2024-09-09 21:03:52.700445: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-09-09 21:03:52.718609: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-09-09 21:03:52.739675: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-09-09 21:03:52.746064: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-09-09 21:03:52.761965: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-09-09 21:03:54.014019: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
09/09/2024 21:03:55 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
09/09/2024 21:03:55 - 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] 2024-09-09 21:04:16,367 >> 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] 2024-09-09 21:04:16,371 >> 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-MORFOLOGIA_NEOPLASIA",
    "2": "I-MORFOLOGIA_NEOPLASIA"
  },
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "label2id": {
    "B-MORFOLOGIA_NEOPLASIA": 1,
    "I-MORFOLOGIA_NEOPLASIA": 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] 2024-09-09 21:04:16,606 >> 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] 2024-09-09 21:04:16,607 >> 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] 2024-09-09 21:04:16,617 >> 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] 2024-09-09 21:04:16,617 >> 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] 2024-09-09 21:04:16,617 >> loading file tokenizer.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-09 21:04:16,617 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2269] 2024-09-09 21:04:16,617 >> 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] 2024-09-09 21:04:16,617 >> 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] 2024-09-09 21:04:16,617 >> 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] 2024-09-09 21:04:16,618 >> 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] 2024-09-09 21:04:16,696 >> 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] 2024-09-09 21:04:16,698 >> 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] 2024-09-09 21:04:17,023 >> 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] 2024-09-09 21:04:17,101 >> 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] 2024-09-09 21:04:17,101 >> 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] 2024-09-09 21:04:25,177 >> The following columns in the training set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, tokens, id. If ner_tags, tokens, id are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:2134] 2024-09-09 21:04:25,739 >> ***** Running training *****
[INFO|trainer.py:2135] 2024-09-09 21:04:25,740 >>   Num examples = 39,426
[INFO|trainer.py:2136] 2024-09-09 21:04:25,740 >>   Num Epochs = 10
[INFO|trainer.py:2137] 2024-09-09 21:04:25,740 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2140] 2024-09-09 21:04:25,740 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2141] 2024-09-09 21:04:25,740 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2142] 2024-09-09 21:04:25,740 >>   Total optimization steps = 6,160
[INFO|trainer.py:2143] 2024-09-09 21:04:25,740 >>   Number of trainable parameters = 124,055,043

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[INFO|trainer.py:3819] 2024-09-09 21:06:59,291 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 21:06:59,291 >>   Num examples = 7354
[INFO|trainer.py:3824] 2024-09-09 21:06:59,291 >>   Batch size = 8
{'loss': 0.0571, 'grad_norm': 0.7429273724555969, 'learning_rate': 4.5941558441558444e-05, 'epoch': 0.81}


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                                                 [INFO|trainer.py:3503] 2024-09-09 21:07:14,685 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-616
[INFO|configuration_utils.py:472] 2024-09-09 21:07:14,686 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-616/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:07:15,712 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-616/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:07:15,713 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-616/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:07:15,714 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-616/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:07:18,923 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:07:18,923 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 21:09:50,710 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 21:09:50,710 >>   Num examples = 7354
[INFO|trainer.py:3824] 2024-09-09 21:09:50,710 >>   Batch size = 8
{'eval_loss': 0.02658958174288273, 'eval_precision': 0.776657060518732, 'eval_recall': 0.8491532099251674, 'eval_f1': 0.8112888052681091, 'eval_accuracy': 0.9906421648980531, 'eval_runtime': 15.3903, 'eval_samples_per_second': 477.832, 'eval_steps_per_second': 59.778, 'epoch': 1.0}
{'loss': 0.018, 'grad_norm': 0.32481127977371216, 'learning_rate': 4.1883116883116886e-05, 'epoch': 1.62}


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                                                 [INFO|trainer.py:3503] 2024-09-09 21:10:06,470 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1233
[INFO|configuration_utils.py:472] 2024-09-09 21:10:06,472 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1233/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:10:07,469 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1233/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:10:07,471 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1233/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:10:07,471 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1233/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:10:12,539 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:10:12,540 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 21:12:44,374 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 21:12:44,375 >>   Num examples = 7354
[INFO|trainer.py:3824] 2024-09-09 21:12:44,375 >>   Batch size = 8
{'eval_loss': 0.030444670468568802, 'eval_precision': 0.8075046904315197, 'eval_recall': 0.8475777865301299, 'eval_f1': 0.8270561106840891, 'eval_accuracy': 0.9914186293963707, 'eval_runtime': 15.7588, 'eval_samples_per_second': 466.66, 'eval_steps_per_second': 58.38, 'epoch': 2.0}
{'loss': 0.0101, 'grad_norm': 0.10290508717298508, 'learning_rate': 3.782467532467533e-05, 'epoch': 2.43}


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                                                 [INFO|trainer.py:3503] 2024-09-09 21:12:59,927 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1849
[INFO|configuration_utils.py:472] 2024-09-09 21:12:59,929 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1849/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:13:00,931 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1849/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:13:00,932 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1849/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:13:00,932 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1849/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:13:03,130 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:13:03,130 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 21:15:35,056 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 21:15:35,056 >>   Num examples = 7354
[INFO|trainer.py:3824] 2024-09-09 21:15:35,056 >>   Batch size = 8
{'eval_loss': 0.03564191609621048, 'eval_precision': 0.8159392789373814, 'eval_recall': 0.8467900748326113, 'eval_f1': 0.8310784692694241, 'eval_accuracy': 0.9906249101314238, 'eval_runtime': 15.5516, 'eval_samples_per_second': 472.877, 'eval_steps_per_second': 59.158, 'epoch': 3.0}
{'loss': 0.0057, 'grad_norm': 0.3212472200393677, 'learning_rate': 3.376623376623377e-05, 'epoch': 3.24}


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                                                 [INFO|trainer.py:3503] 2024-09-09 21:15:50,862 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2466
[INFO|configuration_utils.py:472] 2024-09-09 21:15:50,863 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2466/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:15:51,904 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2466/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:15:51,905 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2466/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:15:51,906 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2466/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:15:54,029 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:15:54,029 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 21:18:26,677 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 21:18:26,677 >>   Num examples = 7354
[INFO|trainer.py:3824] 2024-09-09 21:18:26,677 >>   Batch size = 8
{'eval_loss': 0.03653988987207413, 'eval_precision': 0.8239355581127733, 'eval_recall': 0.8460023631350926, 'eval_f1': 0.8348231636222309, 'eval_accuracy': 0.9910217697638973, 'eval_runtime': 15.8023, 'eval_samples_per_second': 465.375, 'eval_steps_per_second': 58.219, 'epoch': 4.0}
{'loss': 0.0039, 'grad_norm': 1.049054741859436, 'learning_rate': 2.9707792207792207e-05, 'epoch': 4.06}
{'loss': 0.0027, 'grad_norm': 0.12911342084407806, 'learning_rate': 2.5649350649350652e-05, 'epoch': 4.87}


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                                                 [INFO|trainer.py:3503] 2024-09-09 21:18:42,155 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3082
[INFO|configuration_utils.py:472] 2024-09-09 21:18:42,157 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3082/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:18:43,163 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3082/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:18:43,164 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3082/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:18:43,165 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3082/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:18:45,331 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:18:45,331 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 21:21:17,847 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 21:21:17,847 >>   Num examples = 7354
[INFO|trainer.py:3824] 2024-09-09 21:21:17,848 >>   Batch size = 8
{'eval_loss': 0.03956884518265724, 'eval_precision': 0.8211289092295957, 'eval_recall': 0.8479716423788893, 'eval_f1': 0.8343344313117613, 'eval_accuracy': 0.9915624191182815, 'eval_runtime': 15.4772, 'eval_samples_per_second': 475.151, 'eval_steps_per_second': 59.442, 'epoch': 5.0}
{'loss': 0.0018, 'grad_norm': 0.1811482459306717, 'learning_rate': 2.1590909090909093e-05, 'epoch': 5.68}


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                                                 [INFO|trainer.py:3503] 2024-09-09 21:21:33,345 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3699
[INFO|configuration_utils.py:472] 2024-09-09 21:21:33,346 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3699/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:21:34,363 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3699/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:21:34,364 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3699/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:21:34,364 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3699/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:21:36,603 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:21:36,603 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 21:24:08,922 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 21:24:08,922 >>   Num examples = 7354
[INFO|trainer.py:3824] 2024-09-09 21:24:08,922 >>   Batch size = 8
{'eval_loss': 0.04350048676133156, 'eval_precision': 0.8306176582038651, 'eval_recall': 0.8633320204805042, 'eval_f1': 0.846658941676323, 'eval_accuracy': 0.9915394127627757, 'eval_runtime': 15.4958, 'eval_samples_per_second': 474.581, 'eval_steps_per_second': 59.371, 'epoch': 6.0}
{'loss': 0.0013, 'grad_norm': 0.0691465437412262, 'learning_rate': 1.7532467532467535e-05, 'epoch': 6.49}


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                                                 [INFO|trainer.py:3503] 2024-09-09 21:24:24,581 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4315
[INFO|configuration_utils.py:472] 2024-09-09 21:24:24,583 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4315/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:24:25,598 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4315/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:24:25,599 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4315/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:24:25,599 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4315/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:24:32,591 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:24:32,591 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 21:27:04,710 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 21:27:04,710 >>   Num examples = 7354
[INFO|trainer.py:3824] 2024-09-09 21:27:04,710 >>   Batch size = 8
{'eval_loss': 0.047813717275857925, 'eval_precision': 0.8462436745815493, 'eval_recall': 0.8562426152028357, 'eval_f1': 0.8512137823022711, 'eval_accuracy': 0.991867253328732, 'eval_runtime': 15.6578, 'eval_samples_per_second': 469.669, 'eval_steps_per_second': 58.757, 'epoch': 7.0}
{'loss': 0.0008, 'grad_norm': 0.016636306419968605, 'learning_rate': 1.3474025974025975e-05, 'epoch': 7.3}


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                                                 [INFO|trainer.py:3503] 2024-09-09 21:27:20,397 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4932
[INFO|configuration_utils.py:472] 2024-09-09 21:27:20,398 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4932/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:27:21,413 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4932/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:27:21,414 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4932/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:27:21,415 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4932/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:27:23,577 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:27:23,578 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3819] 2024-09-09 21:29:55,623 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 21:29:55,623 >>   Num examples = 7354
[INFO|trainer.py:3824] 2024-09-09 21:29:55,624 >>   Batch size = 8
{'eval_loss': 0.04689360409975052, 'eval_precision': 0.8347328244274809, 'eval_recall': 0.8613627412367074, 'eval_f1': 0.8478387284357433, 'eval_accuracy': 0.9914761452851351, 'eval_runtime': 15.6856, 'eval_samples_per_second': 468.838, 'eval_steps_per_second': 58.653, 'epoch': 8.0}
{'loss': 0.0008, 'grad_norm': 0.02262457087635994, 'learning_rate': 9.415584415584416e-06, 'epoch': 8.11}
{'loss': 0.0004, 'grad_norm': 0.6416856646537781, 'learning_rate': 5.357142857142857e-06, 'epoch': 8.92}


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                                                 [INFO|trainer.py:3503] 2024-09-09 21:30:11,210 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-5548
[INFO|configuration_utils.py:472] 2024-09-09 21:30:11,211 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-5548/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:30:12,224 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-5548/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:30:12,225 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-5548/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:30:12,225 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-5548/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:30:14,403 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:30:14,403 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|configuration_utils.py:472] 2024-09-09 21:32:45,012 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-6160/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:32:46,033 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-6160/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:32:46,034 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-6160/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:32:46,034 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-6160/special_tokens_map.json
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:32:51,805 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:32:51,805 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:811] 2024-09-09 21:32:51,860 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, tokens, id. If ner_tags, tokens, id are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 21:32:51,863 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 21:32:51,863 >>   Num examples = 7354
[INFO|trainer.py:3824] 2024-09-09 21:32:51,863 >>   Batch size = 8
{'eval_loss': 0.05153572931885719, 'eval_precision': 0.8414164742109315, 'eval_recall': 0.860968885387948, 'eval_f1': 0.851080397118941, 'eval_accuracy': 0.9918845080953613, 'eval_runtime': 15.5849, 'eval_samples_per_second': 471.866, 'eval_steps_per_second': 59.031, 'epoch': 9.0}
{'loss': 0.0002, 'grad_norm': 0.011619620956480503, 'learning_rate': 1.2987012987012988e-06, 'epoch': 9.73}


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                                                 [INFO|trainer.py:3503] 2024-09-09 21:33:07,811 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-6160
[INFO|configuration_utils.py:472] 2024-09-09 21:33:07,813 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-6160/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:33:09,303 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-6160/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:33:09,304 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-6160/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:33:09,305 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-6160/special_tokens_map.json
[INFO|trainer.py:2394] 2024-09-09 21:33:11,056 >> 

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


[INFO|trainer.py:2632] 2024-09-09 21:33:11,056 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-4315 (score: 0.8512137823022711).

                                                   

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[INFO|trainer.py:4283] 2024-09-09 21:33:11,257 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3503] 2024-09-09 21:33:24,305 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 21:33:24,306 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:33:25,652 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:33:25,653 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:33:25,654 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3503] 2024-09-09 21:33:25,702 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 21:33:25,703 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:33:26,889 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:33:26,890 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:33:26,890 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.05196274444460869, 'eval_precision': 0.8386477141759509, 'eval_recall': 0.85978731784167, 'eval_f1': 0.8490859587709063, 'eval_accuracy': 0.9917694763178329, 'eval_runtime': 15.9471, 'eval_samples_per_second': 461.149, 'eval_steps_per_second': 57.691, 'epoch': 9.99}
{'train_runtime': 1725.5138, 'train_samples_per_second': 228.488, 'train_steps_per_second': 3.57, 'train_loss': 0.008344567238123386, 'epoch': 9.99}

events.out.tfevents.1725915865.0ada7e7d1d89.20997.0:   0%|          | 0.00/12.8k [00:00<?, ?B/s]
events.out.tfevents.1725915865.0ada7e7d1d89.20997.0: 100%|██████████| 12.8k/12.8k [00:00<00:00, 40.4kB/s]
***** train metrics *****
  epoch                    =     9.9919
  total_flos               = 17879389GF
  train_loss               =     0.0083
  train_runtime            = 0:28:45.51
  train_samples            =      39426
  train_samples_per_second =    228.488
  train_steps_per_second   =       3.57
09/09/2024 21:33:32 - INFO - __main__ -   *** Evaluate ***
[INFO|trainer.py:811] 2024-09-09 21:33:32,451 >> The following columns in the evaluation set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, tokens, id. If ner_tags, tokens, id are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 21:33:32,453 >> 
***** Running Evaluation *****
[INFO|trainer.py:3821] 2024-09-09 21:33:32,453 >>   Num examples = 7354
[INFO|trainer.py:3824] 2024-09-09 21:33:32,453 >>   Batch size = 8

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***** eval metrics *****
  epoch                   =     9.9919
  eval_accuracy           =     0.9919
  eval_f1                 =     0.8512
  eval_loss               =     0.0478
  eval_precision          =     0.8462
  eval_recall             =     0.8562
  eval_runtime            = 0:00:15.61
  eval_samples            =       7354
  eval_samples_per_second =    470.984
  eval_steps_per_second   =     58.921
09/09/2024 21:33:48 - INFO - __main__ -   *** Predict ***
[INFO|trainer.py:811] 2024-09-09 21:33:48,070 >> The following columns in the test set don't have a corresponding argument in `RobertaForTokenClassification.forward` and have been ignored: ner_tags, tokens, id. If ner_tags, tokens, id are not expected by `RobertaForTokenClassification.forward`,  you can safely ignore this message.
[INFO|trainer.py:3819] 2024-09-09 21:33:48,072 >> 
***** Running Prediction *****
[INFO|trainer.py:3821] 2024-09-09 21:33:48,072 >>   Num examples = 10838
[INFO|trainer.py:3824] 2024-09-09 21:33:48,073 >>   Batch size = 8

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[INFO|trainer.py:3503] 2024-09-09 21:34:11,757 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-09-09 21:34:11,759 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2799] 2024-09-09 21:34:13,106 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2684] 2024-09-09 21:34:13,107 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2693] 2024-09-09 21:34:13,107 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
***** predict metrics *****
  predict_accuracy           =     0.9922
  predict_f1                 =     0.8534
  predict_loss               =     0.0449
  predict_precision          =     0.8332
  predict_recall             =     0.8747
  predict_runtime            = 0:00:23.14
  predict_samples_per_second =    468.329
  predict_steps_per_second   =     58.552

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