transformers tensorboard safetensors bert token-classification es dataset:eriktks/conll2002 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
datasets:
- eriktks/conll2002
language:
- es
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
pipeline_tag: token-classification
---

# Model Name: bert-finetuned-ner-1

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)

# 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 | 0.1735| 0.1508 | 0.6577 | 0.7323 | 0.6930 | 0.9586 |
| 2 | 0.0770| 0.1421 | 0.6876 | 0.7702 | 0.7266 | 0.9629 |
| 3 | 0.0504| 0.1373 | 0.7353 | 0.7845 | 0.7591 | 0.9663 |
| 4 | 0.0358| 0.1442 | 0.7453 | 0.7902 | 0.7671 | 0.9664 |
| 5 | 0.0272| 0.1536 | 0.7527 | 0.7946 | 0.7731 | 0.9667 |


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

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