---
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: 0.8614471581830633
    - name: Recall
      type: recall
      value: 0.8671875
    - name: F1
      type: f1
      value: 0.8643077980075576
    - name: Accuracy
      type: accuracy
      value: 0.9790072369291234
---

<!-- 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: 0.1659
- Precision: 0.8614
- Recall: 0.8672
- F1: 0.8643
- Accuracy: 0.9790

## 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-06
- 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: 16

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0272        | 1.0   | 1041  | 0.1062          | 0.8549    | 0.8637 | 0.8593 | 0.9786   |
| 0.0208        | 2.0   | 2082  | 0.1127          | 0.8443    | 0.8596 | 0.8519 | 0.9782   |
| 0.0158        | 3.0   | 3123  | 0.1195          | 0.8545    | 0.8598 | 0.8572 | 0.9787   |
| 0.0129        | 4.0   | 4164  | 0.1332          | 0.8629    | 0.8589 | 0.8609 | 0.9782   |
| 0.0107        | 5.0   | 5205  | 0.1299          | 0.8555    | 0.8635 | 0.8595 | 0.9786   |
| 0.0087        | 6.0   | 6246  | 0.1486          | 0.8564    | 0.8564 | 0.8564 | 0.9782   |
| 0.0085        | 7.0   | 7287  | 0.1583          | 0.8618    | 0.8596 | 0.8607 | 0.9783   |
| 0.0066        | 8.0   | 8328  | 0.1582          | 0.8604    | 0.8580 | 0.8592 | 0.9783   |
| 0.0052        | 9.0   | 9369  | 0.1571          | 0.8554    | 0.8566 | 0.8560 | 0.9781   |
| 0.0054        | 10.0  | 10410 | 0.1604          | 0.8628    | 0.8640 | 0.8634 | 0.9787   |
| 0.004         | 11.0  | 11451 | 0.1584          | 0.8624    | 0.8670 | 0.8647 | 0.9791   |
| 0.0036        | 12.0  | 12492 | 0.1633          | 0.8603    | 0.8658 | 0.8630 | 0.9786   |
| 0.0036        | 13.0  | 13533 | 0.1620          | 0.8628    | 0.8658 | 0.8643 | 0.9790   |
| 0.0032        | 14.0  | 14574 | 0.1645          | 0.8617    | 0.8676 | 0.8647 | 0.9793   |
| 0.0028        | 15.0  | 15615 | 0.1645          | 0.8604    | 0.8670 | 0.8637 | 0.9792   |
| 0.003         | 16.0  | 16656 | 0.1659          | 0.8614    | 0.8672 | 0.8643 | 0.9790   |


### Framework versions

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