mt5-small-dequad-qg / README.md
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metadata
license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: de
datasets:
  - lmqg/qg_dequad
pipeline_tag: text2text-generation
tags:
  - question generation
widget:
  - text: >-
      Empfangs- und Sendeantenne sollen in ihrer Polarisation übereinstimmen,
      andernfalls <hl> wird die Signalübertragung stark gedämpft. <hl>
    example_title: Question Generation Example 1
  - text: >-
      das erste weltweit errichtete Hermann Brehmer <hl> 1855 <hl> im
      niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).
    example_title: Question Generation Example 2
  - text: >-
      Er muss Zyperngrieche sein und wird direkt für <hl> fünf Jahre <hl>
      gewählt (Art. 43 Abs. 1 der Verfassung) und verfügt über weitreichende
      Exekutivkompetenzen.
    example_title: Question Generation Example 3
model-index:
  - name: lmqg/mt5-small-dequad-qg
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_dequad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0.43
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 10.08
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 11.47
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 79.9
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 54.64
          - name: >-
              QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
            value: 90.55
          - name: >-
              QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
            value: 90.51
          - name: >-
              QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_bertscore_question_answer_generation_gold_answer
            value: 90.59
          - name: >-
              QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
            value: 64.33
          - name: >-
              QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_recall_moverscore_question_answer_generation_gold_answer
            value: 64.29
          - name: >-
              QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_moverscore_question_answer_generation_gold_answer
            value: 64.37

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

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

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="de", model="lmqg/mt5-small-dequad-qg")

# model prediction
questions = model.generate_q(list_context="das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).", list_answer="1855")
  • With transformers
from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-small-dequad-qg")
output = pipe("Empfangs- und Sendeantenne sollen in ihrer Polarisation übereinstimmen, andernfalls <hl> wird die Signalübertragung stark gedämpft. <hl>")

Evaluation

Score Type Dataset
BERTScore 79.9 default lmqg/qg_dequad
Bleu_1 10.18 default lmqg/qg_dequad
Bleu_2 4.02 default lmqg/qg_dequad
Bleu_3 1.6 default lmqg/qg_dequad
Bleu_4 0.43 default lmqg/qg_dequad
METEOR 11.47 default lmqg/qg_dequad
MoverScore 54.64 default lmqg/qg_dequad
ROUGE_L 10.08 default lmqg/qg_dequad
  • Metric (Question & Answer Generation): QAG metrics are computed with the gold answer and generated question on it for this model, as the model cannot provide an answer. raw metric file
Score Type Dataset
QAAlignedF1Score (BERTScore) 90.55 default lmqg/qg_dequad
QAAlignedF1Score (MoverScore) 64.33 default lmqg/qg_dequad
QAAlignedPrecision (BERTScore) 90.59 default lmqg/qg_dequad
QAAlignedPrecision (MoverScore) 64.37 default lmqg/qg_dequad
QAAlignedRecall (BERTScore) 90.51 default lmqg/qg_dequad
QAAlignedRecall (MoverScore) 64.29 default lmqg/qg_dequad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_dequad
  • dataset_name: default
  • input_types: ['paragraph_answer']
  • output_types: ['question']
  • prefix_types: None
  • model: google/mt5-small
  • max_length: 512
  • max_length_output: 32
  • epoch: 11
  • batch: 16
  • lr: 0.001
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 4
  • 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",
}