metadata

license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: ru
datasets:
  - lmqg/qg_ruquad
pipeline_tag: text2text-generation
tags:
  - question generation
widget:
  - text: >-
      Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев,
      поначалу априорно выдвинув идею о температуре, при которой высота мениска
      будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.
    example_title: Question Generation Example 1
  - text: >-
      Однако, франкоязычный <hl> Квебек <hl> практически никогда не включается в
      состав Латинской Америки.
    example_title: Question Generation Example 2
  - text: >-
      Классическим примером международного синдиката XX века была группа
      компаний <hl> Де Бирс <hl> , которая в 1980-е годы контролировала до 90 %
      мировой торговли алмазами.
    example_title: Question Generation Example 3
model-index:
  - name: lmqg/mt5-small-ruquad-qg
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_ruquad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 16.31
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 31.39
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 26.39
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 84.27
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 62.49
          - name: BLEU4 (Question & Answer Generation)
            type: bleu4_question_answer_generation
            value: 18.61
          - name: ROUGE-L (Question & Answer Generation)
            type: rouge_l_question_answer_generation
            value: 51.04
          - name: METEOR (Question & Answer Generation)
            type: meteor_question_answer_generation
            value: 43.1
          - name: BERTScore (Question & Answer Generation)
            type: bertscore_question_answer_generation
            value: 90.03
          - name: MoverScore (Question & Answer Generation)
            type: moverscore_question_answer_generation
            value: 67.82
          - name: >-
              QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
            value: 90.17
          - name: >-
              QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
            value: 90.16
          - name: >-
              QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_bertscore_question_answer_generation_gold_answer
            value: 90.17
          - name: >-
              QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
            value: 68.22
          - name: >-
              QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_recall_moverscore_question_answer_generation_gold_answer
            value: 68.21
          - name: >-
              QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_moverscore_question_answer_generation_gold_answer
            value: 68.23

Model Card of `lmqg/mt5-small-ruquad-qg`

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

Overview

Language model: google/mt5-small
Language: ru
Training data: lmqg/qg_ruquad (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="ru", model="lmqg/mt5-small-ruquad-qg")

# model prediction
questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года")

With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-small-ruquad-qg")
output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.")

Evaluation

Metric (Question Generation): raw metric file

	Score	Type	Dataset
BERTScore	84.27	default	lmqg/qg_ruquad
Bleu_1	31.03	default	lmqg/qg_ruquad
Bleu_2	24.58	default	lmqg/qg_ruquad
Bleu_3	19.92	default	lmqg/qg_ruquad
Bleu_4	16.31	default	lmqg/qg_ruquad
METEOR	26.39	default	lmqg/qg_ruquad
MoverScore	62.49	default	lmqg/qg_ruquad
ROUGE_L	31.39	default	lmqg/qg_ruquad

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
BERTScore	90.03	default	lmqg/qg_ruquad
Bleu_1	45.81	default	lmqg/qg_ruquad
Bleu_2	34.13	default	lmqg/qg_ruquad
Bleu_3	25.81	default	lmqg/qg_ruquad
Bleu_4	18.61	default	lmqg/qg_ruquad
METEOR	43.1	default	lmqg/qg_ruquad
MoverScore	67.82	default	lmqg/qg_ruquad
QAAlignedF1Score (BERTScore)	90.17	default	lmqg/qg_ruquad
QAAlignedF1Score (MoverScore)	68.22	default	lmqg/qg_ruquad
QAAlignedPrecision (BERTScore)	90.17	default	lmqg/qg_ruquad
QAAlignedPrecision (MoverScore)	68.23	default	lmqg/qg_ruquad
QAAlignedRecall (BERTScore)	90.16	default	lmqg/qg_ruquad
QAAlignedRecall (MoverScore)	68.21	default	lmqg/qg_ruquad
ROUGE_L	51.04	default	lmqg/qg_ruquad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_ruquad
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: 5
batch: 64
lr: 0.001
fp16: False
random_seed: 1
gradient_accumulation_steps: 1
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",
}