lmqg/t5-base-squad-qg-ae的模型卡

该模型是对 t5-base 进行微调的版本，用于在 lmqg 上进行问题生成和答案提取的联合训练（数据集名称：default）。

概述

• 语言模型： t5-base
• 语言：en
• 训练数据： lmqg/qg_squad （默认）
• 在线演示： https://autoqg.net/
• 代码库： https://github.com/asahi417/lm-question-generation
• 论文： https://arxiv.org/abs/2210.03992

用法

• 使用 lmqg

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")

• 使用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.")

评估

• 指标（问题生成）： raw metric file

• 指标（问题和答案生成）： raw metric file

• 指标（答案提取）： raw metric file

训练超参数

在微调过程中使用了以下超参数：

• 数据集路径：lmqg/qg_squad
• 数据集名称：default
• 输入类型：['paragraph_answer', 'paragraph_sentence']
• 输出类型：['question', 'answer']
• 前缀类型：['qg', 'ae']
• 模型：t5-base
• 最大长度：512
• 最大输出长度：32
• 迭代轮数：6
• 批次大小：32
• 学习率：0.0001
• 混合精度：False
• 随机种子：1
• 梯度累积步数：4
• 标签平滑：0.15

完整配置详见 fine-tuning config file 。

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",
}