metadata
language:
- es
license: apache-2.0
datasets:
- eriktks/conll2002
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 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: