Welcome to the NER_LOC_Detection repository! This project demonstrates how to build a Named Entity Recognition (NER) model using spaCy, a powerful NLP library in Python, to detect and classify location entities (LOC) in text. This repository contains all the necessary code and instructions to train, test, and deploy a custom NER model that identifies locations in a variety of text formats.
Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP) that involves identifying and classifying key information (entities) in text into predefined categories, such as names, organizations, locations, etc. This repository focuses on building an NER model to detect and categorize location entities (LOC) in text data. The model uses spaCy, a popular NLP library, and leverages custom training data to improve accuracy in identifying locations.
- Custom NER model for detecting locations in text using spaCy.
- A Jupyter Notebook with all the steps for training, testing, and visualizing the model.
- Instructions for preparing your own dataset.
- Sample code for testing and visualizing the model's predictions.