NER-finetuned-BETO

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: 10
• Learning Rate: 2e-05
• Weight Decay: 0.01
• Batch Size (Train): 16
• Batch Size (Eval): 8

Training Metrics

Authors

Made by:

• Paul Rodrigo Rojas Guerrero
• Jose Luis Hincapie Bucheli
• Sebastián Idrobo Avirama

With help from:

• Raúl Ernesto Gutiérrez