BERT Named Entity Recognition 🤖 Using 🤗 Hugging Face

This repository contains a fine-tuned BERT model specifically designed for Named Entity Recognition (NER) tasks. The model is capable of identifying and classifying entities in text into predefined categories such as Person, Organization, Location, and Miscellaneous.

Features

• Fine-Tuned Model: BERT model fine-tuned on a specific dataset for improved NER accuracy.
• Easy Integration: Simple APIs for loading the model and making predictions on new text.
• Custom Dataset Support: Allows for further fine-tuning with your own dataset.

Technologies Used

• Transformers: The Hugging Face library for natural language processing.
• PyTorch: Deep learning framework used for training the model.
• Pandas: For handling datasets and data manipulation.
• NumPy: For numerical computations.

How to Use

Download the ipynb file and the use conda to make a local session or upload the file in google drive and then use it using google colab

Using the Fine-Tuned Model

After fine-tuning, you can load the model and make predictions as follows:

from transformers import AutoModelForTokenClassification, AutoTokenizer

# Load the model and tokenizer
model = AutoModelForTokenClassification.from_pretrained("path/to/your/model")
tokenizer = AutoTokenizer.from_pretrained("path/to/your/config.json")

# Example usage
text = "[Name] works at [Org] in [Loc]."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

Usage

Fine-Tune the Model: Run the provided script to fine-tune the BERT model with your dataset. Load the Model: Use the provided examples to load the fine-tuned model and make predictions. Analyze Output: The model will output the predicted entities for given input text.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Hugging Face: For providing the Transformers library.

PyTorch: For being a powerful framework for deep learning.

hugging face/conll2003 : For providing the dataset used for fine-tuning.