---
license: cc-by-4.0
base_model: NazaGara/NER-fine-tuned-BETO
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: NER-finetuned-BETO
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2002
      type: conll2002
      config: es
      split: validation
      args: es
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9697960575254153
    - name: F1
      type: f1
      value: 0.9693514387921158
    - name: Precision
      type: precision
      value: 0.9691715895096829
    - name: Recall
      type: recall
      value: 0.9697960575254153
---

<!-- 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-finetuned-BETO

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: 0.1898
- Accuracy: 0.9698
- F1: 0.9694
- Precision: 0.9692
- Recall: 0.9698

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0509        | 1.0   | 521  | 0.1309          | 0.9700   | 0.9696 | 0.9698    | 0.9700 |
| 0.0292        | 2.0   | 1042 | 0.1618          | 0.9679   | 0.9673 | 0.9670    | 0.9679 |
| 0.0178        | 3.0   | 1563 | 0.1460          | 0.9718   | 0.9712 | 0.9709    | 0.9718 |
| 0.0141        | 4.0   | 2084 | 0.1775          | 0.9689   | 0.9682 | 0.9680    | 0.9689 |
| 0.0091        | 5.0   | 2605 | 0.1815          | 0.9700   | 0.9695 | 0.9693    | 0.9700 |
| 0.007         | 6.0   | 3126 | 0.1898          | 0.9698   | 0.9694 | 0.9692    | 0.9698 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1