---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-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: 0.7640546993705232
    - name: Recall
      type: recall
      value: 0.8088235294117647
    - name: F1
      type: f1
      value: 0.7858019868288871
    - name: Accuracy
      type: accuracy
      value: 0.9676902769959431
---

<!-- 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. -->

# bert-finetuned-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1912
- Precision: 0.7641
- Recall: 0.8088
- F1: 0.7858
- Accuracy: 0.9677

## Model description

El modelo base bert-base-cased es una versión pre-entrenada del popular modelo de lenguaje BERT de Google. Inicialmente fue entrenado en grandes cantidades de texto para aprender representaciones densas de palabras y secuencias.
Posteriormente, este modelo toma la arquitectura y pesos pre-entrenados de bert-base-cased y los ajusta aún más en la tarea específica de Reconocimiento de Entidades Nombradas (NER por sus siglas en inglés) utilizando el conjunto de datos conll2002.

## How to Use

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

tokenizer = AutoTokenizer.from_pretrained("JoshuaAAX/bert-finetuned-ner")
model = AutoModelForTokenClassification.from_pretrained("JoshuaAAX/bert-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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1713        | 1.0   | 521  | 0.1404          | 0.6859    | 0.7387 | 0.7114 | 0.9599   |
| 0.0761        | 2.0   | 1042 | 0.1404          | 0.6822    | 0.7693 | 0.7231 | 0.9623   |
| 0.05          | 3.0   | 1563 | 0.1304          | 0.7488    | 0.7937 | 0.7706 | 0.9672   |
| 0.0355        | 4.0   | 2084 | 0.1454          | 0.7585    | 0.7960 | 0.7768 | 0.9664   |
| 0.0253        | 5.0   | 2605 | 0.1501          | 0.7549    | 0.8095 | 0.7812 | 0.9677   |
| 0.0184        | 6.0   | 3126 | 0.1726          | 0.7581    | 0.7992 | 0.7781 | 0.9662   |
| 0.0138        | 7.0   | 3647 | 0.1743          | 0.7524    | 0.8042 | 0.7774 | 0.9676   |
| 0.0112        | 8.0   | 4168 | 0.1853          | 0.7576    | 0.8022 | 0.7792 | 0.9674   |
| 0.0082        | 9.0   | 4689 | 0.1914          | 0.7595    | 0.8061 | 0.7821 | 0.9667   |
| 0.0073        | 10.0  | 5210 | 0.1912          | 0.7641    | 0.8088 | 0.7858 | 0.9677   |


### Framework versions

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