2024-09-05 23:36:52.633751: 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 23:36:52.651586: 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 23:36:52.673036: 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 23:36:52.679387: 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 23:36:52.694719: 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 23:36:53.943993: 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 23:36:55 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/05/2024 23:36:55 - 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 23:37:15,088 >> 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 23:37:15,092 >> 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",
    "3": "B-PROCEDIMIENTO",
    "4": "I-PROCEDIMIENTO",
    "5": "B-SINTOMA",
    "6": "I-SINTOMA",
    "7": "B-FARMACO",
    "8": "I-FARMACO"
  },
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "label2id": {
    "B-ENFERMEDAD": 1,
    "B-FARMACO": 7,
    "B-PROCEDIMIENTO": 3,
    "B-SINTOMA": 5,
    "I-ENFERMEDAD": 2,
    "I-FARMACO": 8,
    "I-PROCEDIMIENTO": 4,
    "I-SINTOMA": 6,
    "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 23:37:15,329 >> 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 23:37:15,330 >> 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 23:37:15,339 >> 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 23:37:15,340 >> 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 23:37:15,340 >> loading file tokenizer.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-05 23:37:15,340 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-05 23:37:15,340 >> 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 23:37:15,340 >> 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 23:37:15,340 >> 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 23:37:15,341 >> 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 23:37:15,422 >> 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 23:37:15,424 >> 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 23:37:15,757 >> 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 23:37:15,835 >> 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 23:37:15,835 >> 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)
[INFO|trainer.py:811] 2024-09-05 23:37:22,969 >> 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 23:37:23,532 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-05 23:37:23,532 >>   Num examples = 34,604
[INFO|trainer.py:2136] 2024-09-05 23:37:23,532 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-05 23:37:23,532 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-05 23:37:23,532 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-05 23:37:23,532 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-05 23:37:23,532 >>   Total optimization steps = 5,410
[INFO|trainer.py:2143] 2024-09-05 23:37:23,533 >>   Number of trainable parameters = 124,059,657

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[INFO|trainer.py:3819] 2024-09-05 23:39:40,063 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 23:39:40,063 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 23:39:40,063 >>   Batch size = 8
{'loss': 0.3191, 'grad_norm': 1.4528056383132935, 'learning_rate': 4.537892791127542e-05, 'epoch': 0.92}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                  

                                                 

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                                                 [INFO|trainer.py:3503] 2024-09-05 23:39:54,460 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-541
[INFO|configuration_utils.py:472] 2024-09-05 23:39:54,462 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-541/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:39:55,494 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-541/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:39:55,495 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-541/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:39:55,496 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-541/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:40:02,352 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:40:02,352 >> 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 23:42:18,732 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 23:42:18,732 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 23:42:18,732 >>   Batch size = 8
{'eval_loss': 0.47723615169525146, 'eval_precision': 0.27250473783954515, 'eval_recall': 0.8074403369209172, 'eval_f1': 0.407486125870823, 'eval_accuracy': 0.8443022505389485, 'eval_runtime': 14.3908, 'eval_samples_per_second': 473.218, 'eval_steps_per_second': 59.204, 'epoch': 1.0}
{'loss': 0.1619, 'grad_norm': 1.5587466955184937, 'learning_rate': 4.075785582255083e-05, 'epoch': 1.85}


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100%|█████████▉| 848/852 [00:10<00:00, 80.09it/s]/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3503] 2024-09-05 23:42:32,951 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1082
[INFO|configuration_utils.py:472] 2024-09-05 23:42:32,952 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1082/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:42:33,984 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1082/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:42:33,984 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1082/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:42:33,985 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1082/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:42:36,139 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:42:36,140 >> 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 23:44:51,372 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 23:44:51,372 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 23:44:51,372 >>   Batch size = 8
{'eval_loss': 0.4583655595779419, 'eval_precision': 0.30406737143881024, 'eval_recall': 0.7941038839494619, 'eval_f1': 0.43975123088883133, 'eval_accuracy': 0.8552871874442172, 'eval_runtime': 14.212, 'eval_samples_per_second': 479.173, 'eval_steps_per_second': 59.949, 'epoch': 2.0}
{'loss': 0.11, 'grad_norm': 0.9616082310676575, 'learning_rate': 3.613678373382625e-05, 'epoch': 2.77}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3503] 2024-09-05 23:45:05,702 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1623
[INFO|configuration_utils.py:472] 2024-09-05 23:45:05,703 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1623/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:45:06,746 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1623/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:45:06,747 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1623/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:45:06,748 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1623/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:45:08,976 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:45:08,977 >> 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 23:47:24,898 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 23:47:24,898 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 23:47:24,898 >>   Batch size = 8
{'eval_loss': 0.6447410583496094, 'eval_precision': 0.29758899817216466, 'eval_recall': 0.7999532054281703, 'eval_f1': 0.4338006724608259, 'eval_accuracy': 0.8435470361267112, 'eval_runtime': 14.3233, 'eval_samples_per_second': 475.451, 'eval_steps_per_second': 59.484, 'epoch': 3.0}
{'loss': 0.0764, 'grad_norm': 1.4018645286560059, 'learning_rate': 3.1515711645101665e-05, 'epoch': 3.7}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3503] 2024-09-05 23:47:39,445 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2164
[INFO|configuration_utils.py:472] 2024-09-05 23:47:39,446 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2164/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:47:40,496 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2164/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:47:40,497 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2164/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:47:40,498 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2164/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:47:45,951 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:47:45,952 >> 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 23:50:01,976 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 23:50:01,977 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 23:50:01,977 >>   Batch size = 8
{'eval_loss': 0.7413247227668762, 'eval_precision': 0.2895756219333735, 'eval_recall': 0.7870846981750117, 'eval_f1': 0.42338430558177587, 'eval_accuracy': 0.8398602166778805, 'eval_runtime': 14.5396, 'eval_samples_per_second': 468.375, 'eval_steps_per_second': 58.598, 'epoch': 4.0}
{'loss': 0.0567, 'grad_norm': 1.0904265642166138, 'learning_rate': 2.6894639556377083e-05, 'epoch': 4.62}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3503] 2024-09-05 23:50:16,255 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2705
[INFO|configuration_utils.py:472] 2024-09-05 23:50:16,257 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2705/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:50:17,297 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2705/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:50:17,298 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2705/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:50:17,299 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2705/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:50:19,462 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:50:19,463 >> 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 23:52:35,536 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 23:52:35,536 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 23:52:35,536 >>   Batch size = 8
{'eval_loss': 0.7005925178527832, 'eval_precision': 0.3152508603513856, 'eval_recall': 0.8144595226953674, 'eval_f1': 0.45455732567249935, 'eval_accuracy': 0.8564886649182308, 'eval_runtime': 14.2723, 'eval_samples_per_second': 477.148, 'eval_steps_per_second': 59.696, 'epoch': 5.0}
{'loss': 0.0428, 'grad_norm': 1.240962028503418, 'learning_rate': 2.2273567467652497e-05, 'epoch': 5.55}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3503] 2024-09-05 23:52:49,942 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3246
[INFO|configuration_utils.py:472] 2024-09-05 23:52:49,943 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3246/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:52:50,987 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3246/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:52:50,988 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3246/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:52:50,989 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3246/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:52:53,140 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:52:53,141 >> 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 23:55:08,784 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 23:55:08,784 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 23:55:08,784 >>   Batch size = 8
{'eval_loss': 0.8111857175827026, 'eval_precision': 0.30710659898477155, 'eval_recall': 0.8210107627515209, 'eval_f1': 0.44700636942675154, 'eval_accuracy': 0.8504057561069384, 'eval_runtime': 14.3991, 'eval_samples_per_second': 472.946, 'eval_steps_per_second': 59.17, 'epoch': 6.0}
{'loss': 0.0332, 'grad_norm': 0.44737720489501953, 'learning_rate': 1.7652495378927914e-05, 'epoch': 6.47}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3503] 2024-09-05 23:55:23,094 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3787
[INFO|configuration_utils.py:472] 2024-09-05 23:55:23,096 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3787/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:55:24,163 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3787/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:55:24,164 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3787/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:55:24,164 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3787/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:55:26,502 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:55:26,503 >> 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 23:57:41,355 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-05 23:57:41,355 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-05 23:57:41,355 >>   Batch size = 8
{'eval_loss': 0.904643714427948, 'eval_precision': 0.3113658932924077, 'eval_recall': 0.8069723912026205, 'eval_f1': 0.4493518337567586, 'eval_accuracy': 0.8532961676301372, 'eval_runtime': 14.3036, 'eval_samples_per_second': 476.105, 'eval_steps_per_second': 59.566, 'epoch': 7.0}
{'loss': 0.0257, 'grad_norm': 0.7116318941116333, 'learning_rate': 1.3031423290203328e-05, 'epoch': 7.39}


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100%|█████████▉| 849/852 [00:10<00:00, 80.25it/s]/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3503] 2024-09-05 23:57:55,784 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4328
[INFO|configuration_utils.py:472] 2024-09-05 23:57:55,785 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4328/config.json
[INFO|modeling_utils.py:2799] 2024-09-05 23:57:56,821 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4328/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:57:56,822 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4328/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:57:56,822 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4328/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-05 23:57:59,084 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-05 23:57:59,084 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-06 00:00:13,925 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-06 00:00:13,925 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-06 00:00:13,926 >>   Batch size = 8
{'eval_loss': 0.9722912907600403, 'eval_precision': 0.30602154335158044, 'eval_recall': 0.8109499298081423, 'eval_f1': 0.44435897435897437, 'eval_accuracy': 0.8481538440413583, 'eval_runtime': 14.4222, 'eval_samples_per_second': 472.19, 'eval_steps_per_second': 59.076, 'epoch': 8.0}
{'loss': 0.022, 'grad_norm': 0.9520462155342102, 'learning_rate': 8.410351201478742e-06, 'epoch': 8.32}


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100%|█████████▉| 849/852 [00:10<00:00, 80.96it/s]/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3503] 2024-09-06 00:00:28,180 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4869
[INFO|configuration_utils.py:472] 2024-09-06 00:00:28,182 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4869/config.json
[INFO|modeling_utils.py:2799] 2024-09-06 00:00:29,220 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4869/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-06 00:00:29,221 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4869/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-06 00:00:29,221 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4869/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-06 00:00:33,504 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-06 00:00:33,505 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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100%|██████████| 5410/5410 [25:26<00:00,  3.15it/s][INFO|trainer.py:3503] 2024-09-06 00:02:49,765 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-5410
[INFO|configuration_utils.py:472] 2024-09-06 00:02:49,766 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-5410/config.json
[INFO|modeling_utils.py:2799] 2024-09-06 00:02:50,798 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-5410/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-06 00:02:50,799 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-5410/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-06 00:02:50,799 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-5410/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-06 00:02:52,993 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-06 00:02:52,993 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-06 00:02:53,039 >> 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] 2024-09-06 00:02:53,042 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-06 00:02:53,043 >>   Num examples = 6810
[INFO|trainer.py:3824] 2024-09-06 00:02:53,043 >>   Batch size = 8
{'eval_loss': 1.002764105796814, 'eval_precision': 0.30871042747272404, 'eval_recall': 0.8076743097800655, 'eval_f1': 0.44668737060041414, 'eval_accuracy': 0.850186057368833, 'eval_runtime': 14.2485, 'eval_samples_per_second': 477.944, 'eval_steps_per_second': 59.796, 'epoch': 9.0}
{'loss': 0.0181, 'grad_norm': 0.6707109212875366, 'learning_rate': 3.789279112754159e-06, 'epoch': 9.24}


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  _warn_prf(average, modifier, msg_start, len(result))

                                                   

                                                 

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                                                 [INFO|trainer.py:3503] 2024-09-06 00:03:07,505 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-5410
[INFO|configuration_utils.py:472] 2024-09-06 00:03:07,507 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-5410/config.json
[INFO|modeling_utils.py:2799] 2024-09-06 00:03:09,034 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-5410/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-06 00:03:09,035 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-5410/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-06 00:03:09,035 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-5410/special_tokens_map.json
[INFO|trainer.py:2394] 2024-09-06 00:03:12,781 >> 

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


[INFO|trainer.py:2632] 2024-09-06 00:03:12,782 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-2705 (score: 0.45455732567249935).

                                                   

100%|██████████| 5410/5410 [25:49<00:00,  3.15it/s]
100%|██████████| 5410/5410 [25:49<00:00,  3.49it/s]
[INFO|trainer.py:4283] 2024-09-06 00:03:12,976 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-06 00:03:38,550 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-06 00:03:38,551 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-06 00:03:39,925 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-06 00:03:39,926 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-06 00:03:39,926 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3503] 2024-09-06 00:03:39,973 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-06 00:03:39,975 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-06 00:03:41,523 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-06 00:03:41,524 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-06 00:03:41,524 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 1.0022608041763306, 'eval_precision': 0.31162999550965426, 'eval_recall': 0.8118858212447356, 'eval_f1': 0.45038613797131544, 'eval_accuracy': 0.8533304955579661, 'eval_runtime': 14.4559, 'eval_samples_per_second': 471.089, 'eval_steps_per_second': 58.938, 'epoch': 10.0}
{'train_runtime': 1549.44, 'train_samples_per_second': 223.332, 'train_steps_per_second': 3.492, 'train_loss': 0.0812657987344287, 'epoch': 10.0}

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events.out.tfevents.1725579443.2a66098fac87.15776.0: 100%|██████████| 12.6k/12.6k [00:00<00:00, 36.7kB/s]