0.89
  predict_loss               =     0.0095
  predict_precision          =     0.8742
  predict_recall             =     0.9065
  predict_runtime            = 0:00:27.19
  predict_samples_per_second =    537.068
  predict_steps_per_second   =     67.147

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events.out.tfevents.1725544808.da2ff10c1388.16078.1: 100%|██████████| 560/560 [00:00<00:00, 3.61kB/s]