AfroXLMR NER

Introduction

AfroXLMR NER is a Python application that allows you to extract named entities from text or PDF documents. It use masakhane/afroxlmr-large-ner-masakhaner-1.0_2.0 Named Entity Recognition (NER) model for 21 African languages: Amharic (Amharic) Bambara (bam) Ghomala (bbj) Ewe (ewe) Fon (fon) Hausa (hau) Igbo (ibo) Kinyarwanda (kin) Luganda (lug) Dholuo (luo) -Mossi (mos) Chichewa (nya) Nigerian Pidgin chShona (sna) Kiswahili (swą) Setswana (tsn) Twi (twi) Wolof (wol) isiXhosa (xho) Yorùbá (yor) isiZulu (zul)

Getting Started

Running on Google Cloud

To test the application on Google Cloud, follow these steps:

1. Ensure you have Python 3 installed.
2. Run the following command: python3 app.py

Running Locally

To run the application on your local machine, you'll need to install the required dependencies first. Make sure you have Python 3 and pip installed. Then, follow these steps:

1. Install the required packages using pip:pip install -r requirements.txt

2. Run the application with the following command: python3 app.py

Usage

AfroXLMR NERallows you to input text or PDF documents and extract named entities from the content. You can use it for various natural language processing tasks that involve recognizing and categorizing entities such as names of people, organizations, locations, and more.

Authors

1. Alim IDRISSOU
2. Alain Ogou

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these guidelines:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and ensure the code is well-documented.
4. Submit a pull request for review.