T5 Base Squad Qag

🚀 Model Card of lmqg/t5-base-squad-qag

This model is a fine - tuned version of [t5 - base](https://huggingface.co/t5 - base) for the question & answer pair generation task on the lmqg/qag_squad (dataset_name: default) via [lmqg](https://github.com/asahi417/lm - question - generation). It aims to generate high - quality question - answer pairs from given text.

🚀 Quick Start

Model Overview

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

Usage

💻 Usage Examples

Basic Usage

• With [lmqg](https://github.com/asahi417/lm - question - generation#lmqg - language - model - for - question - generation -)

from lmqg import TransformersQG

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

# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")

Advanced Usage

• With transformers

from transformers import pipeline

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

📚 Documentation

Evaluation

• Metric (Question & Answer Generation): [raw metric file](https://huggingface.co/lmqg/t5 - base - squad - qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_squad.default.json)

Training hyperparameters

The following hyperparameters were used during fine - tuning:

The full configuration can be found at [fine - tuning config file](https://huggingface.co/lmqg/t5 - base - squad - qag/raw/main/trainer_config.json).

📄 License

This model is licensed under cc - by - 4.0.

📚 Citation

@inproceedings{ushio - etal - 2022 - generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph - {L}evel {Q}uestion {G}eneration",
    author = "Ushio, Asahi  and
        Alva - Manchego, Fernando  and
        Camacho - Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}