2024-09-09 18:51:43.893004: 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 18:51:43.910950: 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 18:51:43.932149: 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 18:51:43.938564: 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 18:51:43.953786: 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 18:51:45.193822: 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 18:51:47 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/09/2024 18:51:47 - 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 18:52:04,045 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/config.json
[INFO|configuration_utils.py:800] 2024-09-09 18:52:04,054 >> Model config BertConfig {
  "_name_or_path": "michiyasunaga/BioLinkBERT-base",
  "architectures": [
    "BertModel"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "finetuning_task": "ner",
  "gradient_checkpointing": false,
  "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",
  "transformers_version": "4.44.2",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 28895
}

[INFO|tokenization_utils_base.py:2269] 2024-09-09 18:52:06,645 >> loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/vocab.txt
[INFO|tokenization_utils_base.py:2269] 2024-09-09 18:52:06,645 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/tokenizer.json
[INFO|tokenization_utils_base.py:2269] 2024-09-09 18:52:06,645 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-09 18:52:06,645 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/special_tokens_map.json
[INFO|tokenization_utils_base.py:2269] 2024-09-09 18:52:06,645 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/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 18:52:37,998 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/pytorch_model.bin
[INFO|modeling_utils.py:4497] 2024-09-09 18:52:38,132 >> Some weights of the model checkpoint at michiyasunaga/BioLinkBERT-base were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.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 18:52:38,132 >> Some weights of BertForTokenClassification were not initialized from the model checkpoint at michiyasunaga/BioLinkBERT-base 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)

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[INFO|trainer.py:811] 2024-09-09 18:52:45,006 >> The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:2134] 2024-09-09 18:52:45,659 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-09 18:52:45,659 >>   Num examples = 27,841
[INFO|trainer.py:2136] 2024-09-09 18:52:45,659 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-09 18:52:45,659 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-09 18:52:45,659 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-09 18:52:45,659 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-09 18:52:45,659 >>   Total optimization steps = 4,350
[INFO|trainer.py:2143] 2024-09-09 18:52:45,660 >>   Number of trainable parameters = 107,644,419

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[INFO|trainer.py:3819] 2024-09-09 18:55:23,833 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 18:55:23,833 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 18:55:23,833 >>   Batch size = 8


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                                                 [INFO|trainer.py:3503] 2024-09-09 18:55:38,835 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-435
[INFO|configuration_utils.py:472] 2024-09-09 18:55:38,837 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-435/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 18:55:39,714 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-435/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 18:55:39,715 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-435/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 18:55:39,716 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-435/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 18:55:46,753 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 18:55:46,753 >> 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 18:58:26,516 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 18:58:26,516 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 18:58:26,516 >>   Batch size = 8
{'eval_loss': 0.006047643255442381, 'eval_precision': 0.8713656387665198, 'eval_recall': 0.9217148182665424, 'eval_f1': 0.8958333333333334, 'eval_accuracy': 0.9981441687829654, 'eval_runtime': 14.9983, 'eval_samples_per_second': 463.12, 'eval_steps_per_second': 57.94, 'epoch': 1.0}
{'loss': 0.0156, 'grad_norm': 0.025908155366778374, 'learning_rate': 4.4252873563218394e-05, 'epoch': 1.15}


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                                                 [INFO|trainer.py:3503] 2024-09-09 18:58:41,678 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-871
[INFO|configuration_utils.py:472] 2024-09-09 18:58:41,679 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-871/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 18:58:42,570 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-871/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 18:58:42,571 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-871/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 18:58:42,572 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-871/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 18:58:46,082 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 18:58:46,082 >> 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 19:01:25,496 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 19:01:25,496 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 19:01:25,496 >>   Batch size = 8
{'eval_loss': 0.004434330388903618, 'eval_precision': 0.9182915506035283, 'eval_recall': 0.9217148182665424, 'eval_f1': 0.92, 'eval_accuracy': 0.9986604526553735, 'eval_runtime': 15.157, 'eval_samples_per_second': 458.27, 'eval_steps_per_second': 57.333, 'epoch': 2.0}
{'loss': 0.0038, 'grad_norm': 0.015398409217596054, 'learning_rate': 3.850574712643678e-05, 'epoch': 2.3}


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                                                 [INFO|trainer.py:3503] 2024-09-09 19:01:40,633 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1306
[INFO|configuration_utils.py:472] 2024-09-09 19:01:40,634 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1306/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:01:41,460 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1306/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:01:41,461 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1306/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:01:41,461 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1306/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:01:46,567 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:01:46,567 >> 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 19:04:26,022 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 19:04:26,022 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 19:04:26,022 >>   Batch size = 8
{'eval_loss': 0.0040275463834404945, 'eval_precision': 0.8968888888888888, 'eval_recall': 0.940354147250699, 'eval_f1': 0.9181073703366698, 'eval_accuracy': 0.9986883598917199, 'eval_runtime': 15.1362, 'eval_samples_per_second': 458.9, 'eval_steps_per_second': 57.412, 'epoch': 3.0}
{'loss': 0.0025, 'grad_norm': 0.024467067793011665, 'learning_rate': 3.275862068965517e-05, 'epoch': 3.44}


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                                                 [INFO|trainer.py:3503] 2024-09-09 19:04:41,304 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1742
[INFO|configuration_utils.py:472] 2024-09-09 19:04:41,305 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1742/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:04:42,184 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1742/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:04:42,185 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1742/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:04:42,185 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1742/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:04:48,201 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:04:48,202 >> 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 19:07:28,601 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 19:07:28,601 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 19:07:28,601 >>   Batch size = 8
{'eval_loss': 0.004492571111768484, 'eval_precision': 0.9077757685352622, 'eval_recall': 0.9356943150046598, 'eval_f1': 0.9215236346948141, 'eval_accuracy': 0.9985837077554209, 'eval_runtime': 15.2768, 'eval_samples_per_second': 454.675, 'eval_steps_per_second': 56.884, 'epoch': 4.0}
{'loss': 0.0016, 'grad_norm': 0.014663909561932087, 'learning_rate': 2.7011494252873566e-05, 'epoch': 4.59}


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                                                 [INFO|trainer.py:3503] 2024-09-09 19:07:43,617 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2177
[INFO|configuration_utils.py:472] 2024-09-09 19:07:43,618 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2177/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:07:44,503 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2177/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:07:44,504 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2177/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:07:44,505 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2177/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:07:46,731 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:07:46,731 >> 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 19:10:26,358 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 19:10:26,358 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 19:10:26,358 >>   Batch size = 8
{'eval_loss': 0.0054086255840957165, 'eval_precision': 0.9181561618062088, 'eval_recall': 0.9095992544268406, 'eval_f1': 0.9138576779026217, 'eval_accuracy': 0.9985906845645076, 'eval_runtime': 15.0107, 'eval_samples_per_second': 462.737, 'eval_steps_per_second': 57.892, 'epoch': 5.0}
{'loss': 0.0011, 'grad_norm': 0.002929804613813758, 'learning_rate': 2.1264367816091954e-05, 'epoch': 5.74}


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                                                 [INFO|trainer.py:3503] 2024-09-09 19:10:41,572 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2613
[INFO|configuration_utils.py:472] 2024-09-09 19:10:41,574 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2613/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:10:42,447 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2613/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:10:42,448 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2613/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:10:42,449 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2613/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:10:48,964 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:10:48,965 >> 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 19:13:28,809 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 19:13:28,809 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 19:13:28,809 >>   Batch size = 8
{'eval_loss': 0.00529602263122797, 'eval_precision': 0.9152073732718894, 'eval_recall': 0.9254426840633737, 'eval_f1': 0.9202965708989805, 'eval_accuracy': 0.9986395222281137, 'eval_runtime': 15.2091, 'eval_samples_per_second': 456.701, 'eval_steps_per_second': 57.137, 'epoch': 6.0}
{'loss': 0.0009, 'grad_norm': 0.06796916574239731, 'learning_rate': 1.5517241379310346e-05, 'epoch': 6.89}


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                                                 [INFO|trainer.py:3503] 2024-09-09 19:13:43,814 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3048
[INFO|configuration_utils.py:472] 2024-09-09 19:13:43,816 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3048/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:13:44,696 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3048/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:13:44,697 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3048/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:13:44,698 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3048/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:13:51,141 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:13:51,141 >> 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 19:16:30,235 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 19:16:30,235 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 19:16:30,236 >>   Batch size = 8
{'eval_loss': 0.006041177082806826, 'eval_precision': 0.9262672811059908, 'eval_recall': 0.9366262814538676, 'eval_f1': 0.9314179796107507, 'eval_accuracy': 0.9986604526553735, 'eval_runtime': 15.0006, 'eval_samples_per_second': 463.048, 'eval_steps_per_second': 57.931, 'epoch': 7.0}


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                                                 [INFO|trainer.py:3503] 2024-09-09 19:16:45,441 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3484
[INFO|configuration_utils.py:472] 2024-09-09 19:16:45,443 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3484/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:16:46,316 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3484/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:16:46,318 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3484/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:16:46,318 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3484/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:16:54,182 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:16:54,182 >> 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 19:19:33,407 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 19:19:33,407 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 19:19:33,407 >>   Batch size = 8
{'eval_loss': 0.005891016684472561, 'eval_precision': 0.9181073703366697, 'eval_recall': 0.940354147250699, 'eval_f1': 0.9290976058931859, 'eval_accuracy': 0.9987511511734993, 'eval_runtime': 15.2005, 'eval_samples_per_second': 456.957, 'eval_steps_per_second': 57.169, 'epoch': 8.0}
{'loss': 0.0005, 'grad_norm': 0.0015728959115222096, 'learning_rate': 9.770114942528738e-06, 'epoch': 8.04}


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                                                 [INFO|trainer.py:3503] 2024-09-09 19:19:48,814 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3919
[INFO|configuration_utils.py:472] 2024-09-09 19:19:48,815 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3919/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:19:49,701 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3919/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:19:49,702 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3919/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:19:49,703 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3919/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:19:54,517 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:19:54,517 >> 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 19:22:32,730 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4350/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:22:33,632 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4350/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:22:33,633 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4350/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:22:33,633 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4350/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:22:37,044 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:22:37,045 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-09 19:22:37,058 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 19:22:37,061 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 19:22:37,061 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 19:22:37,061 >>   Batch size = 8
{'eval_loss': 0.006726197898387909, 'eval_precision': 0.9257884972170687, 'eval_recall': 0.9301025163094129, 'eval_f1': 0.9279404927940494, 'eval_accuracy': 0.9987581279825859, 'eval_runtime': 15.4019, 'eval_samples_per_second': 450.984, 'eval_steps_per_second': 56.422, 'epoch': 9.0}
{'loss': 0.0003, 'grad_norm': 0.11025261133909225, 'learning_rate': 4.022988505747127e-06, 'epoch': 9.18}


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                                                 [INFO|trainer.py:3503] 2024-09-09 19:22:52,334 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4350
[INFO|configuration_utils.py:472] 2024-09-09 19:22:52,336 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4350/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:22:53,567 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4350/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:22:53,568 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4350/tokenizer_config.json
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[INFO|trainer.py:2394] 2024-09-09 19:22:55,109 >> 

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


[INFO|trainer.py:2632] 2024-09-09 19:22:55,109 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-4350 (score: 0.9320297951582869).

                                                   

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[INFO|trainer.py:4283] 2024-09-09 19:22:55,259 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-09 19:23:03,508 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 19:23:03,510 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:23:04,785 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:23:04,786 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:23:04,786 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3503] 2024-09-09 19:23:04,799 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 19:23:04,800 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:23:05,818 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:23:05,819 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:23:05,819 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.007080046460032463, 'eval_precision': 0.9311627906976744, 'eval_recall': 0.9328984156570364, 'eval_f1': 0.9320297951582869, 'eval_accuracy': 0.998772081600759, 'eval_runtime': 15.2679, 'eval_samples_per_second': 454.941, 'eval_steps_per_second': 56.917, 'epoch': 9.99}
{'train_runtime': 1809.5975, 'train_samples_per_second': 153.852, 'train_steps_per_second': 2.404, 'train_loss': 0.003044272955806776, 'epoch': 9.99}

events.out.tfevents.1725907965.3d77e24b7860.2139.0:   0%|          | 0.00/11.9k [00:00<?, ?B/s]
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***** train metrics *****
  epoch                    =     9.9885
  total_flos               = 10322898GF
  train_loss               =      0.003
  train_runtime            = 0:30:09.59
  train_samples            =      27841
  train_samples_per_second =    153.852
  train_steps_per_second   =      2.404
09/09/2024 19:23:10 - INFO - __main__ -   *** Evaluate ***
[INFO|trainer.py:811] 2024-09-09 19:23:10,626 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 19:23:10,629 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 19:23:10,629 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 19:23:10,629 >>   Batch size = 8

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***** eval metrics *****
  epoch                   =     9.9885
  eval_accuracy           =     0.9988
  eval_f1                 =      0.932
  eval_loss               =     0.0071
  eval_precision          =     0.9312
  eval_recall             =     0.9329
  eval_runtime            = 0:00:15.14
  eval_samples            =       6946
  eval_samples_per_second =     458.52
  eval_steps_per_second   =     57.365
09/09/2024 19:23:25 - INFO - __main__ -   *** Predict ***
[INFO|trainer.py:811] 2024-09-09 19:23:25,784 >> The following columns in the test set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 19:23:25,786 >> 
***** Running Prediction *****
[INFO|trainer.py:3821] 2024-09-09 19:23:25,786 >>   Num examples = 14715
[INFO|trainer.py:3824] 2024-09-09 19:23:25,786 >>   Batch size = 8

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[INFO|trainer.py:3503] 2024-09-09 19:23:54,954 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 19:23:54,956 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 19:23:56,214 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 19:23:56,215 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 19:23:56,216 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
  predict_accuracy           =     0.9986
  predict_f1                 =     0.9153
  predict_loss               =     0.0081
  predict_precision          =     0.8907
  predict_recall             =     0.9413
  predict_runtime            = 0:00:28.41
  predict_samples_per_second =    517.915
  predict_steps_per_second   =     64.761

events.out.tfevents.1725909805.3d77e24b7860.2139.1:   0%|          | 0.00/560 [00:00<?, ?B/s]
events.out.tfevents.1725909805.3d77e24b7860.2139.1: 100%|██████████| 560/560 [00:00<00:00, 2.07kB/s]