2024-09-05 22:43:35.221687: 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 22:43:35.239699: 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 22:43:35.261393: 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 22:43:35.267947: 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 22:43:35.283519: 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 22:43:36.568969: 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 22:43:38 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/05/2024 22:43:38 - 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] 2024-09-05 22:43:57,301 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
[INFO|configuration_utils.py:800] 2024-09-05 22:43:57,310 >> Model config RobertaConfig {
  "_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
  "architectures": [
    "RobertaForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "bos_token_id": 0,
  "classifier_dropout": null,
  "eos_token_id": 2,
  "finetuning_task": "ner",
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "id2label": {
    "0": "O",
    "1": "B-ENFERMEDAD",
    "2": "I-ENFERMEDAD"
  },
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "label2id": {
    "B-ENFERMEDAD": 1,
    "I-ENFERMEDAD": 2,
    "O": 0
  },
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 514,
  "model_type": "roberta",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 1,
  "position_embedding_type": "absolute",
  "transformers_version": "4.44.2",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|configuration_utils.py:733] 2024-09-05 22:43:57,806 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
[INFO|configuration_utils.py:800] 2024-09-05 22:43:57,808 >> Model config RobertaConfig {
  "_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
  "architectures": [
    "RobertaForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "bos_token_id": 0,
  "classifier_dropout": null,
  "eos_token_id": 2,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 514,
  "model_type": "roberta",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 1,
  "position_embedding_type": "absolute",
  "transformers_version": "4.44.2",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|tokenization_utils_base.py:2269] 2024-09-05 22:44:00,154 >> loading file vocab.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/vocab.json
[INFO|tokenization_utils_base.py:2269] 2024-09-05 22:44:00,154 >> loading file merges.txt from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/merges.txt
[INFO|tokenization_utils_base.py:2269] 2024-09-05 22:44:00,154 >> loading file tokenizer.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-05 22:44:00,154 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-05 22:44:00,154 >> loading file special_tokens_map.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/special_tokens_map.json
[INFO|tokenization_utils_base.py:2269] 2024-09-05 22:44:00,154 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/tokenizer_config.json
[INFO|configuration_utils.py:733] 2024-09-05 22:44:00,155 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
[INFO|configuration_utils.py:800] 2024-09-05 22:44:00,156 >> Model config RobertaConfig {
  "_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
  "architectures": [
    "RobertaForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "bos_token_id": 0,
  "classifier_dropout": null,
  "eos_token_id": 2,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 514,
  "model_type": "roberta",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 1,
  "position_embedding_type": "absolute",
  "transformers_version": "4.44.2",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

/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|configuration_utils.py:733] 2024-09-05 22:44:00,243 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/config.json
[INFO|configuration_utils.py:800] 2024-09-05 22:44:00,244 >> Model config RobertaConfig {
  "_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
  "architectures": [
    "RobertaForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "bos_token_id": 0,
  "classifier_dropout": null,
  "eos_token_id": 2,
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 514,
  "model_type": "roberta",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 1,
  "position_embedding_type": "absolute",
  "transformers_version": "4.44.2",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|modeling_utils.py:3678] 2024-09-05 22:44:31,232 >> loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--PlanTL-GOB-ES--bsc-bio-ehr-es/snapshots/1e543adb2d21f19d85a89305eebdbd64ab656b99/pytorch_model.bin
[INFO|modeling_utils.py:4497] 2024-09-05 22:44:31,384 >> Some weights of the model checkpoint at PlanTL-GOB-ES/bsc-bio-ehr-es were not used when initializing RobertaForTokenClassification: ['lm_head.bias', 'lm_head.decoder.bias', 'lm_head.decoder.weight', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight']
- This IS expected if you are initializing RobertaForTokenClassification 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 RobertaForTokenClassification 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 22:44:31,384 >> Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at PlanTL-GOB-ES/bsc-bio-ehr-es 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)

Downloading builder script:   0%|          | 0.00/2.47k [00:00<?, ?B/s]
Downloading builder script: 6.33kB [00:00, 10.1MB/s]                   
[INFO|trainer.py:811] 2024-09-05 22:44:38,444 >> The following columns in the training set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:2134] 2024-09-05 22:44:39,141 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-05 22:44:39,141 >>   Num examples = 31,947
[INFO|trainer.py:2136] 2024-09-05 22:44:39,141 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-05 22:44:39,141 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-05 22:44:39,141 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-05 22:44:39,141 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-05 22:44:39,141 >>   Total optimization steps = 4,990
[INFO|trainer.py:2143] 2024-09-05 22:44:39,142 >>   Number of trainable parameters = 124,055,043

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 10%|█         | 499/4990 [02:01<18:19,  4.08it/s][INFO|trainer.py:811] 2024-09-05 22:46:40,950 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 22:46:40,953 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 22:46:40,953 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 22:46:40,953 >>   Batch size = 8


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                                                 [INFO|trainer.py:3503] 2024-09-05 22:46:55,159 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-499
[INFO|configuration_utils.py:472] 2024-09-05 22:46:55,160 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-499/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 22:46:56,182 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-499/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 22:46:56,183 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-499/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 22:46:56,183 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-499/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 22:46:58,215 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 22:46:58,215 >> 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 22:48:58,311 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 22:48:58,311 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 22:48:58,311 >>   Batch size = 8
{'eval_loss': 0.07385822385549545, 'eval_precision': 0.7270588235294118, 'eval_recall': 0.7952737482452036, 'eval_f1': 0.75963794837412, 'eval_accuracy': 0.9730869045820918, 'eval_runtime': 14.2023, 'eval_samples_per_second': 479.501, 'eval_steps_per_second': 59.99, 'epoch': 1.0}
{'loss': 0.105, 'grad_norm': 0.8427119851112366, 'learning_rate': 4.4989979959919844e-05, 'epoch': 1.0}


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                                                 [INFO|trainer.py:3503] 2024-09-05 22:49:12,464 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-999
[INFO|configuration_utils.py:472] 2024-09-05 22:49:12,465 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-999/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 22:49:13,495 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-999/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 22:49:13,496 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-999/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 22:49:13,497 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-999/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 22:49:18,673 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 22:49:18,673 >> 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 22:51:18,287 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 22:51:18,287 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 22:51:18,287 >>   Batch size = 8
{'eval_loss': 0.09075114130973816, 'eval_precision': 0.7435501653803749, 'eval_recall': 0.7889564810481984, 'eval_f1': 0.7655806561471223, 'eval_accuracy': 0.972949592870776, 'eval_runtime': 14.1492, 'eval_samples_per_second': 481.299, 'eval_steps_per_second': 60.215, 'epoch': 2.0}
{'loss': 0.0448, 'grad_norm': 0.9121108651161194, 'learning_rate': 3.997995991983968e-05, 'epoch': 2.0}


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                                                 [INFO|trainer.py:3503] 2024-09-05 22:51:32,426 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1498
[INFO|configuration_utils.py:472] 2024-09-05 22:51:32,428 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1498/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 22:51:33,428 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1498/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 22:51:33,429 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1498/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 22:51:33,429 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1498/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 22:51:35,492 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 22:51:35,492 >> 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 22:53:36,382 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 22:53:36,382 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 22:53:36,382 >>   Batch size = 8
{'eval_loss': 0.09297410398721695, 'eval_precision': 0.7675882220723759, 'eval_recall': 0.7990173139915769, 'eval_f1': 0.7829875042989798, 'eval_accuracy': 0.9743913658395924, 'eval_runtime': 14.1376, 'eval_samples_per_second': 481.693, 'eval_steps_per_second': 60.265, 'epoch': 3.0}
{'loss': 0.0255, 'grad_norm': 0.5380845069885254, 'learning_rate': 3.496993987975952e-05, 'epoch': 3.0}


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                                                 [INFO|trainer.py:3503] 2024-09-05 22:53:50,746 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1998
[INFO|configuration_utils.py:472] 2024-09-05 22:53:50,747 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1998/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 22:53:51,738 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1998/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 22:53:51,739 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1998/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 22:53:51,739 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1998/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 22:53:53,822 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 22:53:53,823 >> 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 22:55:54,214 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 22:55:54,214 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 22:55:54,214 >>   Batch size = 8
{'eval_loss': 0.10520397126674652, 'eval_precision': 0.7805994051704416, 'eval_recall': 0.798315395414132, 'eval_f1': 0.789358010410642, 'eval_accuracy': 0.9757438861960537, 'eval_runtime': 14.3621, 'eval_samples_per_second': 474.163, 'eval_steps_per_second': 59.323, 'epoch': 4.0}
{'loss': 0.0164, 'grad_norm': 0.2705754339694977, 'learning_rate': 2.9959919839679363e-05, 'epoch': 4.0}


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                                                 [INFO|trainer.py:3503] 2024-09-05 22:56:08,452 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2497
[INFO|configuration_utils.py:472] 2024-09-05 22:56:08,454 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2497/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 22:56:09,485 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2497/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 22:56:09,486 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2497/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 22:56:09,487 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2497/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 22:56:11,791 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 22:56:11,791 >> 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 22:58:12,244 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 22:58:12,244 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 22:58:12,244 >>   Batch size = 8
{'eval_loss': 0.10997848957777023, 'eval_precision': 0.7756119673617408, 'eval_recall': 0.8006551240056153, 'eval_f1': 0.7879346074142298, 'eval_accuracy': 0.9750435964683428, 'eval_runtime': 14.2341, 'eval_samples_per_second': 478.428, 'eval_steps_per_second': 59.856, 'epoch': 5.0}
{'loss': 0.0112, 'grad_norm': 0.27666428685188293, 'learning_rate': 2.49498997995992e-05, 'epoch': 5.01}


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                                                 [INFO|trainer.py:3503] 2024-09-05 22:58:26,750 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2997
[INFO|configuration_utils.py:472] 2024-09-05 22:58:26,751 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2997/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 22:58:27,796 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2997/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 22:58:27,797 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2997/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 22:58:27,797 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2997/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 22:58:30,090 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 22:58:30,091 >> 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 23:00:30,136 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 23:00:30,136 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 23:00:30,136 >>   Batch size = 8
{'eval_loss': 0.12663568556308746, 'eval_precision': 0.7869446962828649, 'eval_recall': 0.8123537669630323, 'eval_f1': 0.799447386599125, 'eval_accuracy': 0.9768011863731858, 'eval_runtime': 14.5018, 'eval_samples_per_second': 469.597, 'eval_steps_per_second': 58.751, 'epoch': 6.0}
{'loss': 0.0073, 'grad_norm': 0.24552026391029358, 'learning_rate': 1.993987975951904e-05, 'epoch': 6.01}


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                                                 [INFO|trainer.py:3503] 2024-09-05 23:00:44,487 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3496
[INFO|configuration_utils.py:472] 2024-09-05 23:00:44,489 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3496/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:00:45,516 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3496/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:00:45,517 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3496/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:00:45,517 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3496/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:00:47,760 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:00:47,760 >> 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 23:02:48,156 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 23:02:48,156 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 23:02:48,156 >>   Batch size = 8
{'eval_loss': 0.12882493436336517, 'eval_precision': 0.792911744266852, 'eval_recall': 0.8008890968647637, 'eval_f1': 0.7968804562914678, 'eval_accuracy': 0.976293133041317, 'eval_runtime': 14.3476, 'eval_samples_per_second': 474.643, 'eval_steps_per_second': 59.383, 'epoch': 7.0}
{'loss': 0.0054, 'grad_norm': 0.2008085697889328, 'learning_rate': 1.492985971943888e-05, 'epoch': 7.01}


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                                                 [INFO|trainer.py:3503] 2024-09-05 23:03:02,674 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3996
[INFO|configuration_utils.py:472] 2024-09-05 23:03:02,676 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3996/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:03:03,708 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3996/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:03:03,709 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3996/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:03:03,709 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3996/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:03:05,792 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:03:05,792 >> 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 23:05:06,261 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 23:05:06,261 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 23:05:06,261 >>   Batch size = 8
{'eval_loss': 0.14244574308395386, 'eval_precision': 0.803175344384777, 'eval_recall': 0.8048666354702855, 'eval_f1': 0.8040201005025126, 'eval_accuracy': 0.9764853694371592, 'eval_runtime': 14.514, 'eval_samples_per_second': 469.202, 'eval_steps_per_second': 58.702, 'epoch': 8.0}
{'loss': 0.0038, 'grad_norm': 0.12597906589508057, 'learning_rate': 9.919839679358718e-06, 'epoch': 8.01}


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                                                 [INFO|trainer.py:3503] 2024-09-05 23:05:20,504 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4495
[INFO|configuration_utils.py:472] 2024-09-05 23:05:20,506 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4495/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:05:21,543 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4495/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:05:21,544 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4495/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:05:21,544 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4495/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:05:23,710 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:05:23,711 >> 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 23:07:23,202 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4990/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:07:24,224 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4990/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:07:24,225 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4990/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:07:24,226 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4990/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:07:26,424 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:07:26,425 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-05 23:07:26,475 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 23:07:26,478 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 23:07:26,478 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 23:07:26,478 >>   Batch size = 8
{'eval_loss': 0.14552859961986542, 'eval_precision': 0.7901149425287356, 'eval_recall': 0.8041647168928404, 'eval_f1': 0.7970779220779219, 'eval_accuracy': 0.9765059661938567, 'eval_runtime': 14.2396, 'eval_samples_per_second': 478.242, 'eval_steps_per_second': 59.833, 'epoch': 9.0}
{'loss': 0.0028, 'grad_norm': 0.2577208876609802, 'learning_rate': 4.9098196392785576e-06, 'epoch': 9.01}


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                                                 [INFO|trainer.py:3503] 2024-09-05 23:07:40,875 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4990
[INFO|configuration_utils.py:472] 2024-09-05 23:07:40,877 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4990/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:07:42,245 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4990/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:07:42,247 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4990/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:07:42,248 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4990/special_tokens_map.json
[INFO|trainer.py:2394] 2024-09-05 23:07:44,059 >> 

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


[INFO|trainer.py:2632] 2024-09-05 23:07:44,060 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-3996 (score: 0.8040201005025126).

                                                   

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[INFO|trainer.py:4283] 2024-09-05 23:07:44,259 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-05 23:08:00,051 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 23:08:00,053 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:08:01,524 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:08:01,525 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:08:01,525 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3503] 2024-09-05 23:08:01,574 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 23:08:01,576 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:08:02,935 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:08:02,936 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:08:02,937 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.14972682297229767, 'eval_precision': 0.7898351648351648, 'eval_recall': 0.8072063640617688, 'eval_f1': 0.7984262902105993, 'eval_accuracy': 0.9768286487154489, 'eval_runtime': 14.3927, 'eval_samples_per_second': 473.158, 'eval_steps_per_second': 59.197, 'epoch': 9.99}
{'train_runtime': 1385.1143, 'train_samples_per_second': 230.645, 'train_steps_per_second': 3.603, 'train_loss': 0.022475468706272407, 'epoch': 9.99}

events.out.tfevents.1725576279.2a66098fac87.2185.0:   0%|          | 0.00/12.2k [00:00<?, ?B/s]
events.out.tfevents.1725576279.2a66098fac87.2185.0: 100%|██████████| 12.2k/12.2k [00:00<00:00, 40.3kB/s]
***** train metrics *****
  epoch                    =       9.99
  total_flos               = 14036187GF
  train_loss               =     0.0225
  train_runtime            = 0:23:05.11
  train_samples            =      31947
  train_samples_per_second =    230.645
  train_steps_per_second   =      3.603
09/05/2024 23:08:08 - INFO - __main__ -   *** Evaluate ***
[INFO|trainer.py:811] 2024-09-05 23:08:08,256 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 23:08:08,258 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 23:08:08,258 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 23:08:08,259 >>   Batch size = 8

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***** eval metrics *****
  epoch                   =       9.99
  eval_accuracy           =     0.9765
  eval_f1                 =      0.804
  eval_loss               =     0.1424
  eval_precision          =     0.8032
  eval_recall             =     0.8049
  eval_runtime            = 0:00:14.24
  eval_samples            =       6810
  eval_samples_per_second =    478.175
  eval_steps_per_second   =     59.825
09/05/2024 23:08:22 - INFO - __main__ -   *** Predict ***
[INFO|trainer.py:811] 2024-09-05 23:08:22,503 >> The following columns in the test set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: id, ner_tags, tokens. If id, ner_tags, tokens are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-05 23:08:22,505 >> 
***** Running Prediction *****
[INFO|trainer.py:3821] 2024-09-05 23:08:22,505 >>   Num examples = 14614
[INFO|trainer.py:3824] 2024-09-05 23:08:22,505 >>   Batch size = 8

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[INFO|trainer.py:3503] 2024-09-05 23:08:51,993 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-05 23:08:51,994 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:08:53,393 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:08:53,395 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:08:53,395 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
  predict_accuracy           =     0.9761
  predict_f1                 =     0.7985
  predict_loss               =     0.1291
  predict_precision          =     0.7908
  predict_recall             =     0.8063
  predict_runtime            = 0:00:28.87
  predict_samples_per_second =    506.199
  predict_steps_per_second   =     63.284

events.out.tfevents.1725577702.2a66098fac87.2185.1:   0%|          | 0.00/560 [00:00<?, ?B/s]
events.out.tfevents.1725577702.2a66098fac87.2185.1: 100%|██████████| 560/560 [00:00<00:00, 1.73kB/s]