2024-05-13 11:35:59.227796: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-05-13 11:35:59.227844: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-05-13 11:35:59.229776: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-05-13 11:36:00.372355: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
05/13/2024 11:36:02 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
05/13/2024 11:36:02 - INFO - __main__ -   Training/evaluation parameters TrainingArguments(
_n_gpu=1,
accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None},
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=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_steps=None,
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=4,
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=4,
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'],
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,
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,
)
/usr/local/lib/python3.10/dist-packages/datasets/load.py:1486: FutureWarning: The repository for Rodrigo1771/multi-train-distemist-dev-ner contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/Rodrigo1771/multi-train-distemist-dev-ner
You can avoid this message in future by passing the argument `trust_remote_code=True`.
Passing `trust_remote_code=True` will be mandatory to load this dataset from the next major release of `datasets`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
[INFO|configuration_utils.py:726] 2024-05-13 11:36:06,753 >> 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:789] 2024-05-13 11:36:06,757 >> 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": "4.40.2",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|configuration_utils.py:726] 2024-05-13 11:36:07,020 >> 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:789] 2024-05-13 11:36:07,021 >> 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": "4.40.2",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|tokenization_utils_base.py:2087] 2024-05-13 11:36:07,031 >> 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:2087] 2024-05-13 11:36:07,031 >> 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:2087] 2024-05-13 11:36:07,031 >> loading file tokenizer.json from cache at None
[INFO|tokenization_utils_base.py:2087] 2024-05-13 11:36:07,031 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2087] 2024-05-13 11:36:07,031 >> 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:2087] 2024-05-13 11:36:07,031 >> 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:726] 2024-05-13 11:36:07,031 >> 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:789] 2024-05-13 11:36:07,032 >> 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": "4.40.2",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|configuration_utils.py:726] 2024-05-13 11:36:07,111 >> 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:789] 2024-05-13 11:36:07,112 >> 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": "4.40.2",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|modeling_utils.py:3429] 2024-05-13 11:36:07,512 >> 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:4160] 2024-05-13 11:36:07,636 >> 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:4172] 2024-05-13 11:36:07,636 >> 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.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")
/usr/local/lib/python3.10/dist-packages/datasets/load.py:759: FutureWarning: The repository for seqeval contains custom code which must be executed to correctly load the metric. You can inspect the repository content at https://raw.githubusercontent.com/huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py
You can avoid this message in future by passing the argument `trust_remote_code=True`.
Passing `trust_remote_code=True` will be mandatory to load this metric from the next major release of `datasets`.
  warnings.warn(
[INFO|trainer.py:786] 2024-05-13 11:36:12,193 >> The following columns in the training set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: tokens, ner_tags, id. If tokens, ner_tags, id are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:2048] 2024-05-13 11:36:12,203 >> ***** Running training *****
[INFO|trainer.py:2049] 2024-05-13 11:36:12,203 >>   Num examples = 27,224
[INFO|trainer.py:2050] 2024-05-13 11:36:12,204 >>   Num Epochs = 10
[INFO|trainer.py:2051] 2024-05-13 11:36:12,204 >>   Instantaneous batch size per device = 4
[INFO|trainer.py:2054] 2024-05-13 11:36:12,204 >>   Total train batch size (w. parallel, distributed & accumulation) = 16
[INFO|trainer.py:2055] 2024-05-13 11:36:12,204 >>   Gradient Accumulation steps = 4
[INFO|trainer.py:2056] 2024-05-13 11:36:12,204 >>   Total optimization steps = 17,010
[INFO|trainer.py:2057] 2024-05-13 11:36:12,204 >>   Number of trainable parameters = 124,059,657

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[INFO|trainer.py:3614] 2024-05-13 11:41:19,772 >> ***** Running Evaluation *****
[INFO|trainer.py:3616] 2024-05-13 11:41:19,772 >>   Num examples = 6807
[INFO|trainer.py:3619] 2024-05-13 11:41:19,772 >>   Batch size = 8
{'loss': 0.4174, 'grad_norm': 1.9668159484863281, 'learning_rate': 4.853027630805409e-05, 'epoch': 0.29}
{'loss': 0.2765, 'grad_norm': 3.246731758117676, 'learning_rate': 4.7060552616108174e-05, 'epoch': 0.59}
{'loss': 0.2596, 'grad_norm': 2.936720609664917, 'learning_rate': 4.559082892416226e-05, 'epoch': 0.88}


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

                                                    

                                                 

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                                                 [INFO|trainer.py:3305] 2024-05-13 11:41:36,042 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1701
[INFO|configuration_utils.py:471] 2024-05-13 11:41:36,043 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1701/config.json
[INFO|modeling_utils.py:2590] 2024-05-13 11:41:37,005 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1701/model.safetensors
[INFO|tokenization_utils_base.py:2488] 2024-05-13 11:41:37,006 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1701/tokenizer_config.json
[INFO|tokenization_utils_base.py:2497] 2024-05-13 11:41:37,006 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1701/special_tokens_map.json
[INFO|tokenization_utils_base.py:2488] 2024-05-13 11:41:42,699 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2497] 2024-05-13 11:41:42,699 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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