lmqg/flan-t5-large-squad-qag模型卡片

此模型是在 lmqg/qag_squad （数据集名称：default）上通过 lmqg 进行问题和答案生成任务的fine-tune版本，基于 google/flan-t5-large 进行了训练。

概述

• 语言模型： google/flan-t5-large
• 语言：en
• 训练数据： lmqg/qag_squad （default）
• 在线演示： https://autoqg.net/
• 存储库： https://github.com/asahi417/lm-question-generation
• 论文： https://arxiv.org/abs/2210.03992

用法

• 与 lmqg 一起使用

from lmqg import TransformersQG

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

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

• 与transformers一起使用

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/flan-t5-large-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.")

评估

• 指标（问题和答案生成）： raw metric file

训练超参数

在fine-tuning期间使用了以下超参数：

• dataset_path: lmqg/qag_squad
• dataset_name: default
• input_types: ['paragraph']
• output_types: ['questions_answers']
• prefix_types: ['qag']
• model: google/flan-t5-large
• max_length: 512
• max_length_output: 256
• epoch: 15
• batch: 2
• lr: 0.0001
• fp16: False
• random_seed: 1
• gradient_accumulation_steps: 32
• label_smoothing: 0.0

完整的配置信息可以在 fine-tuning config file 中找到。

引用

@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",
}