---
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
  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: 0.8412017167381974
    - name: Recall
      type: recall
      value: 0.8556985294117647
    - name: F1
      type: f1
      value: 0.8483881991115162
    - name: Accuracy
      type: accuracy
      value: 0.9705709149516489
---

<!-- 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

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: 0.2190
- Precision: 0.8412
- Recall: 0.8557
- F1: 0.8484
- Accuracy: 0.9706

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0512        | 1.0   | 521  | 0.1314          | 0.8328    | 0.8562 | 0.8443 | 0.9703   |
| 0.0305        | 2.0   | 1042 | 0.1553          | 0.8340    | 0.8451 | 0.8395 | 0.9688   |
| 0.0195        | 3.0   | 1563 | 0.1462          | 0.8483    | 0.8568 | 0.8525 | 0.9710   |
| 0.0148        | 4.0   | 2084 | 0.1809          | 0.8395    | 0.8460 | 0.8428 | 0.9683   |
| 0.0112        | 5.0   | 2605 | 0.1889          | 0.8394    | 0.8516 | 0.8454 | 0.9701   |
| 0.0079        | 6.0   | 3126 | 0.1815          | 0.8431    | 0.8571 | 0.8500 | 0.9707   |
| 0.0062        | 7.0   | 3647 | 0.2037          | 0.8410    | 0.8571 | 0.8490 | 0.9704   |
| 0.0049        | 8.0   | 4168 | 0.2065          | 0.84      | 0.8541 | 0.8470 | 0.9706   |
| 0.0038        | 9.0   | 4689 | 0.2189          | 0.8434    | 0.8539 | 0.8486 | 0.9697   |
| 0.0032        | 10.0  | 5210 | 0.2190          | 0.8412    | 0.8557 | 0.8484 | 0.9706   |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1