---
language:
- es
license: apache-2.0
datasets:
- eriktks/conll2002
metrics:
- precision
- recall
- f1
- accuracy
pipeline_tag: token-classification
---

# Model Name: NER-finetuned-BETO

This is a BERT model fine-tuned for Named Entity Recognition (NER).

# Model Description

This is a fine-tuned BERT model for Named Entity Recognition (NER) task using CONLL2002 dataset.

In the first part, the dataset must be pre-processed in order to give it to the model. This is done using the 🤗 Transformers and BERT tokenizers. Once this is done, finetuning is applied from *[bert-base-cased](https://huggingface.co/google-bert/bert-base-cased)* and using the 🤗 *AutoModelForTokenClassification*.

Finally, the model is trained obtaining the neccesary metrics for evaluating its performance (Precision, Recall, F1 and Accuracy)

Summary of executed tests can be found in: https://docs.google.com/spreadsheets/d/1lI7skNIvRurwq3LA5ps7JFK5TxToEx4s7Kaah3ezyQc/edit?usp=sharing

Model can be found in: https://huggingface.co/paulrojasg/bert-finetuned-ner-1

Github repository: https://github.com/paulrojasg/nlp_4th_workshop

# Training

## Training Details

- Epochs: 5
- Learning Rate: 2e-05
- Weight Decay: 0.01
- Batch Size (Train): 16
- Batch Size (Eval): 8

## Training Metrics

| Epoch | Training Loss | Validation Loss | Precision | Recall | F1 Score | Accuracy |
|:----:|:-------------:|:---------------:|:---------:|:------:|:--------:|:--------:|
| 1 | 0.0507| 0.1354 | 0.8310 | 0.8518 | 0.8413 | 0.9700 |
| 2 | 0.0292| 0.1598 | 0.8331 | 0.8433 | 0.8382 | 0.9684 |
| 3 | 0.0172| 0.1565 | 0.8392 | 0.8550 | 0.8470 | 0.9705 |
| 4 | 0.0136| 0.1812 | 0.8456 | 0.8534 | 0.8495 | 0.9698 |
| 5 | 0.0088| 0.1861 | 0.8395 | 0.8543 | 0.8468 | 0.9699 |


# Authors
Made by:
- Paul Rodrigo Rojas Guerrero
- Jose Luis Hincapie Bucheli
- Sebastián Idrobo Avirama

With help from:
- [Raúl Ernesto Gutiérrez](https://huggingface.co/raulgdp)