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Flan T5 Large Squad2

Developed by sjrhuschlee
An extractive QA model fine-tuned on the SQuAD2.0 dataset based on flan-t5-large, capable of handling both answerable and unanswerable questions.
Downloads 57
Release Time : 6/14/2023

Model Overview

This model is optimized for English extractive QA tasks, specifically addressing answerable and unanswerable question pairs in the SQuAD2.0 dataset.

Model Features

LoRA Fine-tuning
Efficient fine-tuning using LoRA from the PEFT library, maintaining model performance while reducing computational resource requirements
Special Token Handling
Uses the <cls> token to predict 'no-answer' scenarios, effectively handling unanswerable questions
Multi-dataset Validation
Comprehensively validated on SQuAD, SQuAD2.0, and multiple adversarial datasets

Model Capabilities

Extractive QA
Unanswerable Question Detection
Context Understanding

Use Cases

Customer Support
FAQ Auto-response
Automatically answers common user questions based on knowledge base content
Achieved 86.8% exact match rate on the SQuAD2.0 validation set
Education
Reading Comprehension Assistance
Helps students understand texts and answer related questions
Achieved 95.06 F1 score on the SQuAD validation set
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