2024-08-30 21:23:38.402974: 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`.
2024-08-30 21:23:38.420874: 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
2024-08-30 21:23:38.443027: 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
2024-08-30 21:23:38.449417: 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
2024-08-30 21:23:38.464827: 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.
2024-08-30 21:23:39.777042: 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:1494: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
08/30/2024 21:23:41 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
08/30/2024 21:23:41 - 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,
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,
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] 2024-08-30 21:23:53,548 >> 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] 2024-08-30 21:23:53,551 >> 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": "4.42.4",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 28895
}

[INFO|tokenization_utils_base.py:2161] 2024-08-30 21:23:54,680 >> loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/vocab.txt
[INFO|tokenization_utils_base.py:2161] 2024-08-30 21:23:54,680 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/tokenizer.json
[INFO|tokenization_utils_base.py:2161] 2024-08-30 21:23:54,680 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2161] 2024-08-30 21:23:54,680 >> 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:2161] 2024-08-30 21:23:54,680 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/tokenizer_config.json
[INFO|modeling_utils.py:3556] 2024-08-30 21:24:11,262 >> 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:4354] 2024-08-30 21:24:11,391 >> 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:4366] 2024-08-30 21:24:11,391 >> 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)
[INFO|trainer.py:805] 2024-08-30 21:24:17,270 >> The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags. If tokens, id, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:2128] 2024-08-30 21:24:17,830 >> ***** Running training *****
[INFO|trainer.py:2129] 2024-08-30 21:24:17,831 >>   Num examples = 27,768
[INFO|trainer.py:2130] 2024-08-30 21:24:17,831 >>   Num Epochs = 10
[INFO|trainer.py:2131] 2024-08-30 21:24:17,831 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2134] 2024-08-30 21:24:17,831 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2135] 2024-08-30 21:24:17,831 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2136] 2024-08-30 21:24:17,831 >>   Total optimization steps = 4,340
[INFO|trainer.py:2137] 2024-08-30 21:24:17,831 >>   Number of trainable parameters = 107,644,419

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[INFO|trainer.py:3788] 2024-08-30 21:25:45,374 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 21:25:45,374 >>   Num examples = 6946
[INFO|trainer.py:3793] 2024-08-30 21:25:45,374 >>   Batch size = 8


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                                                 [INFO|trainer.py:3478] 2024-08-30 21:25:58,929 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-434
[INFO|configuration_utils.py:472] 2024-08-30 21:25:58,931 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-434/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:25:59,817 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-434/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:25:59,818 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-434/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:25:59,818 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-434/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:26:04,842 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:26:04,843 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 21:27:31,645 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 21:27:31,645 >>   Num examples = 6946
[INFO|trainer.py:3793] 2024-08-30 21:27:31,645 >>   Batch size = 8
{'eval_loss': 0.005673006176948547, 'eval_precision': 0.8937558247903076, 'eval_recall': 0.8937558247903076, 'eval_f1': 0.8937558247903076, 'eval_accuracy': 0.9980813775011861, 'eval_runtime': 13.5502, 'eval_samples_per_second': 512.611, 'eval_steps_per_second': 64.132, 'epoch': 1.0}
{'loss': 0.0182, 'grad_norm': 0.324382483959198, 'learning_rate': 4.423963133640553e-05, 'epoch': 1.15}


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                                                 [INFO|trainer.py:3478] 2024-08-30 21:27:45,095 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-868
[INFO|configuration_utils.py:472] 2024-08-30 21:27:45,096 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-868/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:27:45,985 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-868/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:27:45,986 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-868/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:27:45,986 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-868/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:27:51,478 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:27:51,478 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 21:29:18,149 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 21:29:18,149 >>   Num examples = 6946
[INFO|trainer.py:3793] 2024-08-30 21:29:18,149 >>   Batch size = 8
{'eval_loss': 0.004419892560690641, 'eval_precision': 0.9023508137432188, 'eval_recall': 0.9301025163094129, 'eval_f1': 0.9160165213400643, 'eval_accuracy': 0.9985418469009014, 'eval_runtime': 13.4449, 'eval_samples_per_second': 516.626, 'eval_steps_per_second': 64.634, 'epoch': 2.0}
{'loss': 0.0039, 'grad_norm': 0.0693276971578598, 'learning_rate': 3.847926267281106e-05, 'epoch': 2.3}


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                                                 [INFO|trainer.py:3478] 2024-08-30 21:29:31,576 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1302
[INFO|configuration_utils.py:472] 2024-08-30 21:29:31,578 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1302/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:29:32,469 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1302/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:29:32,470 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1302/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:29:32,471 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1302/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:29:34,321 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:29:34,321 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 21:31:00,186 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 21:31:00,186 >>   Num examples = 6946
[INFO|trainer.py:3793] 2024-08-30 21:31:00,186 >>   Batch size = 8
{'eval_loss': 0.0044819144532084465, 'eval_precision': 0.9129239230064161, 'eval_recall': 0.9282385834109972, 'eval_f1': 0.9205175600739371, 'eval_accuracy': 0.9986534758462869, 'eval_runtime': 13.422, 'eval_samples_per_second': 517.51, 'eval_steps_per_second': 64.745, 'epoch': 3.0}
{'loss': 0.0024, 'grad_norm': 0.06998981535434723, 'learning_rate': 3.271889400921659e-05, 'epoch': 3.46}


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                                                 [INFO|trainer.py:3478] 2024-08-30 21:31:13,863 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1736
[INFO|configuration_utils.py:472] 2024-08-30 21:31:13,865 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1736/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:31:14,744 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1736/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:31:14,745 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1736/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:31:14,745 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1736/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:31:16,636 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:31:16,637 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 21:32:43,438 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 21:32:43,438 >>   Num examples = 6946
[INFO|trainer.py:3793] 2024-08-30 21:32:43,439 >>   Batch size = 8
{'eval_loss': 0.005129755008965731, 'eval_precision': 0.882145998240985, 'eval_recall': 0.934762348555452, 'eval_f1': 0.9076923076923078, 'eval_accuracy': 0.9983255658192169, 'eval_runtime': 13.6729, 'eval_samples_per_second': 508.013, 'eval_steps_per_second': 63.556, 'epoch': 4.0}
{'loss': 0.0017, 'grad_norm': 0.0027600331231951714, 'learning_rate': 2.6958525345622122e-05, 'epoch': 4.61}


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                                                 [INFO|trainer.py:3478] 2024-08-30 21:32:56,882 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2170
[INFO|configuration_utils.py:472] 2024-08-30 21:32:56,884 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2170/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:32:57,770 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2170/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:32:57,771 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2170/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:32:57,771 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2170/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:32:59,675 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:32:59,675 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 21:34:26,117 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 21:34:26,117 >>   Num examples = 6946
[INFO|trainer.py:3793] 2024-08-30 21:34:26,117 >>   Batch size = 8
{'eval_loss': 0.0056963409297168255, 'eval_precision': 0.9250693802035153, 'eval_recall': 0.9319664492078286, 'eval_f1': 0.9285051067780873, 'eval_accuracy': 0.9986464990372004, 'eval_runtime': 13.4387, 'eval_samples_per_second': 516.864, 'eval_steps_per_second': 64.664, 'epoch': 5.0}
{'loss': 0.0012, 'grad_norm': 0.018086101859807968, 'learning_rate': 2.1198156682027652e-05, 'epoch': 5.76}


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                                                 [INFO|trainer.py:3478] 2024-08-30 21:34:39,501 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2604
[INFO|configuration_utils.py:472] 2024-08-30 21:34:39,502 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2604/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:34:40,386 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2604/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:34:40,387 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2604/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:34:40,387 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2604/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:34:42,312 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:34:42,313 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 21:36:08,607 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 21:36:08,607 >>   Num examples = 6946
[INFO|trainer.py:3793] 2024-08-30 21:36:08,607 >>   Batch size = 8
{'eval_loss': 0.006062328349798918, 'eval_precision': 0.9000908265213442, 'eval_recall': 0.923578751164958, 'eval_f1': 0.9116835326586937, 'eval_accuracy': 0.9984023107191695, 'eval_runtime': 13.3783, 'eval_samples_per_second': 519.199, 'eval_steps_per_second': 64.956, 'epoch': 6.0}
{'loss': 0.0009, 'grad_norm': 0.009607589803636074, 'learning_rate': 1.543778801843318e-05, 'epoch': 6.91}


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                                                 [INFO|trainer.py:3478] 2024-08-30 21:36:22,011 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3038
[INFO|configuration_utils.py:472] 2024-08-30 21:36:22,013 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3038/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:36:22,900 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3038/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:36:22,901 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3038/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:36:22,902 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3038/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:36:24,847 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:36:24,847 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 21:37:51,033 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 21:37:51,033 >>   Num examples = 6946
[INFO|trainer.py:3793] 2024-08-30 21:37:51,033 >>   Batch size = 8
{'eval_loss': 0.005624314770102501, 'eval_precision': 0.9327102803738317, 'eval_recall': 0.9301025163094129, 'eval_f1': 0.9314045730284647, 'eval_accuracy': 0.9986953367008066, 'eval_runtime': 13.399, 'eval_samples_per_second': 518.397, 'eval_steps_per_second': 64.856, 'epoch': 7.0}


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                                                 [INFO|trainer.py:3478] 2024-08-30 21:38:04,410 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3472
[INFO|configuration_utils.py:472] 2024-08-30 21:38:04,411 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3472/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:38:05,289 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3472/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:38:05,290 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3472/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:38:05,290 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3472/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:38:07,168 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:38:07,168 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 21:39:33,764 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 21:39:33,764 >>   Num examples = 6946
[INFO|trainer.py:3793] 2024-08-30 21:39:33,764 >>   Batch size = 8
{'eval_loss': 0.006831143982708454, 'eval_precision': 0.9118181818181819, 'eval_recall': 0.934762348555452, 'eval_f1': 0.9231477220432582, 'eval_accuracy': 0.9985837077554209, 'eval_runtime': 13.3718, 'eval_samples_per_second': 519.45, 'eval_steps_per_second': 64.987, 'epoch': 8.0}
{'loss': 0.0006, 'grad_norm': 0.0005392630700953305, 'learning_rate': 9.67741935483871e-06, 'epoch': 8.06}


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                                                 [INFO|trainer.py:3478] 2024-08-30 21:39:47,295 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3906
[INFO|configuration_utils.py:472] 2024-08-30 21:39:47,296 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3906/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:39:48,182 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3906/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:39:48,183 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3906/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:39:48,183 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3906/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:39:50,129 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:39:50,130 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|configuration_utils.py:472] 2024-08-30 21:41:16,780 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4340/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:41:17,664 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4340/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:41:17,665 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4340/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:41:17,666 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4340/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:41:20,153 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:41:20,153 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:805] 2024-08-30 21:41:20,216 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags. If tokens, id, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3788] 2024-08-30 21:41:20,219 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 21:41:20,219 >>   Num examples = 6946
[INFO|trainer.py:3793] 2024-08-30 21:41:20,219 >>   Batch size = 8
{'eval_loss': 0.0071532572619616985, 'eval_precision': 0.9267161410018553, 'eval_recall': 0.9310344827586207, 'eval_f1': 0.9288702928870293, 'eval_accuracy': 0.9986813830826333, 'eval_runtime': 13.5255, 'eval_samples_per_second': 513.548, 'eval_steps_per_second': 64.249, 'epoch': 9.0}
{'loss': 0.0004, 'grad_norm': 0.0045371875166893005, 'learning_rate': 3.9170506912442395e-06, 'epoch': 9.22}


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                                                 [INFO|trainer.py:3478] 2024-08-30 21:41:34,028 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4340
[INFO|configuration_utils.py:472] 2024-08-30 21:41:34,030 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4340/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:41:35,219 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4340/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:41:35,220 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4340/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:41:35,221 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4340/special_tokens_map.json
[INFO|trainer.py:2383] 2024-08-30 21:41:36,725 >> 

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


[INFO|trainer.py:2621] 2024-08-30 21:41:36,725 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-3038 (score: 0.9314045730284647).

                                                   

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[INFO|trainer.py:4239] 2024-08-30 21:41:36,893 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3478] 2024-08-30 21:41:37,582 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-08-30 21:41:37,583 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:41:38,705 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:41:38,706 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:41:38,706 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3478] 2024-08-30 21:41:38,719 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-08-30 21:41:38,721 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:41:39,977 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:41:39,979 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:41:39,979 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.007294897455722094, 'eval_precision': 0.9191919191919192, 'eval_recall': 0.9328984156570364, 'eval_f1': 0.9259944495837187, 'eval_accuracy': 0.998618591800854, 'eval_runtime': 13.8041, 'eval_samples_per_second': 503.184, 'eval_steps_per_second': 62.952, 'epoch': 10.0}
{'train_runtime': 1039.0596, 'train_samples_per_second': 267.242, 'train_steps_per_second': 4.177, 'train_loss': 0.003382195293697344, 'epoch': 10.0}

events.out.tfevents.1725053057.6b97e535edda.35455.0:   0%|          | 0.00/11.8k [00:00<?, ?B/s]
events.out.tfevents.1725053057.6b97e535edda.35455.0: 100%|██████████| 11.8k/11.8k [00:00<00:00, 59.6kB/s]
***** train metrics *****
  epoch                    =       10.0
  total_flos               = 10241691GF
  train_loss               =     0.0034
  train_runtime            = 0:17:19.05
  train_samples            =      27768
  train_samples_per_second =    267.242
  train_steps_per_second   =      4.177
08/30/2024 21:41:43 - INFO - __main__ -   *** Evaluate ***
[INFO|trainer.py:805] 2024-08-30 21:41:43,553 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags. If tokens, id, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3788] 2024-08-30 21:41:43,555 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 21:41:43,555 >>   Num examples = 6946
[INFO|trainer.py:3793] 2024-08-30 21:41:43,555 >>   Batch size = 8

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***** eval metrics *****
  epoch                   =       10.0
  eval_accuracy           =     0.9987
  eval_f1                 =     0.9314
  eval_loss               =     0.0056
  eval_precision          =     0.9327
  eval_recall             =     0.9301
  eval_runtime            = 0:00:13.39
  eval_samples            =       6946
  eval_samples_per_second =     518.45
  eval_steps_per_second   =     64.862
08/30/2024 21:41:56 - INFO - __main__ -   *** Predict ***
[INFO|trainer.py:805] 2024-08-30 21:41:56,958 >> The following columns in the test set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, id, ner_tags. If tokens, id, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3788] 2024-08-30 21:41:56,961 >> 
***** Running Prediction *****
[INFO|trainer.py:3790] 2024-08-30 21:41:56,961 >>   Num examples = 14715
[INFO|trainer.py:3793] 2024-08-30 21:41:56,961 >>   Batch size = 8

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[INFO|trainer.py:3478] 2024-08-30 21:42:23,773 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-08-30 21:42:23,774 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 21:42:25,063 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 21:42:25,064 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 21:42:25,065 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
  predict_accuracy           =     0.9987
  predict_f1                 =     0.9214
  predict_loss               =     0.0058
  predict_precision          =     0.8892
  predict_recall             =     0.9559
  predict_runtime            = 0:00:26.27
  predict_samples_per_second =    560.104
  predict_steps_per_second   =     70.037

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events.out.tfevents.1725054116.6b97e535edda.35455.1: 100%|██████████| 560/560 [00:00<00:00, 4.68kB/s]