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 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/NER-finetuned-BETO
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 | 0.0178 | 0.1665 | 0.8275 | 0.8509 | 0.8390 | 0.9706 |
| 2 | 0.0144 | 0.1737 | 0.8355 | 0.8495 | 0.8424 | 0.9689 |
| 3 | 0.0121 | 0.1754 | 0.8432 | 0.8612 | 0.8521 | 0.9715 |
| 4 | 0.0085 | 0.1986 | 0.8352 | 0.8527 | 0.8439 | 0.9701 |
| 5 | 0.0060 | 0.2106 | 0.8390 | 0.8536 | 0.8462 | 0.9696 |
Authors
Made by:
- Paul Rodrigo Rojas Guerrero
- Jose Luis Hincapie Bucheli
- Sebastián Idrobo Avirama
With help from: