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/mbart-large-cc25-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: 18.8
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 34.18
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 29.3
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 87.18
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 65.88
- name: >-
QAAlignedF1Score-BERTScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 92.08
- name: >-
QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold
Answer)) [Gold Answer]
type: >-
qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 92.08
- name: >-
QAAlignedPrecision-BERTScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 92.09
- name: >-
QAAlignedF1Score-MoverScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 71.45
- name: >-
QAAlignedRecall-MoverScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 71.45
- name: >-
QAAlignedPrecision-MoverScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 71.46
Model Card of lmqg/mbart-large-cc25-ruquad-qg
This model is fine-tuned version of facebook/mbart-large-cc25 for question generation task on the lmqg/qg_ruquad (dataset_name: default) via lmqg
.
Overview
- Language model: facebook/mbart-large-cc25
- 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/mbart-large-cc25-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/mbart-large-cc25-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 | 87.18 | default | lmqg/qg_ruquad |
Bleu_1 | 35.25 | default | lmqg/qg_ruquad |
Bleu_2 | 28.1 | default | lmqg/qg_ruquad |
Bleu_3 | 22.87 | default | lmqg/qg_ruquad |
Bleu_4 | 18.8 | default | lmqg/qg_ruquad |
METEOR | 29.3 | default | lmqg/qg_ruquad |
MoverScore | 65.88 | default | lmqg/qg_ruquad |
ROUGE_L | 34.18 | default | lmqg/qg_ruquad |
- Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file
Score | Type | Dataset | |
---|---|---|---|
QAAlignedF1Score (BERTScore) | 92.08 | default | lmqg/qg_ruquad |
QAAlignedF1Score (MoverScore) | 71.45 | default | lmqg/qg_ruquad |
QAAlignedPrecision (BERTScore) | 92.09 | default | lmqg/qg_ruquad |
QAAlignedPrecision (MoverScore) | 71.46 | default | lmqg/qg_ruquad |
QAAlignedRecall (BERTScore) | 92.08 | default | lmqg/qg_ruquad |
QAAlignedRecall (MoverScore) | 71.45 | 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: facebook/mbart-large-cc25
- max_length: 512
- max_length_output: 32
- epoch: 17
- batch: 4
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 16
- 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",
}