---
library_name: transformers
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-PRO
  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: 0.8403323602066023
    - name: Recall
      type: recall
      value: 0.8598345588235294
    - name: F1
      type: f1
      value: 0.8499716070414537
    - name: Accuracy
      type: accuracy
      value: 0.9712682866352591
---

<!-- 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-PRO

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: 0.1541
- Precision: 0.8403
- Recall: 0.8598
- F1: 0.8500
- Accuracy: 0.9713

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0458        | 1.0   | 1041 | 0.1475          | 0.8485    | 0.8587 | 0.8536 | 0.9708   |
| 0.0229        | 2.0   | 2082 | 0.1541          | 0.8403    | 0.8598 | 0.8500 | 0.9713   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cpu
- Datasets 3.1.0
- Tokenizers 0.20.3