Named Entity Recognition for Azerbaijani Language

A custom Named Entity Recognition (NER) model specifically designed for the Azerbaijani language. This project includes a FastAPI application for model deployment and a user-friendly frontend interface for testing and visualizing NER results.

Demo

Try the live demo: Named Entity Recognition Demo

Note: The server runs on a free tier and may take 1-2 minutes to initialize if inactive. Please be patient during startup.

Project Structure

.
├── Dockerfile                # Docker image configuration
├── README.md                # Project documentation
├── fly.toml                 # Fly.io deployment configuration
├── main.py                  # FastAPI application entry point
├── models/                  # Model-related files
│   ├── NER_from_scratch.ipynb    # Custom NER implementation notebook
│   ├── README.md                 # Models documentation
│   ├── XLM-RoBERTa.ipynb        # XLM-RoBERTa training notebook
│   ├── azeri-turkish-bert-ner.ipynb  # Azeri-Turkish BERT training
│   ├── mBERT.ipynb              # mBERT training notebook
│   ├── push_to_HF.py            # Hugging Face upload script
│   ├── train-00000-of-00001.parquet  # Training data
│   └── xlm_roberta_large.ipynb  # XLM-RoBERTa Large training
├── requirements.txt         # Python dependencies
├── static/                  # Frontend assets
│   ├── app.js               # Frontend logic
│   └── style.css            # UI styling
└── templates/               # HTML templates
    └── index.html           # Main UI template

Models & Dataset

Available Models

• mBERT Azerbaijani NER
• XLM-RoBERTa Azerbaijani NER
• XLM-RoBERTa Large Azerbaijani NER
• Azerbaijani-Turkish BERT Base NER

Dataset

• Azerbaijani NER Dataset

Note: All models were fine-tuned on an A100 GPU using Google Colab Pro+. The XLM-RoBERTa base model is currently deployed in production.

Model Performance

mBERT Performance

Epoch	Training Loss	Validation Loss	Precision	Recall	F1	Accuracy
1	0.2952	0.2657	0.7154	0.6229	0.6659	0.9191
2	0.2486	0.2521	0.7210	0.6380	0.6770	0.9214
3	0.2068	0.2534	0.7049	0.6507	0.6767	0.9209

XLM-RoBERTa Base Performance

Epoch	Training Loss	Validation Loss	Precision	Recall	F1
1	0.3231	0.2755	0.7758	0.6949	0.7331
3	0.2486	0.2525	0.7515	0.7412	0.7463
5	0.2238	0.2522	0.7644	0.7405	0.7522
7	0.2097	0.2507	0.7607	0.7394	0.7499

XLM-RoBERTa Large Performance

Epoch	Training Loss	Validation Loss	Precision	Recall	F1
1	0.4075	0.2538	0.7689	0.7214	0.7444
3	0.2144	0.2488	0.7509	0.7489	0.7499
6	0.1526	0.2881	0.7831	0.7284	0.7548
9	0.1194	0.3316	0.7393	0.7495	0.7444

Azeri-Turkish-BERT Performance

Epoch	Training Loss	Validation Loss	Precision	Recall	F1
1	0.4331	0.3067	0.7390	0.6933	0.7154
3	0.2506	0.2751	0.7583	0.7094	0.7330
6	0.1992	0.2861	0.7551	0.7170	0.7355
9	0.1717	0.3138	0.7431	0.7255	0.7342

Setup Instructions

Local Development

1. Clone the repository

git clone https://github.com/Ismat-Samadov/Named_Entity_Recognition.git
cd Named_Entity_Recognition

2. Set up Python environment

# Create virtual environment
python -m venv .venv

# Activate virtual environment
# On Unix/macOS:
source .venv/bin/activate
# On Windows:
.venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

3. Run the application

uvicorn main:app --host 0.0.0.0 --port 8080

Fly.io Deployment

1. Install Fly CLI

# On Unix/macOS
curl -L https://fly.io/install.sh | sh

2. Configure deployment

# Login to Fly.io
fly auth login

# Initialize app
fly launch

# Configure memory (minimum 2GB recommended)
fly scale memory 2048

3. Deploy application

fly deploy

# Monitor deployment
fly logs

Usage

1. Access the application:

   • Local: http://localhost:8080
   • Production: https://named-entity-recognition.fly.dev

2. Enter Azerbaijani text in the input field

3. Click "Process" to view the named entities

4. Results will display recognized entities highlighted in different colors

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is open source and available under the MIT License.