/named-entity-recognition-bert-base-ner

Find entities using the `dslim/bert-base-NER` model under the hood!

Primary LanguageJupyter NotebookMIT LicenseMIT

Named Entity Recognition with BERT-base-NER

Welcome to the Named Entity Recognition with BERT-base-NER project! This repository focuses on utilizing the dslim/bert-base-NER model to perform Named Entity Recognition (NER) tasks.

Introduction

Named Entity Recognition (NER) is a natural language processing task where the goal is to identify and classify named entities mentioned in unstructured text into predefined categories such as person names, organizations, locations, dates, etc. BERT-base-NER, based on the powerful BERT architecture, offers state-of-the-art performance in NER tasks.

Getting Started

Environment Setup

To get started, create a virtual environment and activate it:

virtualenv venv
source venv/bin/activate

Install Dependencies

Next, install the required dependencies using pip:

pip install -r requirements.txt

Run the Application

Now, you can run the application:

gradio app.py

This will start the application, allowing you to input text and the DistilBART-CNN model can now classify named entities.

Usage

Simply input the text you want into the application's interface. The model will then process the input and provide a named entity of the text.

Additional Resources

  • Check out the model on Hugging Face Spaces
  • Check out the Colab notebook for this project on Open in Colab
  • Explore more about the BERT-base-NER model here

Feel free to reach out if you have any questions or feedback!