Skip to content

Latest commit

 

History

History
52 lines (39 loc) · 2.18 KB

README.md

File metadata and controls

52 lines (39 loc) · 2.18 KB
Raw

AutoQAPairGen

AutoQAPairGen is a system designed to simplify the process of preparing data for fine-tuning machine learning models. It generates question-answer pairs from unstructured data (such as PDFs) based on user-defined schemas. This tool allows you to convert unstructured data into a structured format that aligns with your schema, making it easier to prepare data for training or fine-tuning both language and vision models.

Read more about the project here: blog

Features

  • Generate question-answer pairs from unstructured data (including PDFs) based on custom schemas.
  • Capable of chunking data without losing continuity, ensuring seamless processing of large or complex documents.
  • Handles embedded images and includes them in the corresponding data chunks for processing.
  • Automatically generates structured question-answer pairs in line with user-defined schemas.
  • Fully open-source, customizable, and easily extendable for specific use cases.

Prerequisites

  • GPU support: since we are using the open source model, we need to have a GPU to run the model.
  • Python 3.10 or higher.

Installation

To get started, follow these steps:

1. Clone the repository

git clone https://github.com/ragesh2000/AutoQAPairGen.git
cd AutoQAPairGen

2. Create a virtual environment

conda create -n qa-gen python=3.10
conda activate qa-gen

3. Install the required packages

pip install -r requirements.txt

4. Run the application

You can run the application with the following command:

python run.py  -category <text/vision>

Note: The -category is an optional argument to specify whether you are processing data for a language model (text) or a vision model (vision). If not provided, the system will default to processing data for a language model.

Usage

  1. Run the application
  2. Provide the path for the unstructured data (currently only pdf is supported)
  3. Provide the schema for the question-answer pairs (eg: {'user': question, 'bot': answer})
  4. The application will generate the question-answer pairs based on the schema