2024-08-30 20:25:26.549777: 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`.
2024-08-30 20:25:26.568217: 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
2024-08-30 20:25:26.590253: 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
2024-08-30 20:25:26.597224: 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
2024-08-30 20:25:26.612962: 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.
2024-08-30 20:25:27.916342: 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:1494: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
  warnings.warn(
08/30/2024 20:25:29 - WARNING - __main__ -   Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
08/30/2024 20:25:29 - INFO - __main__ -   Training/evaluation parameters TrainingArguments(
_n_gpu=1,
accelerator_config={'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None, 'use_configured_state': False},
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
auto_find_batch_size=False,
batch_eval_metrics=False,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_persistent_workers=False,
dataloader_pin_memory=True,
dataloader_prefetch_factor=None,
ddp_backend=None,
ddp_broadcast_buffers=None,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
ddp_timeout=1800,
debug=[],
deepspeed=None,
disable_tqdm=False,
dispatch_batches=None,
do_eval=True,
do_predict=True,
do_train=True,
eval_accumulation_steps=None,
eval_delay=0,
eval_do_concat_batches=True,
eval_on_start=False,
eval_steps=None,
eval_strategy=epoch,
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,
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] 2024-08-30 20:25:41,919 >> 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] 2024-08-30 20:25:41,923 >> Model config RobertaConfig {
  "_name_or_path": "PlanTL-GOB-ES/bsc-bio-ehr-es",
  "architectures": [
    "RobertaForMaskedLM"
  ],
  "attention_probs_dropout_prob": 0.1,
  "bos_token_id": 0,
  "classifier_dropout": null,
  "eos_token_id": 2,
  "finetuning_task": "ner",
  "gradient_checkpointing": false,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "id2label": {
    "0": "O",
    "1": "B-ENFERMEDAD",
    "2": "I-ENFERMEDAD"
  },
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "label2id": {
    "B-ENFERMEDAD": 1,
    "I-ENFERMEDAD": 2,
    "O": 0
  },
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 514,
  "model_type": "roberta",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 1,
  "position_embedding_type": "absolute",
  "transformers_version": "4.42.4",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|configuration_utils.py:733] 2024-08-30 20:25:42,016 >> 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] 2024-08-30 20:25:42,017 >> 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": "4.42.4",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|tokenization_utils_base.py:2161] 2024-08-30 20:25:42,027 >> 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:2161] 2024-08-30 20:25:42,028 >> 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:2161] 2024-08-30 20:25:42,028 >> loading file tokenizer.json from cache at None
[INFO|tokenization_utils_base.py:2161] 2024-08-30 20:25:42,028 >> loading file added_tokens.json from cache at None
[INFO|tokenization_utils_base.py:2161] 2024-08-30 20:25:42,028 >> 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:2161] 2024-08-30 20:25:42,028 >> 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] 2024-08-30 20:25:42,028 >> 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] 2024-08-30 20:25:42,029 >> 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": "4.42.4",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|configuration_utils.py:733] 2024-08-30 20:25:42,112 >> 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] 2024-08-30 20:25:42,113 >> 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": "4.42.4",
  "type_vocab_size": 1,
  "use_cache": true,
  "vocab_size": 50262
}

[INFO|modeling_utils.py:3556] 2024-08-30 20:25:42,300 >> 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:4354] 2024-08-30 20:25:42,438 >> 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:4366] 2024-08-30 20:25:42,438 >> 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:805] 2024-08-30 20:25:48,288 >> 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:2128] 2024-08-30 20:25:48,850 >> ***** Running training *****
[INFO|trainer.py:2129] 2024-08-30 20:25:48,850 >>   Num examples = 27,229
[INFO|trainer.py:2130] 2024-08-30 20:25:48,850 >>   Num Epochs = 10
[INFO|trainer.py:2131] 2024-08-30 20:25:48,850 >>   Instantaneous batch size per device = 32
[INFO|trainer.py:2134] 2024-08-30 20:25:48,850 >>   Total train batch size (w. parallel, distributed & accumulation) = 64
[INFO|trainer.py:2135] 2024-08-30 20:25:48,851 >>   Gradient Accumulation steps = 2
[INFO|trainer.py:2136] 2024-08-30 20:25:48,851 >>   Total optimization steps = 4,250
[INFO|trainer.py:2137] 2024-08-30 20:25:48,851 >>   Number of trainable parameters = 124,055,043

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 10%|█         | 425/4250 [01:42<14:28,  4.40it/s][INFO|trainer.py:805] 2024-08-30 20:27:31,190 >> The following columns in the evaluation 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:3788] 2024-08-30 20:27:31,192 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 20:27:31,192 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 20:27:31,192 >>   Batch size = 8


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                                                 [INFO|trainer.py:3478] 2024-08-30 20:27:45,881 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-425
[INFO|configuration_utils.py:472] 2024-08-30 20:27:45,882 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-425/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 20:27:47,247 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-425/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:27:47,248 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-425/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:27:47,248 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-425/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:27:50,017 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:27:50,017 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 20:29:32,382 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 20:29:32,382 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 20:29:32,382 >>   Batch size = 8
{'eval_loss': 0.07383445650339127, 'eval_precision': 0.7233218588640276, 'eval_recall': 0.786616752456715, 'eval_f1': 0.7536426810132258, 'eval_accuracy': 0.9733340656624604, 'eval_runtime': 14.6851, 'eval_samples_per_second': 463.736, 'eval_steps_per_second': 58.018, 'epoch': 1.0}
{'loss': 0.0996, 'grad_norm': 1.1428656578063965, 'learning_rate': 4.411764705882353e-05, 'epoch': 1.18}


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                                                 [INFO|trainer.py:3478] 2024-08-30 20:29:46,798 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-851
[INFO|configuration_utils.py:472] 2024-08-30 20:29:46,799 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-851/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 20:29:48,128 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-851/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:29:48,129 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-851/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:29:48,129 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-851/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:29:50,700 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:29:50,700 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 20:31:32,722 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 20:31:32,722 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 20:31:32,722 >>   Batch size = 8
{'eval_loss': 0.07865303754806519, 'eval_precision': 0.7363811151463363, 'eval_recall': 0.8065044454843239, 'eval_f1': 0.7698492462311558, 'eval_accuracy': 0.9743364411550661, 'eval_runtime': 14.4143, 'eval_samples_per_second': 472.448, 'eval_steps_per_second': 59.108, 'epoch': 2.0}
{'loss': 0.0458, 'grad_norm': 1.476723074913025, 'learning_rate': 3.8235294117647055e-05, 'epoch': 2.35}


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                                                 [INFO|trainer.py:3478] 2024-08-30 20:31:47,406 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1276
[INFO|configuration_utils.py:472] 2024-08-30 20:31:47,407 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1276/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 20:31:48,772 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1276/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:31:48,773 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1276/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:31:48,773 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1276/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:31:51,081 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:31:51,081 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 20:33:32,909 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 20:33:32,909 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 20:33:32,909 >>   Batch size = 8
{'eval_loss': 0.07876282930374146, 'eval_precision': 0.7715297764002657, 'eval_recall': 0.8153954141319607, 'eval_f1': 0.7928563303378454, 'eval_accuracy': 0.9759155258351985, 'eval_runtime': 14.6794, 'eval_samples_per_second': 463.917, 'eval_steps_per_second': 58.041, 'epoch': 3.0}
{'loss': 0.0279, 'grad_norm': 0.4094911515712738, 'learning_rate': 3.235294117647059e-05, 'epoch': 3.53}


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                                                 [INFO|trainer.py:3478] 2024-08-30 20:33:47,735 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-1702
[INFO|configuration_utils.py:472] 2024-08-30 20:33:47,736 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-1702/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 20:33:49,085 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-1702/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:33:49,086 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-1702/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:33:49,086 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-1702/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:33:51,483 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:33:51,483 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 20:35:33,184 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 20:35:33,184 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 20:35:33,184 >>   Batch size = 8
{'eval_loss': 0.09221930056810379, 'eval_precision': 0.775441735629613, 'eval_recall': 0.8111839026672906, 'eval_f1': 0.7929102344196683, 'eval_accuracy': 0.9757026926826589, 'eval_runtime': 14.825, 'eval_samples_per_second': 459.361, 'eval_steps_per_second': 57.471, 'epoch': 4.0}
{'loss': 0.0169, 'grad_norm': 0.49960169196128845, 'learning_rate': 2.647058823529412e-05, 'epoch': 4.7}


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                                                 [INFO|trainer.py:3478] 2024-08-30 20:35:47,552 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2127
[INFO|configuration_utils.py:472] 2024-08-30 20:35:47,553 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2127/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 20:35:48,877 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2127/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:35:48,878 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2127/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:35:48,879 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2127/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:35:51,191 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:35:51,191 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 20:37:33,100 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 20:37:33,100 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 20:37:33,100 >>   Batch size = 8
{'eval_loss': 0.09940312057733536, 'eval_precision': 0.7584782608695653, 'eval_recall': 0.816331305568554, 'eval_f1': 0.7863421230561191, 'eval_accuracy': 0.974439424938553, 'eval_runtime': 14.3671, 'eval_samples_per_second': 473.999, 'eval_steps_per_second': 59.302, 'epoch': 5.0}
{'loss': 0.0114, 'grad_norm': 0.6507154703140259, 'learning_rate': 2.058823529411765e-05, 'epoch': 5.88}


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                                                 [INFO|trainer.py:3478] 2024-08-30 20:37:47,635 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2553
[INFO|configuration_utils.py:472] 2024-08-30 20:37:47,636 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2553/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 20:37:48,898 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2553/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:37:48,899 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2553/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:37:48,899 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2553/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:37:51,199 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:37:51,200 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 20:39:33,182 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 20:39:33,182 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 20:39:33,182 >>   Batch size = 8
{'eval_loss': 0.10803968459367752, 'eval_precision': 0.7765501691093574, 'eval_recall': 0.8058025269068788, 'eval_f1': 0.7909059593523942, 'eval_accuracy': 0.9764579070948961, 'eval_runtime': 14.5335, 'eval_samples_per_second': 468.572, 'eval_steps_per_second': 58.623, 'epoch': 6.0}


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                                                 [INFO|trainer.py:3478] 2024-08-30 20:39:47,868 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-2978
[INFO|configuration_utils.py:472] 2024-08-30 20:39:47,869 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-2978/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 20:39:49,212 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-2978/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:39:49,213 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-2978/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:39:49,213 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-2978/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:39:51,528 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:39:51,528 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 20:41:33,406 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 20:41:33,406 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 20:41:33,406 >>   Batch size = 8
{'eval_loss': 0.11656877398490906, 'eval_precision': 0.7792032410533424, 'eval_recall': 0.810014038371549, 'eval_f1': 0.7943099690260411, 'eval_accuracy': 0.975997912861988, 'eval_runtime': 14.6842, 'eval_samples_per_second': 463.764, 'eval_steps_per_second': 58.022, 'epoch': 7.0}
{'loss': 0.0079, 'grad_norm': 0.15811629593372345, 'learning_rate': 1.4705882352941177e-05, 'epoch': 7.05}


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                                                 [INFO|trainer.py:3478] 2024-08-30 20:41:48,181 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3404
[INFO|configuration_utils.py:472] 2024-08-30 20:41:48,183 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3404/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 20:41:49,516 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3404/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:41:49,518 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3404/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:41:49,518 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3404/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:41:51,822 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:41:51,822 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|trainer.py:3788] 2024-08-30 20:43:32,655 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 20:43:32,655 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 20:43:32,655 >>   Batch size = 8
{'eval_loss': 0.1294233798980713, 'eval_precision': 0.7938948817994033, 'eval_recall': 0.8093121197941039, 'eval_f1': 0.8015293708724366, 'eval_accuracy': 0.9767668584453568, 'eval_runtime': 14.774, 'eval_samples_per_second': 460.946, 'eval_steps_per_second': 57.669, 'epoch': 8.0}
{'loss': 0.0053, 'grad_norm': 0.11404519528150558, 'learning_rate': 8.823529411764707e-06, 'epoch': 8.23}


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                                                 [INFO|trainer.py:3478] 2024-08-30 20:43:47,100 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-3829
[INFO|configuration_utils.py:472] 2024-08-30 20:43:47,101 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-3829/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 20:43:48,426 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-3829/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:43:48,427 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-3829/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:43:48,428 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-3829/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:43:50,869 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:43:50,869 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json

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[INFO|configuration_utils.py:472] 2024-08-30 20:45:30,894 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4250/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 20:45:32,240 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4250/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:45:32,241 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4250/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:45:32,241 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4250/special_tokens_map.json
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:45:34,549 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:45:34,550 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:805] 2024-08-30 20:45:34,601 >> The following columns in the evaluation 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:3788] 2024-08-30 20:45:34,604 >> 
***** Running Evaluation *****
[INFO|trainer.py:3790] 2024-08-30 20:45:34,604 >>   Num examples = 6810
[INFO|trainer.py:3793] 2024-08-30 20:45:34,604 >>   Batch size = 8
{'eval_loss': 0.13398884236812592, 'eval_precision': 0.787630741246021, 'eval_recall': 0.8104819840898456, 'eval_f1': 0.7988929889298894, 'eval_accuracy': 0.9766158155629093, 'eval_runtime': 14.4437, 'eval_samples_per_second': 471.487, 'eval_steps_per_second': 58.988, 'epoch': 9.0}
{'loss': 0.0038, 'grad_norm': 0.5636719465255737, 'learning_rate': 2.9411764705882355e-06, 'epoch': 9.4}


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                                                 [INFO|trainer.py:3478] 2024-08-30 20:45:49,488 >> Saving model checkpoint to /content/dissertation/scripts/ner/output/checkpoint-4250
[INFO|configuration_utils.py:472] 2024-08-30 20:45:49,490 >> Configuration saved in /content/dissertation/scripts/ner/output/checkpoint-4250/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 20:45:50,835 >> Model weights saved in /content/dissertation/scripts/ner/output/checkpoint-4250/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:45:50,838 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/checkpoint-4250/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:45:50,839 >> Special tokens file saved in /content/dissertation/scripts/ner/output/checkpoint-4250/special_tokens_map.json
[INFO|trainer.py:2383] 2024-08-30 20:45:52,645 >> 

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


[INFO|trainer.py:2621] 2024-08-30 20:45:52,646 >> Loading best model from /content/dissertation/scripts/ner/output/checkpoint-3404 (score: 0.8015293708724366).

                                                   

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[INFO|trainer.py:4239] 2024-08-30 20:45:52,840 >> Waiting for the current checkpoint push to be finished, this might take a couple of minutes.
[INFO|trainer.py:3478] 2024-08-30 20:45:55,484 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-08-30 20:45:55,486 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 20:45:56,829 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:45:56,830 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:45:56,830 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
[INFO|trainer.py:3478] 2024-08-30 20:45:56,879 >> Saving model checkpoint to /content/dissertation/scripts/ner/output
[INFO|configuration_utils.py:472] 2024-08-30 20:45:56,881 >> Configuration saved in /content/dissertation/scripts/ner/output/config.json
[INFO|modeling_utils.py:2690] 2024-08-30 20:45:58,218 >> Model weights saved in /content/dissertation/scripts/ner/output/model.safetensors
[INFO|tokenization_utils_base.py:2574] 2024-08-30 20:45:58,219 >> tokenizer config file saved in /content/dissertation/scripts/ner/output/tokenizer_config.json
[INFO|tokenization_utils_base.py:2583] 2024-08-30 20:45:58,219 >> Special tokens file saved in /content/dissertation/scripts/ner/output/special_tokens_map.json
{'eval_loss': 0.13674204051494598, 'eval_precision': 0.7882031427920747, 'eval_recall': 0.8097800655124006, 'eval_f1': 0.7988459319099828, 'eval_accuracy': 0.9766776058330014, 'eval_runtime': 14.8828, 'eval_samples_per_second': 457.575, 'eval_steps_per_second': 57.247, 'epoch': 9.99}
{'train_runtime': 1203.9865, 'train_samples_per_second': 226.157, 'train_steps_per_second': 3.53, 'train_loss': 0.02590363489880281, 'epoch': 9.99}

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