2024-09-05 20:46:30.525749: 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 20:46:30.543947: 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 20:46:30.565279: 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 20:46:30.571709: 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 20:46:30.587388: 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 20:46:31.893073: 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 20:46:34 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/05/2024 20:46:34 - 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 20:46:49,939 >> 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] 2024-09-05 20:46:49,949 >> 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] 2024-09-05 20:46:52,215 >> 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] 2024-09-05 20:46:52,215 >> 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] 2024-09-05 20:46:52,215 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-05 20:46:52,215 >> 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] 2024-09-05 20:46:52,215 >> 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] 2024-09-05 20:46:54,406 >> 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] 2024-09-05 20:46:54,546 >> 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] 2024-09-05 20:46:54,546 >> 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] 2024-09-05 20:47:01,019 >> The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:2134] 2024-09-05 20:47:01,709 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-05 20:47:01,709 >>   Num examples = 27,967
[INFO|trainer.py:2136] 2024-09-05 20:47:01,709 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-05 20:47:01,709 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-05 20:47:01,709 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-05 20:47:01,709 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-05 20:47:01,709 >>   Total optimization steps = 4,370
[INFO|trainer.py:2143] 2024-09-05 20:47:01,710 >>   Number of trainable parameters = 107,644,419

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[INFO|trainer.py:3819] 2024-09-05 20:48:30,656 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 20:48:30,656 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 20:48:30,656 >>   Batch size = 8


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                                                 [INFO|trainer.py:3503] 2024-09-05 20:48:44,033 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-437
[INFO|configuration_utils.py:472] 2024-09-05 20:48:44,034 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-437/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 20:48:44,918 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-437/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:48:44,919 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-437/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:48:44,919 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-437/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:48:46,827 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:48:46,827 >> 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 20:50:14,337 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 20:50:14,337 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 20:50:14,337 >>   Batch size = 8
{'eval_loss': 0.004654619377106428, 'eval_precision': 0.8994565217391305, 'eval_recall': 0.9254426840633737, 'eval_f1': 0.9122645842903078, 'eval_accuracy': 0.9984720788100354, 'eval_runtime': 13.3719, 'eval_samples_per_second': 519.448, 'eval_steps_per_second': 64.987, 'epoch': 1.0}
{'loss': 0.0144, 'grad_norm': 0.35407835245132446, 'learning_rate': 4.4279176201373e-05, 'epoch': 1.14}


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                                                 [INFO|trainer.py:3503] 2024-09-05 20:50:27,616 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-874
[INFO|configuration_utils.py:472] 2024-09-05 20:50:27,618 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-874/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 20:50:28,497 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-874/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:50:28,498 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-874/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:50:28,499 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-874/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:50:30,468 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:50:30,468 >> 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 20:51:57,733 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 20:51:57,733 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 20:51:57,733 >>   Batch size = 8
{'eval_loss': 0.005305842496454716, 'eval_precision': 0.8959641255605382, 'eval_recall': 0.9310344827586207, 'eval_f1': 0.9131627056672761, 'eval_accuracy': 0.9984511483827756, 'eval_runtime': 13.2738, 'eval_samples_per_second': 523.286, 'eval_steps_per_second': 65.467, 'epoch': 2.0}
{'loss': 0.0038, 'grad_norm': 0.008026196621358395, 'learning_rate': 3.8558352402745995e-05, 'epoch': 2.29}


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                                                 [INFO|trainer.py:3503] 2024-09-05 20:52:10,982 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1311
[INFO|configuration_utils.py:472] 2024-09-05 20:52:10,983 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1311/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 20:52:11,865 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1311/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:52:11,866 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1311/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:52:11,866 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1311/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:52:13,842 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:52:13,842 >> 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 20:53:41,142 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 20:53:41,143 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 20:53:41,143 >>   Batch size = 8
{'eval_loss': 0.004605981521308422, 'eval_precision': 0.9297597042513863, 'eval_recall': 0.9375582479030755, 'eval_f1': 0.9336426914153132, 'eval_accuracy': 0.9987999888371054, 'eval_runtime': 13.2443, 'eval_samples_per_second': 524.45, 'eval_steps_per_second': 65.613, 'epoch': 3.0}
{'loss': 0.0022, 'grad_norm': 0.13839516043663025, 'learning_rate': 3.2837528604119e-05, 'epoch': 3.43}


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                                                 [INFO|trainer.py:3503] 2024-09-05 20:53:54,604 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1748
[INFO|configuration_utils.py:472] 2024-09-05 20:53:54,605 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1748/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 20:53:55,495 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1748/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:53:55,496 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1748/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:53:55,496 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1748/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:53:58,426 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:53:58,426 >> 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 20:55:25,713 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 20:55:25,713 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 20:55:25,713 >>   Batch size = 8
{'eval_loss': 0.005482749082148075, 'eval_precision': 0.9202226345083488, 'eval_recall': 0.9245107176141659, 'eval_f1': 0.9223616922361693, 'eval_accuracy': 0.9985627773281612, 'eval_runtime': 13.4564, 'eval_samples_per_second': 516.186, 'eval_steps_per_second': 64.579, 'epoch': 4.0}
{'loss': 0.0019, 'grad_norm': 0.01504958514124155, 'learning_rate': 2.7116704805491993e-05, 'epoch': 4.58}


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                                                 [INFO|trainer.py:3503] 2024-09-05 20:55:38,999 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2185
[INFO|configuration_utils.py:472] 2024-09-05 20:55:39,001 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2185/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 20:55:39,884 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2185/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:55:39,885 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2185/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:55:39,885 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2185/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:55:41,919 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:55:41,919 >> 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 20:57:09,164 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 20:57:09,164 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 20:57:09,164 >>   Batch size = 8
{'eval_loss': 0.005268561653792858, 'eval_precision': 0.9118181818181819, 'eval_recall': 0.934762348555452, 'eval_f1': 0.9231477220432582, 'eval_accuracy': 0.9985627773281612, 'eval_runtime': 13.2814, 'eval_samples_per_second': 522.986, 'eval_steps_per_second': 65.43, 'epoch': 5.0}
{'loss': 0.0014, 'grad_norm': 0.11815565079450607, 'learning_rate': 2.139588100686499e-05, 'epoch': 5.72}


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                                                 [INFO|trainer.py:3503] 2024-09-05 20:57:22,279 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2622
[INFO|configuration_utils.py:472] 2024-09-05 20:57:22,280 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2622/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 20:57:23,162 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2622/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:57:23,163 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2622/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:57:23,163 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2622/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:57:24,970 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:57:24,970 >> 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 20:58:51,944 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 20:58:51,944 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 20:58:51,945 >>   Batch size = 8
{'eval_loss': 0.005419280380010605, 'eval_precision': 0.9194444444444444, 'eval_recall': 0.9254426840633737, 'eval_f1': 0.92243381328379, 'eval_accuracy': 0.998618591800854, 'eval_runtime': 13.1103, 'eval_samples_per_second': 529.811, 'eval_steps_per_second': 66.284, 'epoch': 6.0}
{'loss': 0.0009, 'grad_norm': 0.012974879704415798, 'learning_rate': 1.5675057208237986e-05, 'epoch': 6.86}


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                                                 [INFO|trainer.py:3503] 2024-09-05 20:59:05,119 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3059
[INFO|configuration_utils.py:472] 2024-09-05 20:59:05,120 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3059/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 20:59:06,004 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3059/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:59:06,005 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3059/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:59:06,005 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3059/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 20:59:07,866 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 20:59:07,867 >> 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 21:00:35,233 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 21:00:35,233 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 21:00:35,233 >>   Batch size = 8
{'eval_loss': 0.007274709176272154, 'eval_precision': 0.9323943661971831, 'eval_recall': 0.9254426840633737, 'eval_f1': 0.9289055191768008, 'eval_accuracy': 0.9986255686099406, 'eval_runtime': 13.1693, 'eval_samples_per_second': 527.439, 'eval_steps_per_second': 65.987, 'epoch': 7.0}


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                                                 [INFO|trainer.py:3503] 2024-09-05 21:00:48,434 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3496
[INFO|configuration_utils.py:472] 2024-09-05 21:00:48,435 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3496/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 21:00:49,321 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3496/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 21:00:49,322 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3496/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 21:00:49,322 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3496/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 21:00:51,243 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 21:00:51,244 >> 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 21:02:18,472 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 21:02:18,472 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 21:02:18,472 >>   Batch size = 8
{'eval_loss': 0.006545887794345617, 'eval_precision': 0.9341486359360301, 'eval_recall': 0.9254426840633737, 'eval_f1': 0.9297752808988765, 'eval_accuracy': 0.9986604526553735, 'eval_runtime': 13.1956, 'eval_samples_per_second': 526.386, 'eval_steps_per_second': 65.855, 'epoch': 8.0}
{'loss': 0.0005, 'grad_norm': 0.0015736627392470837, 'learning_rate': 9.954233409610985e-06, 'epoch': 8.01}


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                                                 [INFO|trainer.py:3503] 2024-09-05 21:02:31,982 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3933
[INFO|configuration_utils.py:472] 2024-09-05 21:02:31,984 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3933/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 21:02:32,864 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3933/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 21:02:32,865 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3933/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 21:02:32,866 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3933/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 21:02:34,895 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 21:02:34,895 >> 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 21:04:01,727 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4370/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 21:04:02,604 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4370/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 21:04:02,605 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4370/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 21:04:02,606 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4370/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 21:04:04,446 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 21:04:04,446 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-05 21:04:04,459 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 21:04:04,462 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 21:04:04,462 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 21:04:04,462 >>   Batch size = 8
{'eval_loss': 0.006908744107931852, 'eval_precision': 0.9326473339569691, 'eval_recall': 0.9291705498602051, 'eval_f1': 0.930905695611578, 'eval_accuracy': 0.9986674294644602, 'eval_runtime': 13.5052, 'eval_samples_per_second': 514.321, 'eval_steps_per_second': 64.346, 'epoch': 9.0}
{'loss': 0.0004, 'grad_norm': 0.0005491800257004797, 'learning_rate': 4.233409610983982e-06, 'epoch': 9.15}


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                                                 [INFO|trainer.py:3503] 2024-09-05 21:04:17,886 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4370
[INFO|configuration_utils.py:472] 2024-09-05 21:04:17,888 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4370/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 21:04:19,128 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4370/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 21:04:19,130 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4370/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 21:04:19,130 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4370/special_tokens_map.json
[INFO|trainer.py:2394] 2024-09-05 21:04:20,556 >> 

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


[INFO|trainer.py:2632] 2024-09-05 21:04:20,556 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-1311 (score: 0.9336426914153132).

                                                   

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[INFO|trainer.py:4283] 2024-09-05 21:04:20,741 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-05 21:04:33,780 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 21:04:33,782 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 21:04:35,047 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 21:04:35,048 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 21:04:35,049 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3503] 2024-09-05 21:04:35,062 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 21:04:35,063 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 21:04:36,304 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 21:04:36,305 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 21:04:36,305 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.00707679707556963, 'eval_precision': 0.924860853432282, 'eval_recall': 0.9291705498602051, 'eval_f1': 0.9270106927010694, 'eval_accuracy': 0.9986534758462869, 'eval_runtime': 13.4189, 'eval_samples_per_second': 517.629, 'eval_steps_per_second': 64.76, 'epoch': 10.0}
{'train_runtime': 1039.0289, 'train_samples_per_second': 269.165, 'train_steps_per_second': 4.206, 'train_loss': 0.002938754050150616, 'epoch': 10.0}

events.out.tfevents.1725569221.c3806e32a2f8.1237.0:   0%|          | 0.00/11.9k [00:00<?, ?B/s]
events.out.tfevents.1725569221.c3806e32a2f8.1237.0: 100%|██████████| 11.9k/11.9k [00:00<00:00, 27.2kB/s]
***** train metrics *****
  epoch                    =       10.0
  total_flos               = 10385610GF
  train_loss               =     0.0029
  train_runtime            = 0:17:19.02
  train_samples            =      27967
  train_samples_per_second =    269.165
  train_steps_per_second   =      4.206
09/05/2024 21:04:40 - INFO - __main__ -   *** Evaluate ***
[INFO|trainer.py:811] 2024-09-05 21:04:40,816 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 21:04:40,819 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 21:04:40,819 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 21:04:40,819 >>   Batch size = 8

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***** eval metrics *****
  epoch                   =       10.0
  eval_accuracy           =     0.9988
  eval_f1                 =     0.9336
  eval_loss               =     0.0046
  eval_precision          =     0.9298
  eval_recall             =     0.9376
  eval_runtime            = 0:00:13.27
  eval_samples            =       6946
  eval_samples_per_second =    523.342
  eval_steps_per_second   =     65.474
09/05/2024 21:04:54 - INFO - __main__ -   *** Predict ***
[INFO|trainer.py:811] 2024-09-05 21:04:54,097 >> The following columns in the test set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 21:04:54,099 >> 
***** Running Prediction *****
[INFO|trainer.py:3821] 2024-09-05 21:04:54,099 >>   Num examples = 14715
[INFO|trainer.py:3824] 2024-09-05 21:04:54,099 >>   Batch size = 8

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[INFO|trainer.py:3503] 2024-09-05 21:05:20,650 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 21:05:20,652 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 21:05:21,937 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 21:05:21,938 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 21:05:21,939 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
  predict_accuracy           =     0.9987
  predict_f1                 =     0.9206
  predict_loss               =      0.005
  predict_precision          =     0.8939
  predict_recall             =     0.9489
  predict_runtime            = 0:00:25.75
  predict_samples_per_second =    571.412
  predict_steps_per_second   =     71.451

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events.out.tfevents.1725570294.c3806e32a2f8.1237.1: 100%|██████████| 560/560 [00:00<00:00, 1.95kB/s]