lmqg/t5-small-squad-qag的模型卡片

这个模型是基于 t5-small 进行微调的，用于 lmqg 上的问答对生成任务（数据集名称：默认）。

概述

• 语言模型： t5-small
• 语言：en
• 训练数据： lmqg/qag_squad （默认）
• 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

使用方式

• 使用 lmqg

from lmqg import TransformersQG

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

# 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-small-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

• 度量指标（问题和答案生成）： 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/qag_squad
• 数据集名称：默认
• 输入类型：['paragraph']
• 输出类型：['questions_answers']
• 前缀类型：['qag']
• 模型：t5-small
• 最大长度：512
• 输出最大长度：256
• 迭代轮数：18
• 批次大小：32
• 学习率：0.0001
• fp16：False
• 随机种子：1
• 梯度累积步数：2
• 标签平滑：0.0

详细配置信息可在 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",
}