---
library_name: transformers
license: cc-by-4.0
base_model: NazaGara/NER-fine-tuned-BETO
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER-finetuning-BETO-actualizado
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2002
      type: conll2002
      config: es
      split: validation
      args: es
    metrics:
    - name: Precision
      type: precision
      value: 0.8385333636984742
    - name: Recall
      type: recall
      value: 0.8460477941176471
    - name: F1
      type: f1
      value: 0.8422738190552442
    - name: Accuracy
      type: accuracy
      value: 0.9687422514257377
---

<!-- 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. -->

# NER-finetuning-BETO-actualizado

This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2642
- Precision: 0.8385
- Recall: 0.8460
- F1: 0.8423
- Accuracy: 0.9687

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0778        | 1.0   | 2081  | 0.1603          | 0.8164    | 0.8277 | 0.8220 | 0.9676   |
| 0.0608        | 2.0   | 4162  | 0.1756          | 0.8114    | 0.8136 | 0.8125 | 0.9640   |
| 0.0362        | 3.0   | 6243  | 0.1986          | 0.8236    | 0.8226 | 0.8231 | 0.9650   |
| 0.0277        | 4.0   | 8324  | 0.1904          | 0.8092    | 0.8359 | 0.8223 | 0.9665   |
| 0.0217        | 5.0   | 10405 | 0.1988          | 0.8137    | 0.8389 | 0.8261 | 0.9658   |
| 0.0146        | 6.0   | 12486 | 0.2298          | 0.8470    | 0.8536 | 0.8503 | 0.9684   |
| 0.0091        | 7.0   | 14567 | 0.2509          | 0.8315    | 0.8412 | 0.8363 | 0.9665   |
| 0.0058        | 8.0   | 16648 | 0.2375          | 0.8274    | 0.8435 | 0.8354 | 0.9688   |
| 0.0033        | 9.0   | 18729 | 0.2489          | 0.8349    | 0.8421 | 0.8385 | 0.9683   |
| 0.0023        | 10.0  | 20810 | 0.2642          | 0.8385    | 0.8460 | 0.8423 | 0.9687   |


### Framework versions

- Transformers 4.51.2
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1