lmqg/mt5-small-zhquad-qag

license: cc-by-4.0 metrics:

• —bleu4
• —meteor
• —rouge-l
• —bertscore
• —moverscore language: zh datasets:
• —lmqg/qagzhquad pipelinetag: text2text-generation tags:
• —questions and answers generation widget:
• —text: "南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近 南安普敦中央 火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。" example_title: "Questions & Answers Generation Example 1" model-index:
• —name: lmqg/mt5-small-zhquad-qag results:
• —task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qag_zhquad type: default args: default metrics:
• —name: QAAlignedF1Score-BERTScore (Question & Answer Generation) type: qaalignedf1scorebertscorequestionanswer_generation value: 75.47
• —name: QAAlignedRecall-BERTScore (Question & Answer Generation) type: qaalignedrecallbertscorequestionanswergeneration value: 75.41
• —name: QAAlignedPrecision-BERTScore (Question & Answer Generation) type: qaalignedprecisionbertscorequestionanswergeneration value: 75.56
• —name: QAAlignedF1Score-MoverScore (Question & Answer Generation) type: qaalignedf1scoremoverscorequestionanswer_generation value: 52.42
• —name: QAAlignedRecall-MoverScore (Question & Answer Generation) type: qaalignedrecallmoverscorequestionanswergeneration value: 52.33
• —name: QAAlignedPrecision-MoverScore (Question & Answer Generation) type: qaalignedprecisionmoverscorequestionanswergeneration value: 52.53 ---

Model Card of lmqg/mt5-small-zhquad-qag

This model is fine-tuned version of google/mt5-small for question & answer pair generation task on the lmqg/qag_zhquad (dataset_name: default) via `lmqg`.

Overview

• —Language model: google/mt5-small
• —Language: zh
• —Training data: lmqg/qag_zhquad (default)
• —Online Demo: https://autoqg.net/
• —Repository: https://github.com/asahi417/lm-question-generation
• —Paper: https://arxiv.org/abs/2210.03992

Usage

• —With `lmqg`

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="zh", model="lmqg/mt5-small-zhquad-qag")

# model prediction
question_answer_pairs = model.generate_qa("南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近南安普敦中央火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。")

• —With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-small-zhquad-qag")
output = pipe("南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近 南安普敦中央 火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。")

Evaluation

• —*Metric (Question & Answer Generation)*: raw metric file

Training hyperparameters

The following hyperparameters were used during fine-tuning:

• —datasetpath: lmqg/qagzhquad
• —dataset_name: default
• —input_types: ['paragraph']
• —outputtypes: ['questionsanswers']
• —prefix_types: None
• —model: google/mt5-small
• —max_length: 512
• —maxlengthoutput: 256
• —epoch: 12
• —batch: 8
• —lr: 0.001
• —fp16: False
• —random_seed: 1
• —gradientaccumulationsteps: 8
• —label_smoothing: 0.15

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

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",
}