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breast-cancer-biomedical-ner-sp-1

Token Classification
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TensorBoard
Safetensors
Spanish
xlm-roberta
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metadata
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: breast-cancer-biomedical-ner-sp
    results: []
language:
  - es
pipeline_tag: token-classification

breast-cancer-biomedical-ner-sp

This model is a fine-tuned version of FacebookAI/xlm-roberta-large on the breast cancer dataset in spanish: anvorja/breast_cancer_es_dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0178
  • Precision: 0.9793
  • Recall: 0.9867
  • F1: 0.9830
  • Accuracy: 0.9948

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 64
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine_with_restarts
  • lr_scheduler_warmup_ratio: 0.2
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
2.1649 1.0 112 1.9231 0.0099 0.0051 0.0067 0.6051
0.8237 2.0 224 0.6627 0.5850 0.5669 0.5758 0.8329
0.391 3.0 336 0.3065 0.7703 0.8068 0.7882 0.9269
0.2341 4.0 448 0.1873 0.8221 0.875 0.8477 0.9558
0.1588 5.0 560 0.1463 0.8642 0.9078 0.8855 0.9641
0.129 6.0 672 0.1099 0.8836 0.9205 0.9017 0.9720
0.1098 7.0 784 0.0902 0.9166 0.9501 0.9330 0.9798
0.0764 8.0 896 0.0772 0.9167 0.9444 0.9303 0.9822
0.0745 9.0 1008 0.0613 0.9358 0.9577 0.9466 0.9856
0.0645 10.0 1120 0.0496 0.9564 0.9691 0.9627 0.9891
0.0623 11.0 1232 0.0426 0.9595 0.9722 0.9658 0.9898
0.0489 12.0 1344 0.0393 0.9596 0.9741 0.9668 0.9901
0.0389 13.0 1456 0.0307 0.9646 0.9798 0.9721 0.9917
0.0371 14.0 1568 0.0255 0.9737 0.9798 0.9767 0.9934
0.0232 15.0 1680 0.0228 0.9756 0.9830 0.9792 0.9939
0.0237 16.0 1792 0.0205 0.9762 0.9842 0.9802 0.9939
0.0262 17.0 1904 0.0178 0.9793 0.9867 0.9830 0.9948
0.0189 18.0 2016 0.0174 0.9793 0.9855 0.9824 0.9947
0.0188 19.0 2128 0.0170 0.9774 0.9848 0.9811 0.9946
0.0214 20.0 2240 0.0172 0.9781 0.9855 0.9818 0.9945

Framework versions

  • Transformers 4.51.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.6.0
  • Tokenizers 0.21.1

License

CC-BY-SA-4.0