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Distilbart Qgen 3 3

Developed by gpssohi
This model is a BART variant fine-tuned on the SQuAD dataset, specifically designed to generate corresponding questions based on text passages and answers.
Downloads 21
Release Time : 3/2/2022

Model Overview

This is a text-to-text generation model capable of generating natural language questions based on given text passages and answers. The model is compressed into a smaller scale through distillation techniques, making it suitable for practical deployment.

Model Features

Distillation Compression
Distilled from a 6-6 layer BART model to a 3-3 layer structure, reducing model size while maintaining performance.
End-to-End Question Generation
Directly generates fluent and natural questions based on text passages and answers.
Fine-tuned on SQuAD
Fine-tuned on a high-quality QA dataset to ensure the relevance of generated questions.

Model Capabilities

Text Understanding
Question Generation
Text Summarization (basic capability)

Use Cases

Educational Technology
Automatic Question Bank Generation
Automatically generates exercises and exam questions based on textbook content.
Significantly reduces teachers' workload in question preparation.
Dialogue Systems
Dialogue Guidance
Helps chatbots proactively ask relevant questions to guide conversations.
Improves dialogue fluency and user engagement.
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