lmqg/mt5-small-itquad-qg

license: cc-by-4.0 metrics:

• —bleu4
• —meteor
• —rouge-l
• —bertscore
• —moverscore language: it datasets:
• —lmqg/qgitquad pipelinetag: text2text-generation tags:
• —question generation widget:
• —text: "<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento." example_title: "Question Generation Example 1"
• —text: "L' individuazione del petrolio e lo sviluppo di nuovi giacimenti richiedeva in genere <hl> da cinque a dieci anni <hl> prima di una produzione significativa." example_title: "Question Generation Example 2"
• —text: "il <hl> Giappone <hl> è stato il paese più dipendente dal petrolio arabo." example_title: "Question Generation Example 3" model-index:
• —name: lmqg/mt5-small-itquad-qg results:
• —task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_itquad type: default args: default metrics:
• —name: BLEU4 (Question Generation) type: bleu4questiongeneration value: 7.37
• —name: ROUGE-L (Question Generation) type: rougelquestion_generation value: 21.93
• —name: METEOR (Question Generation) type: meteorquestiongeneration value: 17.57
• —name: BERTScore (Question Generation) type: bertscorequestiongeneration value: 80.8
• —name: MoverScore (Question Generation) type: moverscorequestiongeneration value: 56.79
• —name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qaalignedf1scorebertscorequestionanswergenerationwithgoldanswergoldanswer value: 87.66
• —name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qaalignedrecallbertscorequestionanswergenerationwithgoldanswergold_answer value: 87.57
• —name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qaalignedprecisionbertscorequestionanswergenerationwithgoldanswergold_answer value: 87.76
• —name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qaalignedf1scoremoverscorequestionanswergenerationwithgoldanswergoldanswer value: 61.6
• —name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qaalignedrecallmoverscorequestionanswergenerationwithgoldanswergold_answer value: 61.48
• —name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qaalignedprecisionmoverscorequestionanswergenerationwithgoldanswergold_answer value: 61.73
• —name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] type: qaalignedf1scorebertscorequestionanswergenerationgold_answer value: 81.63
• —name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] type: qaalignedrecallbertscorequestionanswergenerationgoldanswer value: 82.28
• —name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] type: qaalignedprecisionbertscorequestionanswergenerationgoldanswer value: 81.04
• —name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] type: qaalignedf1scoremoverscorequestionanswergenerationgold_answer value: 55.85
• —name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] type: qaalignedrecallmoverscorequestionanswergenerationgoldanswer value: 56.14
• —name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] type: qaalignedprecisionmoverscorequestionanswergenerationgoldanswer value: 55.6 ---

Model Card of lmqg/mt5-small-itquad-qg

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

Overview

• —Language model: google/mt5-small
• —Language: it
• —Training data: lmqg/qg_itquad (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="it", model="lmqg/mt5-small-itquad-qg")

# model prediction
questions = model.generate_q(list_context="Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", list_answer="Dopo il 1971")

• —With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-small-itquad-qg")
output = pipe("<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")

Evaluation

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

• —*Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer*. raw metric file

• —*Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by `lmqg/mt5-small-itquad-ae`. raw metric file

Training hyperparameters

The following hyperparameters were used during fine-tuning:

• —datasetpath: lmqg/qgitquad
• —dataset_name: default
• —inputtypes: ['paragraphanswer']
• —output_types: ['question']
• —prefix_types: None
• —model: google/mt5-small
• —max_length: 512
• —maxlengthoutput: 32
• —epoch: 15
• —batch: 16
• —lr: 0.0005
• —fp16: False
• —random_seed: 1
• —gradientaccumulationsteps: 4
• —label_smoothing: 0.0

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",
}