---
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: beto-finetuned-ner-cfv
  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: 0.865948670944088
    - name: Recall
      type: recall
      value: 0.8683363970588235
    - name: F1
      type: f1
      value: 0.867140890316659
    - name: Accuracy
      type: accuracy
      value: 0.9792528768210419
---

<!-- 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. -->

# beto-finetuned-ner-cfv

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: 0.1667
- Precision: 0.8659
- Recall: 0.8683
- F1: 0.8671
- Accuracy: 0.9793

## 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: 4e-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: 11

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0373        | 1.0   | 521  | 0.1002          | 0.8642    | 0.8568 | 0.8605 | 0.9779   |
| 0.0255        | 2.0   | 1042 | 0.1018          | 0.8410    | 0.8555 | 0.8482 | 0.9779   |
| 0.0147        | 3.0   | 1563 | 0.1093          | 0.8654    | 0.8626 | 0.8640 | 0.9789   |
| 0.0107        | 4.0   | 2084 | 0.1277          | 0.8772    | 0.8614 | 0.8692 | 0.9787   |
| 0.0069        | 5.0   | 2605 | 0.1422          | 0.8496    | 0.8529 | 0.8513 | 0.9782   |
| 0.0052        | 6.0   | 3126 | 0.1436          | 0.8511    | 0.8511 | 0.8511 | 0.9775   |
| 0.0039        | 7.0   | 3647 | 0.1515          | 0.8663    | 0.8621 | 0.8642 | 0.9784   |
| 0.0029        | 8.0   | 4168 | 0.1525          | 0.8585    | 0.8617 | 0.8601 | 0.9785   |
| 0.0024        | 9.0   | 4689 | 0.1549          | 0.8635    | 0.8633 | 0.8634 | 0.9784   |
| 0.0021        | 10.0  | 5210 | 0.1643          | 0.8660    | 0.8672 | 0.8666 | 0.9792   |
| 0.0017        | 11.0  | 5731 | 0.1667          | 0.8659    | 0.8683 | 0.8671 | 0.9793   |


### Framework versions

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