---
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
  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: 0.8416742493175614
    - name: Recall
      type: recall
      value: 0.8501838235294118
    - name: F1
      type: f1
      value: 0.8459076360310929
    - name: Accuracy
      type: accuracy
      value: 0.967827919662782
---

<!-- 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

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: 0.2653
- Precision: 0.8417
- Recall: 0.8502
- F1: 0.8459
- Accuracy: 0.9678

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0507        | 1.0   | 1041  | 0.1448          | 0.8298    | 0.8571 | 0.8432 | 0.9691   |
| 0.0333        | 2.0   | 2082  | 0.1728          | 0.8259    | 0.8481 | 0.8369 | 0.9678   |
| 0.0195        | 3.0   | 3123  | 0.1722          | 0.8392    | 0.8516 | 0.8453 | 0.9693   |
| 0.0147        | 4.0   | 4164  | 0.2037          | 0.8502    | 0.8488 | 0.8495 | 0.9679   |
| 0.011         | 5.0   | 5205  | 0.2041          | 0.8394    | 0.8529 | 0.8461 | 0.9695   |
| 0.0082        | 6.0   | 6246  | 0.2418          | 0.8410    | 0.8401 | 0.8406 | 0.9664   |
| 0.006         | 7.0   | 7287  | 0.2323          | 0.8448    | 0.8552 | 0.8500 | 0.9678   |
| 0.0046        | 8.0   | 8328  | 0.2415          | 0.8411    | 0.8527 | 0.8469 | 0.9691   |
| 0.003         | 9.0   | 9369  | 0.2502          | 0.8402    | 0.8495 | 0.8448 | 0.9677   |
| 0.0022        | 10.0  | 10410 | 0.2653          | 0.8417    | 0.8502 | 0.8459 | 0.9678   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1