T5 Base Squad Qg Ae

🚀 Model Card of lmqg/t5-base-squad-qg-ae

This model is a fine - tuned version of [t5 - base](https://huggingface.co/t5 - base) for joint question generation and answer extraction. It is trained on the lmqg/qg_squad (dataset_name: default) via [lmqg](https://github.com/asahi417/lm - question - generation).

✨ Features

• Language Model: Utilizes [t5 - base](https://huggingface.co/t5 - base) as the foundation.
• Language: Supports English (en).
• Training Data: Trained on lmqg/qg_squad (default).
• Online Demo: Available at https://autoqg.net/.
• Repository: Check the source code at [https://github.com/asahi417/lm - question - generation](https://github.com/asahi417/lm - question - generation).
• Paper: Refer to the research paper https://arxiv.org/abs/2210.03992.

📦 Installation

Since the model is used through existing libraries, you need to install relevant dependencies. For example, if you use lmqg or transformers, you can install them via pip:

pip install lmqg transformers

💻 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-qg-ae")

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

With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/t5-base-squad-qg-ae")

# answer extraction
answer = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

# question generation
question = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.")

📚 Documentation

Evaluation

Question Generation

• Metric (Question Generation): [raw metric file](https://huggingface.co/lmqg/t5 - base - squad - qg - ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json)

Question & Answer Generation

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

Answer Extraction

• Metric (Answer Extraction): [raw metric file](https://huggingface.co/lmqg/t5 - base - squad - qg - ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_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 - qg - ae/raw/main/trainer_config.json).

📄 License

This project is licensed under the CC - BY - 4.0 license.

📖 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",
}