README.md

metadata

title: BERT NER App
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.49.0
app_file: app.py
pinned: false

Fine-Tuned BERT for Named Entity Recognition (NER)

This project contains a complete end-to-end workflow for fine-tuning a bert-base-cased model for Named Entity Recognition (NER) on the CoNLL-2003 dataset. The fine-tuned model is served via an interactive web application built with Streamlit.

The application identifies four types of named entities in user-provided text:

• PER: Person
• ORG: Organization
• LOC: Location
• MISC: Miscellaneous

🚀 Live Demo

A live version of this application is deployed on Streamlit Community Cloud.

➡️ Access the Live Application Here <!-- Replace with your actual Streamlit URL -->

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📸 Application Screenshot

(It is highly recommended to add a screenshot or GIF of your application in action here. This significantly improves the presentation of your project.)

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✨ Features

• Interactive UI: A clean and simple user interface powered by Streamlit.
• Real-time NER: Input any text and get immediate NER predictions.
• Entity Highlighting: Identified entities are highlighted with color-coded labels directly in the input text for easy visualization.
• Extracted Entity List: A clear list of all recognized entities and their types is displayed below the analysis.
• Fine-Tuned Model: Utilizes a BERT model specifically fine-tuned on a standard NER dataset for high accuracy.

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🛠️ Technologies Used

• Backend & Modeling: Python, PyTorch
• NLP Framework: Hugging Face (transformers, datasets, accelerate)
• Web Framework: Streamlit
• Dataset: CoNLL-2003
• Base Model: bert-base-cased

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⚙️ Setup and Local Installation

To run this project on your local machine, please follow these steps:

Prerequisites

• Python 3.8+
• Git and Git LFS (Git LFS Installation Guide)

Installation Steps

1. Clone the repository: Open your terminal and clone the repository, then navigate into the project directory.

   git clone https://github.com/780Noman/NER_Using_BERT.git
   cd NER_Using_BERT
   

2. Set up a virtual environment (Recommended):

   # Create a virtual environment
   python -m venv venv
   
   # Activate it
   # On Windows:
   .\venv\Scripts\activate
   # On macOS/Linux:
   source venv/bin/activate
   

3. Install dependencies: The required packages are listed in requirements.txt.

   pip install -r requirements.txt
   

   Note: If you encounter issues with the large model file, ensure Git LFS is installed and pull the LFS files.

   git lfs pull
   

4. Run the Streamlit Application:

   streamlit run app.py
   

   Your web browser should open with the application running locally at http://localhost:8501.

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🚢 Deployment

This application is deployed on Streamlit Community Cloud. The deployment process leverages Git LFS to handle the large model file (model.safetensors). The Streamlit Cloud platform automatically detects the requirements.txt file and installs all necessary dependencies to run the application.

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