2024-09-09 12:49:35.169739: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-09-09 12:49:35.188435: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-09-09 12:49:35.210470: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-09-09 12:49:35.217107: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-09-09 12:49:35.233109: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-09-09 12:49:36.523400: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
09/09/2024 12:49:38 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/09/2024 12:49:38 - INFO - __main__ -   Training/evaluation parameters TrainingArguments(
_n_gpu=1,
accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None, 'use_configured_state': False},
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
batch_eval_metrics=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_persistent_workers=False,
dataloader_pin_memory=True,
dataloader_prefetch_factor=None,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
dispatch_batches=None,
do_eval=True,
do_predict=True,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_do_concat_batches=True,
eval_on_start=False,
eval_steps=None,
eval_strategy=epoch,
eval_use_gather_object=False,
evaluation_strategy=epoch,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
fsdp=[],
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False},
fsdp_min_num_params=0,
fsdp_transformer_layer_cls_to_wrap=None,
full_determinism=False,
gradient_accumulation_steps=2,
gradient_checkpointing=False,
gradient_checkpointing_kwargs=None,
greater_is_better=True,
group_by_length=False,
half_precision_backend=auto,
hub_always_push=False,
hub_model_id=None,
hub_private_repo=False,
hub_strategy=every_save,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
include_inputs_for_metrics=False,
include_num_input_tokens_seen=False,
include_tokens_per_second=False,
jit_mode_eval=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=5e-05,
length_column_name=length,
load_best_model_at_end=True,
local_rank=0,
log_level=passive,
log_level_replica=warning,
log_on_each_node=True,
logging_dir=/content/dissertation/scripts/ner/output/tb,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=500,
logging_strategy=steps,
lr_scheduler_kwargs={},
lr_scheduler_type=linear,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=f1,
mp_parameters=,
neftune_noise_alpha=None,
no_cuda=False,
num_train_epochs=10.0,
optim=adamw_torch,
optim_args=None,
optim_target_modules=None,
output_dir=/content/dissertation/scripts/ner/output,
overwrite_output_dir=True,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=32,
prediction_loss_only=False,
push_to_hub=True,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
ray_scope=last,
remove_unused_columns=True,
report_to=['tensorboard'],
restore_callback_states_from_checkpoint=False,
resume_from_checkpoint=None,
run_name=/content/dissertation/scripts/ner/output,
save_on_each_node=False,
save_only_model=False,
save_safetensors=True,
save_steps=500,
save_strategy=epoch,
save_total_limit=None,
seed=42,
skip_memory_metrics=True,
split_batches=None,
tf32=None,
torch_compile=False,
torch_compile_backend=None,
torch_compile_mode=None,
torch_empty_cache_steps=None,
torchdynamo=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_cpu=False,
use_ipex=False,
use_legacy_prediction_loop=False,
use_mps_device=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.0,
)

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[INFO|configuration_utils.py:733] 2024-09-09 12:49:56,508 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/config.json
[INFO|configuration_utils.py:800] 2024-09-09 12:49:56,512 >> Model config BertConfig {
  "_name_or_path": "michiyasunaga/BioLinkBERT-base",
  "architectures": [
    "BertModel"
  ],
  "attention_probs_dropout_prob": 0.1,
  "classifier_dropout": null,
  "finetuning_task": "ner",
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "id2label": {
    "0": "O",
    "1": "B-FARMACO",
    "2": "I-FARMACO"
  },
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "label2id": {
    "B-FARMACO": 1,
    "I-FARMACO": 2,
    "O": 0
  },
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "model_type": "bert",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 0,
  "position_embedding_type": "absolute",
  "transformers_version": "4.44.2",
  "type_vocab_size": 2,
  "use_cache": true,
  "vocab_size": 28895
}

[INFO|tokenization_utils_base.py:2269] 2024-09-09 12:49:58,923 >> loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/vocab.txt
[INFO|tokenization_utils_base.py:2269] 2024-09-09 12:49:58,923 >> loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/tokenizer.json
[INFO|tokenization_utils_base.py:2269] 2024-09-09 12:49:58,923 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-09 12:49:58,923 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/special_tokens_map.json
[INFO|tokenization_utils_base.py:2269] 2024-09-09 12:49:58,923 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/tokenizer_config.json
/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
  warnings.warn(
[INFO|modeling_utils.py:3678] 2024-09-09 12:50:02,660 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--michiyasunaga--BioLinkBERT-base/snapshots/b71f5d70f063d1c8f1124070ce86f1ee463ca1fe/pytorch_model.bin
[INFO|modeling_utils.py:4497] 2024-09-09 12:50:02,740 >> Some weights of the model checkpoint at michiyasunaga/BioLinkBERT-base were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']
- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
[WARNING|modeling_utils.py:4509] 2024-09-09 12:50:02,740 >> 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:811] 2024-09-09 12:50:10,148 >> The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:2134] 2024-09-09 12:50:10,704 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-09 12:50:10,704 >>   Num examples = 32,447
[INFO|trainer.py:2136] 2024-09-09 12:50:10,704 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-09 12:50:10,704 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-09 12:50:10,704 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-09 12:50:10,704 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-09 12:50:10,704 >>   Total optimization steps = 5,070
[INFO|trainer.py:2143] 2024-09-09 12:50:10,705 >>   Number of trainable parameters = 107,644,419

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 10%|█         | 507/5070 [03:18<27:23,  2.78it/s][INFO|trainer.py:811] 2024-09-09 12:53:28,785 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 12:53:28,787 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 12:53:28,787 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 12:53:28,787 >>   Batch size = 8
{'loss': 0.0183, 'grad_norm': 0.16175590455532074, 'learning_rate': 4.5069033530571994e-05, 'epoch': 0.99}


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                                                 [INFO|trainer.py:3503] 2024-09-09 12:53:44,013 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-507
[INFO|configuration_utils.py:472] 2024-09-09 12:53:44,015 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-507/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:53:44,902 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-507/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:53:44,903 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-507/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:53:44,903 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-507/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:53:48,529 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:53:48,529 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 12:57:06,399 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 12:57:06,399 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 12:57:06,399 >>   Batch size = 8
{'eval_loss': 0.005470008589327335, 'eval_precision': 0.8974130240856378, 'eval_recall': 0.9375582479030755, 'eval_f1': 0.9170464904284412, 'eval_accuracy': 0.9985418469009014, 'eval_runtime': 15.2218, 'eval_samples_per_second': 456.319, 'eval_steps_per_second': 57.089, 'epoch': 1.0}
{'loss': 0.0043, 'grad_norm': 0.13494741916656494, 'learning_rate': 4.0138067061143986e-05, 'epoch': 1.97}


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                                                 [INFO|trainer.py:3503] 2024-09-09 12:57:21,533 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1014
[INFO|configuration_utils.py:472] 2024-09-09 12:57:21,535 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1014/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 12:57:22,446 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1014/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:57:22,447 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1014/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:57:22,447 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1014/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 12:57:28,828 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 12:57:28,829 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 13:00:47,753 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 13:00:47,754 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 13:00:47,754 >>   Batch size = 8
{'eval_loss': 0.005941031500697136, 'eval_precision': 0.9099181073703366, 'eval_recall': 0.9319664492078286, 'eval_f1': 0.9208103130755064, 'eval_accuracy': 0.9985976613735942, 'eval_runtime': 15.129, 'eval_samples_per_second': 459.117, 'eval_steps_per_second': 57.439, 'epoch': 2.0}
{'loss': 0.0022, 'grad_norm': 0.14869354665279388, 'learning_rate': 3.520710059171598e-05, 'epoch': 2.96}


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                                                 [INFO|trainer.py:3503] 2024-09-09 13:01:02,913 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1521
[INFO|configuration_utils.py:472] 2024-09-09 13:01:02,914 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1521/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 13:01:03,808 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1521/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:01:03,810 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1521/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:01:03,810 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1521/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:01:08,731 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:01:08,731 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 13:04:26,000 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 13:04:26,000 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 13:04:26,000 >>   Batch size = 8
{'eval_loss': 0.005716267507523298, 'eval_precision': 0.9015356820234869, 'eval_recall': 0.9301025163094129, 'eval_f1': 0.9155963302752294, 'eval_accuracy': 0.998513939664555, 'eval_runtime': 15.1542, 'eval_samples_per_second': 458.356, 'eval_steps_per_second': 57.344, 'epoch': 3.0}
{'loss': 0.0018, 'grad_norm': 0.15148845314979553, 'learning_rate': 3.027613412228797e-05, 'epoch': 3.94}


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                                                 [INFO|trainer.py:3503] 2024-09-09 13:04:41,347 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2028
[INFO|configuration_utils.py:472] 2024-09-09 13:04:41,348 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2028/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 13:04:42,187 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2028/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:04:42,188 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2028/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:04:42,189 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2028/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:04:46,852 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:04:46,853 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 13:08:04,109 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 13:08:04,109 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 13:08:04,109 >>   Batch size = 8
{'eval_loss': 0.0072021945379674435, 'eval_precision': 0.9274952919020716, 'eval_recall': 0.9179869524697111, 'eval_f1': 0.9227166276346604, 'eval_accuracy': 0.9985767309463344, 'eval_runtime': 15.3453, 'eval_samples_per_second': 452.646, 'eval_steps_per_second': 56.63, 'epoch': 4.0}
{'loss': 0.0009, 'grad_norm': 0.17791983485221863, 'learning_rate': 2.5345167652859964e-05, 'epoch': 4.93}


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                                                 [INFO|trainer.py:3503] 2024-09-09 13:08:19,308 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2535
[INFO|configuration_utils.py:472] 2024-09-09 13:08:19,310 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2535/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 13:08:20,212 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2535/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:08:20,214 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2535/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:08:20,214 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2535/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:08:26,250 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:08:26,250 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 13:11:44,619 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 13:11:44,620 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 13:11:44,620 >>   Batch size = 8
{'eval_loss': 0.0063728527165949345, 'eval_precision': 0.9077757685352622, 'eval_recall': 0.9356943150046598, 'eval_f1': 0.9215236346948141, 'eval_accuracy': 0.9986604526553735, 'eval_runtime': 15.1945, 'eval_samples_per_second': 457.14, 'eval_steps_per_second': 57.192, 'epoch': 5.0}
{'loss': 0.0007, 'grad_norm': 0.027111981064081192, 'learning_rate': 2.0414201183431952e-05, 'epoch': 5.92}


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                                                 [INFO|trainer.py:3503] 2024-09-09 13:11:59,722 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3042
[INFO|configuration_utils.py:472] 2024-09-09 13:11:59,724 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3042/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 13:12:00,618 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3042/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:12:00,619 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3042/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:12:00,620 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3042/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:12:05,088 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:12:05,089 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 13:15:22,427 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 13:15:22,427 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 13:15:22,427 >>   Batch size = 8
{'eval_loss': 0.006401057820767164, 'eval_precision': 0.9194139194139194, 'eval_recall': 0.9356943150046598, 'eval_f1': 0.9274826789838336, 'eval_accuracy': 0.9987302207462395, 'eval_runtime': 15.0971, 'eval_samples_per_second': 460.088, 'eval_steps_per_second': 57.561, 'epoch': 6.0}
{'loss': 0.0004, 'grad_norm': 0.0044996230863034725, 'learning_rate': 1.5483234714003947e-05, 'epoch': 6.9}


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                                                 [INFO|trainer.py:3503] 2024-09-09 13:15:37,601 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3549
[INFO|configuration_utils.py:472] 2024-09-09 13:15:37,602 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3549/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 13:15:38,484 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3549/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:15:38,485 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3549/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:15:38,485 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3549/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:15:44,419 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:15:44,419 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 13:19:01,057 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 13:19:01,057 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 13:19:01,057 >>   Batch size = 8
{'eval_loss': 0.00720419455319643, 'eval_precision': 0.9289012003693444, 'eval_recall': 0.9375582479030755, 'eval_f1': 0.9332096474953618, 'eval_accuracy': 0.9987651047916725, 'eval_runtime': 15.1686, 'eval_samples_per_second': 457.919, 'eval_steps_per_second': 57.289, 'epoch': 7.0}
{'loss': 0.0004, 'grad_norm': 0.0005134351667948067, 'learning_rate': 1.0552268244575937e-05, 'epoch': 7.89}


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                                                 [INFO|trainer.py:3503] 2024-09-09 13:19:16,187 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4056
[INFO|configuration_utils.py:472] 2024-09-09 13:19:16,188 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4056/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 13:19:17,029 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4056/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:19:17,030 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4056/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:19:17,030 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4056/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:19:23,447 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:19:23,447 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 13:22:40,182 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 13:22:40,182 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 13:22:40,182 >>   Batch size = 8
{'eval_loss': 0.007628325838595629, 'eval_precision': 0.9249771271729186, 'eval_recall': 0.9422180801491147, 'eval_f1': 0.9335180055401663, 'eval_accuracy': 0.998772081600759, 'eval_runtime': 15.1289, 'eval_samples_per_second': 459.122, 'eval_steps_per_second': 57.44, 'epoch': 8.0}
{'loss': 0.0003, 'grad_norm': 0.0011633747490122914, 'learning_rate': 5.621301775147929e-06, 'epoch': 8.88}


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                                                 [INFO|trainer.py:3503] 2024-09-09 13:22:55,522 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4563
[INFO|configuration_utils.py:472] 2024-09-09 13:22:55,524 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4563/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 13:22:56,412 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4563/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:22:56,413 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4563/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:22:56,413 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4563/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 13:23:02,986 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 13:23:02,986 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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