---
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-base-cased-finetuned-conll2002
  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: 0.8597014925373134
    - name: Recall
      type: recall
      value: 0.8602941176470589
    - name: F1
      type: f1
      value: 0.8599977029975882
    - name: Accuracy
      type: accuracy
      value: 0.978761597037205
---

<!-- 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-base-cased-finetuned-conll2002

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: 0.1259
- Precision: 0.8597
- Recall: 0.8603
- F1: 0.8600
- Accuracy: 0.9788

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0154        | 1.0   | 1041 | 0.1064          | 0.8523    | 0.8603 | 0.8563 | 0.9782   |
| 0.0162        | 2.0   | 2082 | 0.1060          | 0.8556    | 0.8635 | 0.8596 | 0.9790   |
| 0.0161        | 3.0   | 3123 | 0.1259          | 0.8597    | 0.8603 | 0.8600 | 0.9788   |


### Framework versions

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