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Model Card of lmqg/mt5-base-itquad-qg-ae

This model is fine-tuned version of google/mt5-base for question generation and answer extraction jointly on the lmqg/qg_itquad (dataset_name: default) via lmqg.

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="it", model="lmqg/mt5-base-itquad-qg-ae")

# model prediction
question_answer_pairs = model.generate_qa("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")
  • With transformers
from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-base-itquad-qg-ae")

# answer extraction
answer = pipe("generate question: <hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")

# question generation
question = pipe("extract answers: <hl> Il 6 ottobre 1973 , la Siria e l' Egitto, con il sostegno di altre nazioni arabe, lanciarono un attacco a sorpresa su Israele, su Yom Kippur. <hl> Questo rinnovo delle ostilità nel conflitto arabo-israeliano ha liberato la pressione economica sottostante sui prezzi del petrolio. All' epoca, l' Iran era il secondo esportatore mondiale di petrolio e un vicino alleato degli Stati Uniti. Settimane più tardi, lo scià d' Iran ha detto in un' intervista: Naturalmente[il prezzo del petrolio] sta andando a salire Certamente! E come! Avete[Paesi occidentali] aumentato il prezzo del grano che ci vendete del 300 per cento, e lo stesso per zucchero e cemento.")

Evaluation

Score Type Dataset
BERTScore 81.15 default lmqg/qg_itquad
Bleu_1 23.3 default lmqg/qg_itquad
Bleu_2 15.39 default lmqg/qg_itquad
Bleu_3 10.74 default lmqg/qg_itquad
Bleu_4 7.72 default lmqg/qg_itquad
METEOR 18.56 default lmqg/qg_itquad
MoverScore 57.15 default lmqg/qg_itquad
ROUGE_L 22.81 default lmqg/qg_itquad
Score Type Dataset
QAAlignedF1Score (BERTScore) 81.98 default lmqg/qg_itquad
QAAlignedF1Score (MoverScore) 56.35 default lmqg/qg_itquad
QAAlignedPrecision (BERTScore) 81.19 default lmqg/qg_itquad
QAAlignedPrecision (MoverScore) 56 default lmqg/qg_itquad
QAAlignedRecall (BERTScore) 82.83 default lmqg/qg_itquad
QAAlignedRecall (MoverScore) 56.75 default lmqg/qg_itquad
Score Type Dataset
AnswerExactMatch 60.7 default lmqg/qg_itquad
AnswerF1Score 74.04 default lmqg/qg_itquad
BERTScore 91.12 default lmqg/qg_itquad
Bleu_1 40.14 default lmqg/qg_itquad
Bleu_2 34.56 default lmqg/qg_itquad
Bleu_3 30.56 default lmqg/qg_itquad
Bleu_4 26.87 default lmqg/qg_itquad
METEOR 43.51 default lmqg/qg_itquad
MoverScore 82.62 default lmqg/qg_itquad
ROUGE_L 45.82 default lmqg/qg_itquad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_itquad
  • dataset_name: default
  • input_types: ['paragraph_answer', 'paragraph_sentence']
  • output_types: ['question', 'answer']
  • prefix_types: ['qg', 'ae']
  • model: google/mt5-base
  • max_length: 512
  • max_length_output: 32
  • epoch: 13
  • batch: 32
  • lr: 0.0005
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 2
  • 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",
}
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Dataset used to train lmqg/mt5-base-itquad-qg-ae

Evaluation results

  • BLEU4 (Question Generation) on lmqg/qg_itquad
    self-reported
    7.720
  • ROUGE-L (Question Generation) on lmqg/qg_itquad
    self-reported
    22.810
  • METEOR (Question Generation) on lmqg/qg_itquad
    self-reported
    18.560
  • BERTScore (Question Generation) on lmqg/qg_itquad
    self-reported
    81.150
  • MoverScore (Question Generation) on lmqg/qg_itquad
    self-reported
    57.150
  • QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_itquad
    self-reported
    81.980
  • QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_itquad
    self-reported
    82.830
  • QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_itquad
    self-reported
    81.190
  • QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_itquad
    self-reported
    56.350
  • QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_itquad
    self-reported
    56.750
  • QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_itquad
    self-reported
    56.000
  • BLEU4 (Answer Extraction) on lmqg/qg_itquad
    self-reported
    26.870
  • ROUGE-L (Answer Extraction) on lmqg/qg_itquad
    self-reported
    45.820
  • METEOR (Answer Extraction) on lmqg/qg_itquad
    self-reported
    43.510
  • BERTScore (Answer Extraction) on lmqg/qg_itquad
    self-reported
    91.120
  • MoverScore (Answer Extraction) on lmqg/qg_itquad
    self-reported
    82.620
  • AnswerF1Score (Answer Extraction) on lmqg/qg_itquad
    self-reported
    74.040
  • AnswerExactMatch (Answer Extraction) on lmqg/qg_itquad
    self-reported
    60.700