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beto-finetuned-ner

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beto-finetuned-ner
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---
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: 0.8402527075812274
    - name: Recall
      type: recall
      value: 0.8556985294117647
    - name: F1
      type: f1
      value: 0.8479052823315117
    - name: Accuracy
      type: accuracy
      value: 0.9701834862385321
---

<!-- 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: 0.2248
- Precision: 0.8403
- Recall: 0.8557
- F1: 0.8479
- Accuracy: 0.9702

## Model description

Este modelo está basado en BETO, que es un modelo de lenguaje preentrenado para el español similar a BERT. BETO fue entrenado inicialmente en grandes cantidades de texto en español.
Posteriormente, este modelo toma la arquitectura y pesos preentrenados de BETO y los ajusta aún más en la tarea específica de Reconocimiento de Entidades Nombradas (NER) utilizando el conjunto de datos conll2002.
Este modelo ajustado puede usarse para anotar automáticamente nuevos textos en español, asignando etiquetas de entidad nombradas.

## How to Use

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

tokenizer = AutoTokenizer.from_pretrained("JoshuaAAX/beto-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("JoshuaAAX/beto-finetuned-ner")


text = "La Federación nacional de cafeteros de Colombia es una entidad del estado. El primer presidente el Dr Augusto Guerra contó con el aval de la Asociación Colombiana de Aviación."


ner_pipeline= pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="max")
ner_pipeline(text) 
``` 

## Training data

| Abbreviation  | Description   | 
|:-------------:|:-------------:|
| O             | Outside of NE | 
| PER           | Person’s name |
| ORG           | Organization  | 
| LOC           | Location      |
| MISC          | Miscellaneous | 



### 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 | 0.1549          | 0.8318    | 0.8442 | 0.8380 | 0.9688   |
| 0.0193        | 3.0   | 1563 | 0.1498          | 0.8513    | 0.8578 | 0.8545 | 0.9708   |
| 0.0148        | 4.0   | 2084 | 0.1810          | 0.8363    | 0.8442 | 0.8403 | 0.9682   |
| 0.0112        | 5.0   | 2605 | 0.1904          | 0.8412    | 0.8529 | 0.8470 | 0.9703   |
| 0.0078        | 6.0   | 3126 | 0.1831          | 0.8364    | 0.8539 | 0.8450 | 0.9708   |
| 0.0058        | 7.0   | 3647 | 0.2060          | 0.8419    | 0.8543 | 0.8481 | 0.9701   |
| 0.0049        | 8.0   | 4168 | 0.2111          | 0.8357    | 0.8541 | 0.8448 | 0.9697   |
| 0.0037        | 9.0   | 4689 | 0.2255          | 0.8371    | 0.8504 | 0.8437 | 0.9692   |
| 0.0031        | 10.0  | 5210 | 0.2248          | 0.8403    | 0.8557 | 0.8479 | 0.9702   |


### Framework versions

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