2024-09-05 08:55:19.425038: 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 08:55:19.443069: 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 08:55:19.464387: 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 08:55:19.470819: 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 08:55:19.486241: 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 08:55:20.761878: 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 08:55:23 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/05/2024 08:55:23 - 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 08:55:41,517 >> 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 08:55:41,525 >> 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 08:55:43,948 >> 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 08:55:43,948 >> 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 08:55:43,948 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-05 08:55:43,948 >> 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 08:55:43,948 >> 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 08:55:47,400 >> 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 08:55:47,565 >> 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 08:55:47,565 >> 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 08:55:54,837 >> 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:2134] 2024-09-05 08:55:55,545 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-05 08:55:55,545 >>   Num examples = 32,232
[INFO|trainer.py:2136] 2024-09-05 08:55:55,545 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-05 08:55:55,545 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-05 08:55:55,545 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-05 08:55:55,545 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-05 08:55:55,545 >>   Total optimization steps = 5,040
[INFO|trainer.py:2143] 2024-09-05 08:55:55,546 >>   Number of trainable parameters = 107,644,419

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 10%|█         | 504/5040 [01:49<14:44,  5.13it/s][INFO|trainer.py:811] 2024-09-05 08:57:45,274 >> 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:3819] 2024-09-05 08:57:45,276 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 08:57:45,277 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 08:57:45,277 >>   Batch size = 8
{'loss': 0.0189, 'grad_norm': 0.06281786412000656, 'learning_rate': 4.503968253968254e-05, 'epoch': 0.99}


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                                                 [INFO|trainer.py:3503] 2024-09-05 08:57:58,494 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-504
[INFO|configuration_utils.py:472] 2024-09-05 08:57:58,495 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-504/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 08:57:59,388 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-504/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 08:57:59,389 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-504/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 08:57:59,389 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-504/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 08:58:01,373 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 08:58:01,373 >> 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 08:59:49,614 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 08:59:49,614 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 08:59:49,614 >>   Batch size = 8
{'eval_loss': 0.00518822530284524, 'eval_precision': 0.8712186689714779, 'eval_recall': 0.9394221808014911, 'eval_f1': 0.9040358744394619, 'eval_accuracy': 0.9983953339100828, 'eval_runtime': 13.2131, 'eval_samples_per_second': 525.689, 'eval_steps_per_second': 65.768, 'epoch': 1.0}
{'loss': 0.0047, 'grad_norm': 0.20689290761947632, 'learning_rate': 4.007936507936508e-05, 'epoch': 1.98}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:00:02,862 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1008
[INFO|configuration_utils.py:472] 2024-09-05 09:00:02,864 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1008/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:00:03,752 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1008/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:00:03,753 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1008/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:00:03,754 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1008/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:00:05,680 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:00:05,681 >> 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 09:01:54,061 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:01:54,061 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:01:54,061 >>   Batch size = 8
{'eval_loss': 0.004834321793168783, 'eval_precision': 0.9253034547152195, 'eval_recall': 0.923578751164958, 'eval_f1': 0.9244402985074627, 'eval_accuracy': 0.9986883598917199, 'eval_runtime': 13.2434, 'eval_samples_per_second': 524.487, 'eval_steps_per_second': 65.618, 'epoch': 2.0}
{'loss': 0.0027, 'grad_norm': 0.02256501279771328, 'learning_rate': 3.511904761904762e-05, 'epoch': 2.98}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:02:07,237 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1512
[INFO|configuration_utils.py:472] 2024-09-05 09:02:07,239 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1512/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:02:08,165 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1512/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:02:08,166 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1512/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:02:08,167 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1512/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:02:10,114 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:02:10,115 >> 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 09:03:58,824 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:03:58,824 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:03:58,825 >>   Batch size = 8
{'eval_loss': 0.005881821736693382, 'eval_precision': 0.9252336448598131, 'eval_recall': 0.9226467847157502, 'eval_f1': 0.9239384041063929, 'eval_accuracy': 0.9986255686099406, 'eval_runtime': 13.1715, 'eval_samples_per_second': 527.351, 'eval_steps_per_second': 65.976, 'epoch': 3.0}
{'loss': 0.0015, 'grad_norm': 0.0326182059943676, 'learning_rate': 3.0158730158730158e-05, 'epoch': 3.97}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:04:12,423 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2016
[INFO|configuration_utils.py:472] 2024-09-05 09:04:12,425 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2016/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:04:13,316 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2016/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:04:13,317 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2016/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:04:13,317 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2016/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:04:15,182 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:04:15,183 >> 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 09:06:03,437 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:06:03,437 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:06:03,437 >>   Batch size = 8
{'eval_loss': 0.0064844791777431965, 'eval_precision': 0.9342105263157895, 'eval_recall': 0.9263746505125815, 'eval_f1': 0.930276087973795, 'eval_accuracy': 0.9987162671280663, 'eval_runtime': 13.5939, 'eval_samples_per_second': 510.963, 'eval_steps_per_second': 63.926, 'epoch': 4.0}
{'loss': 0.0011, 'grad_norm': 0.1600140929222107, 'learning_rate': 2.5198412698412697e-05, 'epoch': 4.96}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:06:16,786 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2520
[INFO|configuration_utils.py:472] 2024-09-05 09:06:16,787 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2520/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:06:17,668 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2520/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:06:17,670 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2520/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:06:17,670 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2520/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:06:19,420 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:06:19,420 >> 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 09:08:07,754 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:08:07,754 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:08:07,754 >>   Batch size = 8
{'eval_loss': 0.007346163038164377, 'eval_precision': 0.9072907290729073, 'eval_recall': 0.9394221808014911, 'eval_f1': 0.923076923076923, 'eval_accuracy': 0.9986116149917673, 'eval_runtime': 13.3438, 'eval_samples_per_second': 520.543, 'eval_steps_per_second': 65.124, 'epoch': 5.0}
{'loss': 0.0005, 'grad_norm': 0.09641193598508835, 'learning_rate': 2.023809523809524e-05, 'epoch': 5.95}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:08:21,079 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3024
[INFO|configuration_utils.py:472] 2024-09-05 09:08:21,080 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3024/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:08:21,975 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3024/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:08:21,976 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3024/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:08:21,976 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3024/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:08:23,771 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:08:23,771 >> 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 09:10:12,488 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:10:12,489 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:10:12,489 >>   Batch size = 8
{'eval_loss': 0.009004838764667511, 'eval_precision': 0.9191449814126395, 'eval_recall': 0.9217148182665424, 'eval_f1': 0.9204281060958585, 'eval_accuracy': 0.998444171573689, 'eval_runtime': 13.3195, 'eval_samples_per_second': 521.491, 'eval_steps_per_second': 65.243, 'epoch': 6.0}
{'loss': 0.0007, 'grad_norm': 0.029784763231873512, 'learning_rate': 1.527777777777778e-05, 'epoch': 6.94}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:10:25,942 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3528
[INFO|configuration_utils.py:472] 2024-09-05 09:10:25,943 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3528/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:10:26,827 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3528/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:10:26,828 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3528/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:10:26,828 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3528/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:10:28,723 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:10:28,724 >> 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 09:12:17,418 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:12:17,418 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:12:17,418 >>   Batch size = 8
{'eval_loss': 0.008385799825191498, 'eval_precision': 0.9073569482288828, 'eval_recall': 0.9310344827586207, 'eval_f1': 0.9190432382704691, 'eval_accuracy': 0.9985767309463344, 'eval_runtime': 13.4485, 'eval_samples_per_second': 516.487, 'eval_steps_per_second': 64.617, 'epoch': 7.0}
{'loss': 0.0004, 'grad_norm': 0.0010318646673113108, 'learning_rate': 1.0317460317460318e-05, 'epoch': 7.94}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:12:30,781 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4032
[INFO|configuration_utils.py:472] 2024-09-05 09:12:30,782 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4032/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:12:31,628 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4032/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:12:31,629 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4032/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:12:31,630 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4032/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:12:33,528 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:12:33,528 >> 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 09:14:21,692 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:14:21,692 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:14:21,692 >>   Batch size = 8
{'eval_loss': 0.008501913398504257, 'eval_precision': 0.9092558983666061, 'eval_recall': 0.9338303821062441, 'eval_f1': 0.9213793103448276, 'eval_accuracy': 0.9985558005190746, 'eval_runtime': 13.3613, 'eval_samples_per_second': 519.86, 'eval_steps_per_second': 65.039, 'epoch': 8.0}
{'loss': 0.0003, 'grad_norm': 0.0005677491426467896, 'learning_rate': 5.357142857142857e-06, 'epoch': 8.93}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:14:35,181 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4536
[INFO|configuration_utils.py:472] 2024-09-05 09:14:35,182 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4536/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:14:36,016 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4536/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:14:36,017 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4536/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:14:36,017 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4536/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:14:37,973 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:14:37,974 >> 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 09:16:25,807 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-5040/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:16:26,642 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-5040/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:16:26,643 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-5040/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:16:26,644 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-5040/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:16:28,582 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:16:28,582 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-05 09:16:28,596 >> 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:3819] 2024-09-05 09:16:28,599 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:16:28,599 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:16:28,599 >>   Batch size = 8
{'eval_loss': 0.008021777495741844, 'eval_precision': 0.918572735590119, 'eval_recall': 0.9356943150046598, 'eval_f1': 0.9270544783010157, 'eval_accuracy': 0.9987023135098931, 'eval_runtime': 13.487, 'eval_samples_per_second': 515.014, 'eval_steps_per_second': 64.432, 'epoch': 9.0}
{'loss': 0.0002, 'grad_norm': 0.0005077613168396056, 'learning_rate': 3.9682539682539683e-07, 'epoch': 9.92}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:16:42,330 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-5040
[INFO|configuration_utils.py:472] 2024-09-05 09:16:42,332 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-5040/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:16:43,608 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-5040/model.safetensors
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[INFO|trainer.py:2394] 2024-09-05 09:16:45,046 >> 

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


[INFO|trainer.py:2632] 2024-09-05 09:16:45,046 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-2016 (score: 0.930276087973795).

                                                   

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[INFO|trainer.py:4283] 2024-09-05 09:16:45,216 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-05 09:16:57,035 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
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[INFO|configuration_utils.py:472] 2024-09-05 09:16:58,315 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:16:59,583 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:16:59,584 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:16:59,585 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.008253143168985844, 'eval_precision': 0.921028466483012, 'eval_recall': 0.934762348555452, 'eval_f1': 0.9278445883441258, 'eval_accuracy': 0.9986883598917199, 'eval_runtime': 13.7257, 'eval_samples_per_second': 506.057, 'eval_steps_per_second': 63.312, 'epoch': 10.0}
{'train_runtime': 1249.6681, 'train_samples_per_second': 257.924, 'train_steps_per_second': 4.033, 'train_loss': 0.0030765269683407886, 'epoch': 10.0}

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