---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# breast-cancer-biomedical-ner-sp

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the breast cancer dataset in spanish: [anvorja/breast_cancer_es_dataset](https://huggingface.co/datasets/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