Wothmag07/NER-fine-tuned-model

Model Card for Model ID

Fine-tuned BERT for Named Entity Recognition (NER)

Model Details

Model Description

This model is a fine-tuned version of google-bert/bert-base-uncased for Named Entity Recognition (NER) using the CoNLL-2003 dataset. The model classifies tokens into named entity categories such as persons, locations, organizations, and miscellaneous entities.

• Developed by: Gowtham Arulmozhi
• Language(s) (NLP): English (en)
• License: MIT
• Finetuned from model : google-bert/bert-base-uncased

How to Get Started with the Model

Run this snippet to use the model with 🤗 Transformers:

from transformers import pipeline

ner_pipeline = pipeline("ner", model="Wothmag07/NER-fine-tuned-model")
text = "Barack Obama was born in Hawaii."
results = ner_pipeline(text)
print(results)

Training Details

Training Data

The model was trained using the CoNLL-2003 dataset, which contains news articles annotated for named entities.

Training Hyperparameters

• Batch size: 16

• Learning rate: 2e-5

• Epochs: 20

• Optimizer: Adam

Evaluation

Tested on CoNLL-2003 test set with:

• Accuracy: 0.987084

• F1-score: 0.947727

• Precision: 0.945248

• Recall: 0.950218