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 BETO 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/Seb00927/NER-finetuned-BETO
Github repository: https://github.com/paulrojasg/nlp_4th_workshop
Training
Training Details
- Epochs: 10
- 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 | 0.0104 | 0.1915 | 0.8359 | 0.8568 | 0.8462 | 0.9701 |
| 2 | 0.0101 | 0.2187 | 0.8226 | 0.8387 | 0.8306 | 0.9676 |
| 3 | 0.0066 | 0.2085 | 0.8551 | 0.8637 | 0.8594 | 0.9699 |
| 4 | 0.0069 | 0.2139 | 0.8342 | 0.8431 | 0.8386 | 0.9698 |
| 5 | 0.0070 | 0.2110 | 0.8480 | 0.8536 | 0.8508 | 0.9708 |
| 6 | 0.0060 | 0.2214 | 0.8378 | 0.8497 | 0.8437 | 0.9703 |
| 7 | 0.0042 | 0.2284 | 0.8437 | 0.8596 | 0.8516 | 0.9704 |
| 8 | 0.0034 | 0.2344 | 0.8417 | 0.8566 | 0.8491 | 0.9702 |
| 9 | 0.0026 | 0.2385 | 0.8400 | 0.8580 | 0.8489 | 0.9698 |
| 10 | 0.0023 | 0.2412 | 0.8460 | 0.8610 | 0.8534 | 0.9704 |
Authors
Made by:
- Paul Rodrigo Rojas Guerrero
- Jose Luis Hincapie Bucheli
- Sebastián Idrobo Avirama
With help from: