This project is designed to generate synthetic data for fine-tuning a Named Entity Recognition (NER) model using SpaCy. The synthetic data is generated based on a specific template that includes a company name, which is randomly generated. The generated data is then used to train and evaluate a SpaCy NER model to recognize company names within text.

To run this project, you need to have Python installed on your system. Additionally, you will need to install the following Python packages:

• spacy
• pandas
• numpy

You can install these packages using pip:

pip install spacy pandas numpy
python -m spacy download en_core_web_sm

Generate Synthetic Data: Run the script to generate synthetic data. This will create a CSV file named synthetic_data.csv containing the synthetic data.

Train the Model: Use the generated synthetic data to train a SpaCy NER model. The training script will update the model to recognize company names within text.

Evaluate the Model: After training, evaluate the model's performance on a test set to measure its accuracy in recognizing company names.

Contributions to this project are welcome. To contribute, please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Make your changes
4. Commit your changes (git commit -am 'Add new feature')
5. Push to the branch (git push origin feature-branch)
6. Create a new Pull Request

This project is licensed under the MIT License.