T5 Large Squad Qg

🚀 Model Card of lmqg/t5-large-squad-qg

This model is a fine-tuned version of t5-large for the question generation task on the lmqg/qg_squad (dataset_name: default) via lmqg.

🚀 Quick Start

Overview

• Language model: t5-large
• Language: en
• Training data: lmqg/qg_squad (default)
• Online Demo: https://autoqg.net/
• Repository: https://github.com/asahi417/lm-question-generation
• Paper: https://arxiv.org/abs/2210.03992

Usage

💻 Usage Examples

Basic Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/t5-large-squad-qg")

# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")

Advanced Usage

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/t5-large-squad-qg")
output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

📚 Documentation

Evaluation

• Metric (Question Generation): raw metric file

• Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file

• Metric (Question & Answer Generation, Pipeline Approach): Each question is generated on the answer generated by lmqg/t5-large-squad-ae. raw metric file

• Metrics (Question Generation, Out-of-Domain)

🔧 Technical Details

Training hyperparameters

The following hyperparameters were used during fine-tuning:

• dataset_path: lmqg/qg_squad
• dataset_name: default
• input_types: ['paragraph_answer']
• output_types: ['question']

📄 License

This model is licensed under CC BY 4.0.