2024-09-05 09:20:26.769117: 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 09:20:26.787235: 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 09:20:26.808505: 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 09:20:26.814951: 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 09:20:26.830934: 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 09:20:28.127288: 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 09:20:29 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/05/2024 09:20:29 - 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 09:20:46,434 >> 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 09:20:46,437 >> 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 09:20:46,675 >> 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 09:20:46,675 >> 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 09:20:46,675 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-05 09:20:46,675 >> 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 09:20:46,675 >> 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 09:20:46,972 >> 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 09:20:47,051 >> 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 09:20:47,051 >> 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] 2024-09-05 09:20:54,057 >> The following columns in the training set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:2134] 2024-09-05 09:20:54,619 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-05 09:20:54,619 >>   Num examples = 31,536
[INFO|trainer.py:2136] 2024-09-05 09:20:54,619 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-05 09:20:54,619 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-05 09:20:54,619 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-05 09:20:54,619 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-05 09:20:54,619 >>   Total optimization steps = 4,930
[INFO|trainer.py:2143] 2024-09-05 09:20:54,620 >>   Number of trainable parameters = 107,644,419

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 10%|█         | 493/4930 [01:46<14:25,  5.13it/s][INFO|trainer.py:811] 2024-09-05 09:22:40,903 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 09:22:40,906 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:22:40,906 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:22:40,906 >>   Batch size = 8


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:22:54,048 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-493
[INFO|configuration_utils.py:472] 2024-09-05 09:22:54,050 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-493/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:22:54,939 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-493/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:22:54,940 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-493/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:22:54,940 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-493/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:22:56,701 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:22:56,701 >> 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 09:24:42,834 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:24:42,834 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:24:42,834 >>   Batch size = 8
{'eval_loss': 0.004987192340195179, 'eval_precision': 0.9288389513108615, 'eval_recall': 0.9245107176141659, 'eval_f1': 0.9266697804764128, 'eval_accuracy': 0.9986744062735468, 'eval_runtime': 13.1382, 'eval_samples_per_second': 528.689, 'eval_steps_per_second': 66.143, 'epoch': 1.0}
{'loss': 0.018, 'grad_norm': 0.12838158011436462, 'learning_rate': 4.4929006085192696e-05, 'epoch': 1.01}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:24:56,032 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-986
[INFO|configuration_utils.py:472] 2024-09-05 09:24:56,033 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-986/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:24:56,916 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-986/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:24:56,917 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-986/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:24:56,917 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-986/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:24:58,668 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:24:58,668 >> 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 09:26:43,662 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:26:43,662 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:26:43,662 >>   Batch size = 8
{'eval_loss': 0.0057116178795695305, 'eval_precision': 0.9104339796860572, 'eval_recall': 0.918918918918919, 'eval_f1': 0.9146567717996289, 'eval_accuracy': 0.998444171573689, 'eval_runtime': 13.1923, 'eval_samples_per_second': 526.518, 'eval_steps_per_second': 65.872, 'epoch': 2.0}
{'loss': 0.0044, 'grad_norm': 0.014988560229539871, 'learning_rate': 3.98580121703854e-05, 'epoch': 2.03}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:26:56,896 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1479
[INFO|configuration_utils.py:472] 2024-09-05 09:26:56,897 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1479/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:26:57,779 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1479/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:26:57,780 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1479/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:26:57,781 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1479/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:26:59,470 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:26:59,471 >> 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 09:28:44,950 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:28:44,950 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:28:44,950 >>   Batch size = 8
{'eval_loss': 0.00787710677832365, 'eval_precision': 0.9361904761904762, 'eval_recall': 0.9161230195712954, 'eval_f1': 0.9260480452190296, 'eval_accuracy': 0.9984930092372952, 'eval_runtime': 13.2289, 'eval_samples_per_second': 525.064, 'eval_steps_per_second': 65.69, 'epoch': 3.0}
{'loss': 0.0023, 'grad_norm': 0.24877521395683289, 'learning_rate': 3.47870182555781e-05, 'epoch': 3.04}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:28:58,417 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1972
[INFO|configuration_utils.py:472] 2024-09-05 09:28:58,418 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1972/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:28:59,307 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1972/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:28:59,308 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1972/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:28:59,308 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1972/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:29:01,072 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:29:01,072 >> 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 09:30:46,123 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:30:46,123 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:30:46,123 >>   Batch size = 8
{'eval_loss': 0.005731322802603245, 'eval_precision': 0.9318394024276377, 'eval_recall': 0.9301025163094129, 'eval_f1': 0.9309701492537313, 'eval_accuracy': 0.9986953367008066, 'eval_runtime': 13.4618, 'eval_samples_per_second': 515.978, 'eval_steps_per_second': 64.553, 'epoch': 4.0}
{'loss': 0.0014, 'grad_norm': 0.003771956777200103, 'learning_rate': 2.9716024340770794e-05, 'epoch': 4.06}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:30:59,324 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2465
[INFO|configuration_utils.py:472] 2024-09-05 09:30:59,326 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2465/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:31:00,210 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2465/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:31:00,211 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2465/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:31:00,211 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2465/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:31:04,413 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:31:04,413 >> 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 09:32:50,222 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:32:50,222 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:32:50,222 >>   Batch size = 8
{'eval_loss': 0.007000353187322617, 'eval_precision': 0.9200743494423792, 'eval_recall': 0.9226467847157502, 'eval_f1': 0.9213587715216379, 'eval_accuracy': 0.9985837077554209, 'eval_runtime': 13.1965, 'eval_samples_per_second': 526.351, 'eval_steps_per_second': 65.851, 'epoch': 5.0}
{'loss': 0.0008, 'grad_norm': 0.006697970442473888, 'learning_rate': 2.4645030425963488e-05, 'epoch': 5.07}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:33:03,358 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2958
[INFO|configuration_utils.py:472] 2024-09-05 09:33:03,360 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2958/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:33:04,242 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2958/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:33:04,243 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2958/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:33:04,243 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2958/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:33:05,968 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:33:05,968 >> 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 09:34:51,409 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:34:51,410 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:34:51,410 >>   Batch size = 8
{'eval_loss': 0.008161710575222969, 'eval_precision': 0.9118457300275482, 'eval_recall': 0.9254426840633737, 'eval_f1': 0.9185938945420906, 'eval_accuracy': 0.9985209164736416, 'eval_runtime': 13.1313, 'eval_samples_per_second': 528.965, 'eval_steps_per_second': 66.178, 'epoch': 6.0}
{'loss': 0.0006, 'grad_norm': 0.0909859761595726, 'learning_rate': 1.957403651115619e-05, 'epoch': 6.09}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:35:04,583 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3451
[INFO|configuration_utils.py:472] 2024-09-05 09:35:04,585 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3451/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:35:05,463 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3451/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:35:05,464 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3451/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:35:05,464 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3451/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:35:07,250 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:35:07,250 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-05 09:36:52,370 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:36:52,370 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:36:52,370 >>   Batch size = 8
{'eval_loss': 0.00739107932895422, 'eval_precision': 0.9171974522292994, 'eval_recall': 0.9394221808014911, 'eval_f1': 0.9281767955801105, 'eval_accuracy': 0.998618591800854, 'eval_runtime': 13.1688, 'eval_samples_per_second': 527.459, 'eval_steps_per_second': 65.989, 'epoch': 7.0}
{'loss': 0.0003, 'grad_norm': 0.011905203573405743, 'learning_rate': 1.4503042596348884e-05, 'epoch': 7.1}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:37:05,590 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3944
[INFO|configuration_utils.py:472] 2024-09-05 09:37:05,592 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3944/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:37:06,466 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3944/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:37:06,467 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3944/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:37:06,467 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3944/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:37:09,606 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:37:09,606 >> 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 09:38:55,110 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:38:55,111 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:38:55,111 >>   Batch size = 8
{'eval_loss': 0.008491172455251217, 'eval_precision': 0.9219330855018587, 'eval_recall': 0.9245107176141659, 'eval_f1': 0.9232201023731968, 'eval_accuracy': 0.9985069628554685, 'eval_runtime': 13.2155, 'eval_samples_per_second': 525.595, 'eval_steps_per_second': 65.756, 'epoch': 8.0}
{'loss': 0.0003, 'grad_norm': 0.00037644465919584036, 'learning_rate': 9.432048681541583e-06, 'epoch': 8.11}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:39:08,663 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4437
[INFO|configuration_utils.py:472] 2024-09-05 09:39:08,664 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4437/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:39:09,552 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4437/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:39:09,553 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4437/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:39:09,553 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4437/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:39:11,406 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:39:11,406 >> 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 09:40:57,462 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4930/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:40:58,336 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4930/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:40:58,337 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4930/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:40:58,337 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4930/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:41:00,244 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:41:00,245 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-05 09:41:00,258 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 09:41:00,261 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:41:00,261 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:41:00,261 >>   Batch size = 8
{'eval_loss': 0.008594635874032974, 'eval_precision': 0.9149130832570905, 'eval_recall': 0.9319664492078286, 'eval_f1': 0.9233610341643582, 'eval_accuracy': 0.9985348700918149, 'eval_runtime': 13.5474, 'eval_samples_per_second': 512.72, 'eval_steps_per_second': 64.145, 'epoch': 9.0}
{'loss': 0.0002, 'grad_norm': 0.053812168538570404, 'learning_rate': 4.36105476673428e-06, 'epoch': 9.13}


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                                                 [INFO|trainer.py:3503] 2024-09-05 09:41:13,940 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4930
[INFO|configuration_utils.py:472] 2024-09-05 09:41:13,942 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4930/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:41:15,211 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4930/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:41:15,214 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4930/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:41:15,214 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4930/special_tokens_map.json
[INFO|trainer.py:2394] 2024-09-05 09:41:16,700 >> 

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


[INFO|trainer.py:2632] 2024-09-05 09:41:16,700 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-1972 (score: 0.9309701492537313).

                                                   

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[INFO|trainer.py:4283] 2024-09-05 09:41:16,869 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-05 09:41:23,447 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 09:41:23,449 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:41:24,716 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:41:24,717 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:41:24,717 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3503] 2024-09-05 09:41:24,730 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 09:41:24,732 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:41:26,453 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:41:26,454 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:41:26,454 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.008874327875673771, 'eval_precision': 0.9172033118675254, 'eval_recall': 0.9291705498602051, 'eval_f1': 0.9231481481481483, 'eval_accuracy': 0.9985418469009014, 'eval_runtime': 13.6739, 'eval_samples_per_second': 507.975, 'eval_steps_per_second': 63.552, 'epoch': 10.0}
{'train_runtime': 1222.2462, 'train_samples_per_second': 258.017, 'train_steps_per_second': 4.034, 'train_loss': 0.0028862289850114567, 'epoch': 10.0}

events.out.tfevents.1725528054.6cb9bed92fd1.10874.0:   0%|          | 0.00/12.1k [00:00<?, ?B/s]
events.out.tfevents.1725528054.6cb9bed92fd1.10874.0: 100%|██████████| 12.1k/12.1k [00:00<00:00, 36.3kB/s]
***** train metrics *****
  epoch                    =       10.0
  total_flos               = 12661346GF
  train_loss               =     0.0029
  train_runtime            = 0:20:22.24
  train_samples            =      31536
  train_samples_per_second =    258.017
  train_steps_per_second   =      4.034
09/05/2024 09:41:31 - INFO - __main__ -   *** Evaluate ***
[INFO|trainer.py:811] 2024-09-05 09:41:31,441 >> The following columns in the evaluation set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 09:41:31,444 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 09:41:31,444 >>   Num examples = 6946
[INFO|trainer.py:3824] 2024-09-05 09:41:31,444 >>   Batch size = 8

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***** eval metrics *****
  epoch                   =       10.0
  eval_accuracy           =     0.9987
  eval_f1                 =      0.931
  eval_loss               =     0.0057
  eval_precision          =     0.9318
  eval_recall             =     0.9301
  eval_runtime            = 0:00:13.26
  eval_samples            =       6946
  eval_samples_per_second =    523.806
  eval_steps_per_second   =     65.532
09/05/2024 09:41:44 - INFO - __main__ -   *** Predict ***
[INFO|trainer.py:811] 2024-09-05 09:41:44,710 >> The following columns in the test set don't have a corresponding argument in `BertForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `BertForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 09:41:44,713 >> 
***** Running Prediction *****
[INFO|trainer.py:3821] 2024-09-05 09:41:44,713 >>   Num examples = 14715
[INFO|trainer.py:3824] 2024-09-05 09:41:44,713 >>   Batch size = 8

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[INFO|trainer.py:3503] 2024-09-05 09:42:11,232 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 09:42:11,233 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 09:42:12,390 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 09:42:12,391 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 09:42:12,391 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
  predict_accuracy           =     0.9987
  predict_f1                 =     0.9273
  predict_loss               =     0.0059
  predict_precision          =     0.9072
  predict_recall             =     0.9483
  predict_runtime            = 0:00:25.98
  predict_samples_per_second =    566.254
  predict_steps_per_second   =     70.806

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