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:

• Raúl Ernesto Gutiérrez