2024-09-05 12:36:40.610572: 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 12:36:40.628921: 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 12:36:40.650374: 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 12:36:40.660049: 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 12:36:40.677052: 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 12:36:41.931934: 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 12:36:44 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/05/2024 12:36:44 - 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 12:36:56,702 >> 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 12:36:56,711 >> 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 12:36:57,332 >> 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 12:36:57,332 >> 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 12:36:57,332 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-05 12:36:57,332 >> 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 12:36:57,332 >> 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 12:37:07,193 >> 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 12:37:07,333 >> 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 12:37:07,333 >> 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)

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[INFO|trainer.py:811] 2024-09-05 12:37:13,965 >> 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 12:37:14,657 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-05 12:37:14,657 >>   Num examples = 31,901
[INFO|trainer.py:2136] 2024-09-05 12:37:14,657 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-05 12:37:14,657 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-05 12:37:14,657 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-05 12:37:14,657 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-05 12:37:14,657 >>   Total optimization steps = 4,980
[INFO|trainer.py:2143] 2024-09-05 12:37:14,658 >>   Number of trainable parameters = 109,339,395

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 10%|█         | 498/4980 [02:17<19:12,  3.89it/s][INFO|trainer.py:811] 2024-09-05 12:39:32,083 >> 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 12:39:32,085 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 12:39:32,085 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 12:39:32,085 >>   Batch size = 8


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                                                 [INFO|trainer.py:3503] 2024-09-05 12:39:46,242 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-498
[INFO|configuration_utils.py:472] 2024-09-05 12:39:46,243 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-498/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 12:39:47,141 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-498/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:39:47,142 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-498/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:39:47,142 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-498/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:39:48,854 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:39:48,854 >> 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 12:42:04,993 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 12:42:04,994 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 12:42:04,994 >>   Batch size = 8
{'eval_loss': 0.00585820572450757, 'eval_precision': 0.8588342440801457, 'eval_recall': 0.9128751210067764, 'eval_f1': 0.8850305021116848, 'eval_accuracy': 0.9980589457283823, 'eval_runtime': 14.1517, 'eval_samples_per_second': 480.368, 'eval_steps_per_second': 60.064, 'epoch': 1.0}
{'loss': 0.0135, 'grad_norm': 0.1897086501121521, 'learning_rate': 4.497991967871486e-05, 'epoch': 1.0}


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                                                 [INFO|trainer.py:3503] 2024-09-05 12:42:19,156 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-997
[INFO|configuration_utils.py:472] 2024-09-05 12:42:19,157 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-997/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 12:42:20,016 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-997/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:42:20,017 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-997/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:42:20,017 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-997/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:42:22,101 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:42:22,101 >> 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 12:44:38,448 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 12:44:38,448 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 12:44:38,448 >>   Batch size = 8
{'eval_loss': 0.005180353298783302, 'eval_precision': 0.8777573529411765, 'eval_recall': 0.9244917715392061, 'eval_f1': 0.9005186232909005, 'eval_accuracy': 0.9984679304076863, 'eval_runtime': 14.161, 'eval_samples_per_second': 480.051, 'eval_steps_per_second': 60.024, 'epoch': 2.0}
{'loss': 0.0036, 'grad_norm': 0.1610834300518036, 'learning_rate': 3.995983935742972e-05, 'epoch': 2.01}


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                                                 [INFO|trainer.py:3503] 2024-09-05 12:44:52,672 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1495
[INFO|configuration_utils.py:472] 2024-09-05 12:44:52,673 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1495/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 12:44:53,525 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1495/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:44:53,526 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1495/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:44:53,526 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1495/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:44:55,535 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:44:55,535 >> 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 12:47:10,990 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 12:47:10,990 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 12:47:10,990 >>   Batch size = 8
{'eval_loss': 0.0061247944831848145, 'eval_precision': 0.8868101028999065, 'eval_recall': 0.9177153920619555, 'eval_f1': 0.9019980970504281, 'eval_accuracy': 0.9983705531030901, 'eval_runtime': 14.2223, 'eval_samples_per_second': 477.981, 'eval_steps_per_second': 59.765, 'epoch': 3.0}
{'loss': 0.0022, 'grad_norm': 0.16693450510501862, 'learning_rate': 3.4939759036144585e-05, 'epoch': 3.01}


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                                                 [INFO|trainer.py:3503] 2024-09-05 12:47:25,336 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1994
[INFO|configuration_utils.py:472] 2024-09-05 12:47:25,337 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1994/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 12:47:26,190 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1994/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:47:26,191 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1994/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:47:26,191 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1994/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:47:28,018 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:47:28,018 >> 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 12:49:42,620 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 12:49:42,620 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 12:49:42,620 >>   Batch size = 8
{'eval_loss': 0.005911467596888542, 'eval_precision': 0.8841911764705882, 'eval_recall': 0.9312681510164569, 'eval_f1': 0.9071192833569071, 'eval_accuracy': 0.9984809140482992, 'eval_runtime': 14.3447, 'eval_samples_per_second': 473.903, 'eval_steps_per_second': 59.255, 'epoch': 4.0}
{'loss': 0.0012, 'grad_norm': 0.07369942218065262, 'learning_rate': 2.991967871485944e-05, 'epoch': 4.01}


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                                                 [INFO|trainer.py:3503] 2024-09-05 12:49:56,795 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2492
[INFO|configuration_utils.py:472] 2024-09-05 12:49:56,796 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2492/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 12:49:57,645 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2492/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:49:57,646 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2492/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:49:57,646 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2492/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:49:59,553 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:49:59,553 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-05 12:52:15,116 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 12:52:15,116 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 12:52:15,116 >>   Batch size = 8
{'eval_loss': 0.007698884699493647, 'eval_precision': 0.8929577464788733, 'eval_recall': 0.920619554695063, 'eval_f1': 0.9065776930409915, 'eval_accuracy': 0.9984679304076863, 'eval_runtime': 14.1729, 'eval_samples_per_second': 479.648, 'eval_steps_per_second': 59.974, 'epoch': 5.0}
{'loss': 0.0006, 'grad_norm': 0.07726099342107773, 'learning_rate': 2.48995983935743e-05, 'epoch': 5.02}


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                                                 [INFO|trainer.py:3503] 2024-09-05 12:52:29,244 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2991
[INFO|configuration_utils.py:472] 2024-09-05 12:52:29,246 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2991/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 12:52:30,095 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2991/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:52:30,096 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2991/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:52:30,097 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2991/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:52:31,963 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:52:31,963 >> 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 12:54:48,756 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 12:54:48,756 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 12:54:48,756 >>   Batch size = 8
{'eval_loss': 0.007444981951266527, 'eval_precision': 0.8813247470101196, 'eval_recall': 0.9273959341723137, 'eval_f1': 0.9037735849056605, 'eval_accuracy': 0.9983705531030901, 'eval_runtime': 14.1272, 'eval_samples_per_second': 481.201, 'eval_steps_per_second': 60.168, 'epoch': 6.0}
{'loss': 0.0005, 'grad_norm': 0.026480326429009438, 'learning_rate': 1.9879518072289157e-05, 'epoch': 6.02}


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                                                 [INFO|trainer.py:3503] 2024-09-05 12:55:02,888 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3489
[INFO|configuration_utils.py:472] 2024-09-05 12:55:02,889 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3489/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 12:55:03,789 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3489/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:55:03,790 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3489/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:55:03,790 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3489/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:55:05,679 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:55:05,680 >> 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 12:57:20,944 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 12:57:20,944 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 12:57:20,944 >>   Batch size = 8
{'eval_loss': 0.0079538244754076, 'eval_precision': 0.8949343339587242, 'eval_recall': 0.9235237173281704, 'eval_f1': 0.9090042877560743, 'eval_accuracy': 0.9985068813295248, 'eval_runtime': 14.1264, 'eval_samples_per_second': 481.228, 'eval_steps_per_second': 60.171, 'epoch': 7.0}
{'loss': 0.0002, 'grad_norm': 0.00035834600566886365, 'learning_rate': 1.4859437751004016e-05, 'epoch': 7.02}


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                                                 [INFO|trainer.py:3503] 2024-09-05 12:57:35,090 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3988
[INFO|configuration_utils.py:472] 2024-09-05 12:57:35,092 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3988/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 12:57:35,945 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3988/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:57:35,946 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3988/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:57:35,946 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3988/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 12:57:37,910 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 12:57:37,910 >> 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 12:59:53,058 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 12:59:53,058 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 12:59:53,058 >>   Batch size = 8
{'eval_loss': 0.007980063557624817, 'eval_precision': 0.914505283381364, 'eval_recall': 0.9215876089060987, 'eval_f1': 0.9180327868852458, 'eval_accuracy': 0.9985847831732018, 'eval_runtime': 14.1447, 'eval_samples_per_second': 480.603, 'eval_steps_per_second': 60.093, 'epoch': 8.0}
{'loss': 0.0002, 'grad_norm': 0.00020492795738391578, 'learning_rate': 9.839357429718876e-06, 'epoch': 8.02}


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                                                 [INFO|trainer.py:3503] 2024-09-05 13:00:07,553 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4486
[INFO|configuration_utils.py:472] 2024-09-05 13:00:07,554 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4486/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:00:08,404 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4486/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:00:08,405 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4486/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:00:08,406 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4486/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:00:10,458 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:00:10,458 >> 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 13:02:24,663 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4980/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:02:25,557 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4980/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:02:25,558 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4980/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:02:25,558 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4980/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:02:27,493 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:02:27,494 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-05 13:02:27,509 >> 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 13:02:27,512 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 13:02:27,512 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 13:02:27,512 >>   Batch size = 8
{'eval_loss': 0.008729132823646069, 'eval_precision': 0.9001883239171374, 'eval_recall': 0.925459825750242, 'eval_f1': 0.9126491646778042, 'eval_accuracy': 0.9985523240716697, 'eval_runtime': 14.4933, 'eval_samples_per_second': 469.045, 'eval_steps_per_second': 58.648, 'epoch': 9.0}
{'loss': 0.0001, 'grad_norm': 0.012676490470767021, 'learning_rate': 4.819277108433735e-06, 'epoch': 9.03}


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                                                 [INFO|trainer.py:3503] 2024-09-05 13:02:41,990 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4980
[INFO|configuration_utils.py:472] 2024-09-05 13:02:41,992 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4980/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:02:43,193 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4980/model.safetensors
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[INFO|trainer.py:2394] 2024-09-05 13:02:44,633 >> 

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


[INFO|trainer.py:2632] 2024-09-05 13:02:44,633 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-3988 (score: 0.9180327868852458).

                                                   

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[INFO|trainer.py:4283] 2024-09-05 13:02:44,811 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-05 13:02:46,282 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 13:02:46,284 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:02:47,559 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
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[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:02:47,560 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3503] 2024-09-05 13:02:47,574 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 13:02:47,575 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:02:48,839 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:02:48,840 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:02:48,840 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.00890978705137968, 'eval_precision': 0.9064885496183206, 'eval_recall': 0.9196515004840271, 'eval_f1': 0.913022585295531, 'eval_accuracy': 0.9985393404310569, 'eval_runtime': 14.4766, 'eval_samples_per_second': 469.584, 'eval_steps_per_second': 58.715, 'epoch': 9.99}
{'train_runtime': 1530.15, 'train_samples_per_second': 208.483, 'train_steps_per_second': 3.255, 'train_loss': 0.002228877000907159, 'epoch': 9.99}

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***** train metrics *****
  epoch                    =       9.99
  total_flos               = 16887518GF
  train_loss               =     0.0022
  train_runtime            = 0:25:30.15
  train_samples            =      31901
  train_samples_per_second =    208.483
  train_steps_per_second   =      3.255
09/05/2024 13:02:51 - INFO - __main__ -   *** Evaluate ***
[INFO|trainer.py:811] 2024-09-05 13:02:51,857 >> 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 13:02:51,860 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 13:02:51,860 >>   Num examples = 6798
[INFO|trainer.py:3824] 2024-09-05 13:02:51,860 >>   Batch size = 8

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***** eval metrics *****
  epoch                   =       9.99
  eval_accuracy           =     0.9986
  eval_f1                 =      0.918
  eval_loss               =      0.008
  eval_precision          =     0.9145
  eval_recall             =     0.9216
  eval_runtime            = 0:00:14.11
  eval_samples            =       6798
  eval_samples_per_second =    481.737
  eval_steps_per_second   =     60.235
09/05/2024 13:03:05 - INFO - __main__ -   *** Predict ***
[INFO|trainer.py:811] 2024-09-05 13:03:05,973 >> 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 13:03:05,976 >> 
***** Running Prediction *****
[INFO|trainer.py:3821] 2024-09-05 13:03:05,976 >>   Num examples = 14605
[INFO|trainer.py:3824] 2024-09-05 13:03:05,976 >>   Batch size = 8

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[INFO|trainer.py:3503] 2024-09-05 13:03:34,176 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 13:03:34,178 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 13:03:35,464 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 13:03:35,465 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 13:03:35,466 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
  predict_accuracy           =     0.9982
  predict_f1                 =     0.8968
  predict_loss               =     0.0108
  predict_precision          =     0.8885
  predict_recall             =     0.9054
  predict_runtime            = 0:00:27.54
  predict_samples_per_second =    530.292
  predict_steps_per_second   =       66.3

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events.out.tfevents.1725541385.da2ff10c1388.1139.1: 100%|██████████| 560/560 [00:00<00:00, 7.08kB/s]