Custom NER Model for Key:Value Pairs using spaCy Fine-Tuning

This repository contains Jupyter Notebooks for fine-tuning a spaCy NER model to achieve high accuracy in recognizing Key:Value pairs in text.

Overview

Named Entity Recognition (NER) is a crucial task in natural language processing (NLP), and this project focuses on training a spaCy model specifically for Key:Value pairs extraction.

Key Features

• spaCy Integration: Utilizes spaCy for training and evaluation.
• High Accuracy: Achieves an accuracy of 97.50%
• No License: The code is provided without any licensing restrictions.

Getting Started

1. Open and run the Jupyter Notebooks in the order specified.

2. Customize as needed: Feel free to modify the notebooks or tweak hyperparameters for your specific task.

Results

The model achieves an accuracy of 97.50% on the evaluation set, showcasing its effectiveness in recognizing Key:Value pairs.

Contact

For any questions or issues, please feel free to contact us...