2024-09-09 14:14:39.290280: 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 14:14:39.308004: 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 14:14:39.329151: 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 14:14:39.335520: 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 14:14:39.350729: 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 14:14:40.598840: 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 14:14:42 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/09/2024 14:14:42 - 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 14:15:00,004 >> 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 14:15:00,008 >> 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 14:15:00,242 >> 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 14:15:00,242 >> 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 14:15:00,242 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-09 14:15:00,242 >> 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 14:15:00,242 >> 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 14:15:00,548 >> 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 14:15:00,628 >> 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 14:15:00,628 >> 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 14:15:07,256 >> 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 14:15:07,820 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-09 14:15:07,820 >>   Num examples = 28,668
[INFO|trainer.py:2136] 2024-09-09 14:15:07,820 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-09 14:15:07,820 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-09 14:15:07,820 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-09 14:15:07,820 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-09 14:15:07,820 >>   Total optimization steps = 4,480
[INFO|trainer.py:2143] 2024-09-09 14:15:07,820 >>   Number of trainable parameters = 107,644,419

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[INFO|trainer.py:3819] 2024-09-09 14:17:54,863 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 14:17:54,863 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 14:17:54,863 >>   Batch size = 8


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                                                 [INFO|trainer.py:3503] 2024-09-09 14:18:09,952 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-448
[INFO|configuration_utils.py:472] 2024-09-09 14:18:09,953 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-448/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:18:10,847 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-448/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:18:10,848 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-448/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:18:10,848 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-448/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:18:13,510 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:18:13,510 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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 20%|██        | 896/4480 [05:53<22:27,  2.66it/s][INFO|trainer.py:811] 2024-09-09 14:21:01,228 >> 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 14:21:01,230 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 14:21:01,230 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 14:21:01,230 >>   Batch size = 8
{'eval_loss': 0.005405870731920004, 'eval_precision': 0.9404296875, 'eval_recall': 0.8974836905871388, 'eval_f1': 0.9184549356223176, 'eval_accuracy': 0.9985976613735942, 'eval_runtime': 15.084, 'eval_samples_per_second': 460.487, 'eval_steps_per_second': 57.611, 'epoch': 1.0}
{'loss': 0.016, 'grad_norm': 0.0893813744187355, 'learning_rate': 4.4419642857142854e-05, 'epoch': 1.12}


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                                                 [INFO|trainer.py:3503] 2024-09-09 14:21:16,449 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-896
[INFO|configuration_utils.py:472] 2024-09-09 14:21:16,450 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-896/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:21:17,344 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-896/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:21:17,345 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-896/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:21:17,345 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-896/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:21:19,697 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:21:19,698 >> 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 14:24:08,319 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 14:24:08,320 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 14:24:08,320 >>   Batch size = 8
{'eval_loss': 0.004539168905466795, 'eval_precision': 0.9162011173184358, 'eval_recall': 0.9170549860205033, 'eval_f1': 0.9166278528178854, 'eval_accuracy': 0.9985627773281612, 'eval_runtime': 15.2132, 'eval_samples_per_second': 456.577, 'eval_steps_per_second': 57.121, 'epoch': 2.0}
{'loss': 0.0039, 'grad_norm': 0.01820996217429638, 'learning_rate': 3.883928571428572e-05, 'epoch': 2.23}


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                                                 [INFO|trainer.py:3503] 2024-09-09 14:24:23,396 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1344
[INFO|configuration_utils.py:472] 2024-09-09 14:24:23,398 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1344/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:24:24,291 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1344/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:24:24,292 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1344/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:24:24,292 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1344/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:24:30,854 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:24:30,854 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 14:27:18,913 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 14:27:18,914 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 14:27:18,914 >>   Batch size = 8
{'eval_loss': 0.005798548460006714, 'eval_precision': 0.9063906390639064, 'eval_recall': 0.9384902143522833, 'eval_f1': 0.9221611721611722, 'eval_accuracy': 0.998548823709988, 'eval_runtime': 15.0713, 'eval_samples_per_second': 460.875, 'eval_steps_per_second': 57.659, 'epoch': 3.0}
{'loss': 0.0022, 'grad_norm': 0.09450385719537735, 'learning_rate': 3.325892857142857e-05, 'epoch': 3.35}


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                                                 [INFO|trainer.py:3503] 2024-09-09 14:27:34,289 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1792
[INFO|configuration_utils.py:472] 2024-09-09 14:27:34,291 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1792/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:27:35,189 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1792/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:27:35,190 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1792/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:27:35,191 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1792/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:27:41,118 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:27:41,118 >> 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 14:30:28,554 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 14:30:28,554 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 14:30:28,554 >>   Batch size = 8
{'eval_loss': 0.005682563409209251, 'eval_precision': 0.8963360142984808, 'eval_recall': 0.934762348555452, 'eval_f1': 0.9151459854014599, 'eval_accuracy': 0.998513939664555, 'eval_runtime': 15.3704, 'eval_samples_per_second': 451.907, 'eval_steps_per_second': 56.537, 'epoch': 4.0}
{'loss': 0.0017, 'grad_norm': 0.08753237873315811, 'learning_rate': 2.767857142857143e-05, 'epoch': 4.46}


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                                                 [INFO|trainer.py:3503] 2024-09-09 14:30:43,702 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2240
[INFO|configuration_utils.py:472] 2024-09-09 14:30:43,704 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2240/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:30:44,608 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2240/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:30:44,609 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2240/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:30:44,609 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2240/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:30:47,317 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:30:47,318 >> 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 14:33:35,175 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 14:33:35,175 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 14:33:35,175 >>   Batch size = 8
{'eval_loss': 0.005979395937174559, 'eval_precision': 0.9178082191780822, 'eval_recall': 0.9366262814538676, 'eval_f1': 0.9271217712177122, 'eval_accuracy': 0.9987023135098931, 'eval_runtime': 15.1424, 'eval_samples_per_second': 458.713, 'eval_steps_per_second': 57.389, 'epoch': 5.0}
{'loss': 0.0012, 'grad_norm': 0.010990791022777557, 'learning_rate': 2.2098214285714286e-05, 'epoch': 5.58}


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                                                 [INFO|trainer.py:3503] 2024-09-09 14:33:50,297 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2688
[INFO|configuration_utils.py:472] 2024-09-09 14:33:50,299 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2688/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:33:51,189 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2688/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:33:51,190 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2688/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:33:51,191 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2688/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:33:58,423 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:33:58,424 >> 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 14:36:46,365 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 14:36:46,365 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 14:36:46,365 >>   Batch size = 8
{'eval_loss': 0.006339639890938997, 'eval_precision': 0.9254426840633737, 'eval_recall': 0.9254426840633737, 'eval_f1': 0.9254426840633737, 'eval_accuracy': 0.9987092903189797, 'eval_runtime': 15.1172, 'eval_samples_per_second': 459.478, 'eval_steps_per_second': 57.484, 'epoch': 6.0}
{'loss': 0.0008, 'grad_norm': 0.0010647091548889875, 'learning_rate': 1.6517857142857144e-05, 'epoch': 6.7}


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                                                 [INFO|trainer.py:3503] 2024-09-09 14:37:01,486 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3136
[INFO|configuration_utils.py:472] 2024-09-09 14:37:01,488 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3136/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:37:02,382 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3136/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:37:02,383 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3136/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:37:02,383 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3136/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:37:08,275 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:37:08,275 >> 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 14:39:55,345 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 14:39:55,346 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 14:39:55,346 >>   Batch size = 8
{'eval_loss': 0.0069307987578213215, 'eval_precision': 0.9130434782608695, 'eval_recall': 0.9394221808014911, 'eval_f1': 0.9260450160771704, 'eval_accuracy': 0.9986116149917673, 'eval_runtime': 15.1162, 'eval_samples_per_second': 459.508, 'eval_steps_per_second': 57.488, 'epoch': 7.0}
{'loss': 0.0005, 'grad_norm': 0.0007633898057974875, 'learning_rate': 1.09375e-05, 'epoch': 7.81}


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                                                 [INFO|trainer.py:3503] 2024-09-09 14:40:10,512 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3584
[INFO|configuration_utils.py:472] 2024-09-09 14:40:10,514 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3584/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:40:11,418 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3584/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:40:11,419 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3584/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:40:11,419 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3584/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:40:18,278 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:40:18,278 >> 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 14:43:06,472 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 14:43:06,472 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 14:43:06,472 >>   Batch size = 8
{'eval_loss': 0.006891186349093914, 'eval_precision': 0.9214417744916821, 'eval_recall': 0.9291705498602051, 'eval_f1': 0.9252900232018563, 'eval_accuracy': 0.9986395222281137, 'eval_runtime': 15.1611, 'eval_samples_per_second': 458.146, 'eval_steps_per_second': 57.318, 'epoch': 8.0}
{'loss': 0.0004, 'grad_norm': 0.0015532037941738963, 'learning_rate': 5.357142857142857e-06, 'epoch': 8.93}


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                                                 [INFO|trainer.py:3503] 2024-09-09 14:43:21,821 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4032
[INFO|configuration_utils.py:472] 2024-09-09 14:43:21,822 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4032/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:43:22,720 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4032/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:43:22,721 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4032/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:43:22,721 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4032/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:43:25,122 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:43:25,123 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|configuration_utils.py:472] 2024-09-09 14:46:11,920 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4480/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:46:12,837 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4480/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:46:12,838 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4480/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:46:12,838 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4480/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:46:17,810 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:46:17,810 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-09 14:46:17,823 >> 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 14:46:17,826 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 14:46:17,827 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 14:46:17,827 >>   Batch size = 8
{'eval_loss': 0.0076610674150288105, 'eval_precision': 0.924860853432282, 'eval_recall': 0.9291705498602051, 'eval_f1': 0.9270106927010694, 'eval_accuracy': 0.9986534758462869, 'eval_runtime': 15.344, 'eval_samples_per_second': 452.684, 'eval_steps_per_second': 56.634, 'epoch': 9.0}


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                                                 [INFO|trainer.py:3503] 2024-09-09 14:46:33,014 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4480
[INFO|configuration_utils.py:472] 2024-09-09 14:46:33,016 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4480/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:46:34,189 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4480/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:46:34,190 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4480/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:46:34,191 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4480/special_tokens_map.json
[INFO|trainer.py:2394] 2024-09-09 14:46:37,346 >> 

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


[INFO|trainer.py:2632] 2024-09-09 14:46:37,346 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-4480 (score: 0.9280074314909428).

                                                   

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[INFO|trainer.py:4283] 2024-09-09 14:46:37,506 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-09 14:47:05,337 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 14:47:05,338 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:47:06,676 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:47:06,677 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:47:06,677 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3503] 2024-09-09 14:47:06,690 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 14:47:06,691 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:47:07,761 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:47:07,762 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:47:07,763 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.007708101533353329, 'eval_precision': 0.925, 'eval_recall': 0.9310344827586207, 'eval_f1': 0.9280074314909428, 'eval_accuracy': 0.9986883598917199, 'eval_runtime': 15.1825, 'eval_samples_per_second': 457.501, 'eval_steps_per_second': 57.237, 'epoch': 10.0}
{'train_runtime': 1889.6824, 'train_samples_per_second': 151.708, 'train_steps_per_second': 2.371, 'train_loss': 0.0030171065125614406, 'epoch': 10.0}

events.out.tfevents.1725891307.0a1c9bec2a53.45801.0:   0%|          | 0.00/11.9k [00:00<?, ?B/s]
events.out.tfevents.1725891307.0a1c9bec2a53.45801.0: 100%|██████████| 11.9k/11.9k [00:00<00:00, 38.2kB/s]
***** train metrics *****
  epoch                    =       10.0
  total_flos               = 10972155GF
  train_loss               =      0.003
  train_runtime            = 0:31:29.68
  train_samples            =      28668
  train_samples_per_second =    151.708
  train_steps_per_second   =      2.371
09/09/2024 14:47:12 - INFO - __main__ -   *** Evaluate ***
[INFO|trainer.py:811] 2024-09-09 14:47:12,879 >> 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 14:47:12,881 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 14:47:12,881 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-09 14:47:12,881 >>   Batch size = 8

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***** eval metrics *****
  epoch                   =       10.0
  eval_accuracy           =     0.9987
  eval_f1                 =      0.928
  eval_loss               =     0.0077
  eval_precision          =      0.925
  eval_recall             =      0.931
  eval_runtime            = 0:00:15.10
  eval_samples            =       6946
  eval_samples_per_second =    459.961
  eval_steps_per_second   =     57.545
09/09/2024 14:47:27 - INFO - __main__ -   *** Predict ***
[INFO|trainer.py:811] 2024-09-09 14:47:27,989 >> The following columns in the test set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: id, tokens, ner_tags. If id, tokens, ner_tags are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 14:47:27,991 >> 
***** Running Prediction *****
[INFO|trainer.py:3821] 2024-09-09 14:47:27,992 >>   Num examples = 14715
[INFO|trainer.py:3824] 2024-09-09 14:47:27,992 >>   Batch size = 8

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[INFO|trainer.py:3503] 2024-09-09 14:47:56,911 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 14:47:56,913 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 14:47:58,226 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 14:47:58,227 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 14:47:58,227 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
  predict_accuracy           =     0.9987
  predict_f1                 =     0.9188
  predict_loss               =      0.008
  predict_precision          =     0.8931
  predict_recall             =      0.946
  predict_runtime            = 0:00:28.38
  predict_samples_per_second =    518.402
  predict_steps_per_second   =     64.822

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