Bart Base Squad Qg No Answer

🚀 research-backup/bart-base-squad-qg-no-answer Model Card

This model is a fine - tuned version of facebook/bart-base for the question generation task on the lmqg/qg_squad (dataset_name: default) via lmqg. It is fine - tuned without answer information, meaning it generates a question only given a paragraph (note that a normal model is fine - tuned to generate a question given a paragraph and an associated answer in the paragraph).

✨ Features

• Language model: facebook/bart-base
• 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

📦 Installation

No specific installation steps are provided in the original README.

💻 Usage Examples

Basic Usage

With lmqg

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="research-backup/bart-base-squad-qg-no-answer")

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

With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "research-backup/bart-base-squad-qg-no-answer")
output = pipe("<hl>  Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>")

📚 Documentation

Evaluation

• Metric (Question Generation): raw metric file

Training hyperparameters

The following hyperparameters were used during fine - tuning:

• dataset_path: lmqg/qg_squad
• dataset_name: default
• input_types: ['paragraph_sentence']
• output_types: ['question']
• prefix_types: None
• model: facebook/bart-base
• max_length: 512
• max_length_output: 32
• epoch: 4
• batch: 32
• lr: 0.0001
• fp16: False
• random_seed: 1
• gradient_accumulation_steps: 8
• label_smoothing: 0.15

The full configuration can be found at fine - tuning config file.

📄 License

The 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",
}