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---
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](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)
- **Language:** ru
- **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
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`
```python
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](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 84.27 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_1 | 31.03 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_2 | 24.58 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_3 | 19.92 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_4 | 16.31 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| METEOR | 26.39 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| MoverScore | 62.49 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| ROUGE_L | 31.39 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/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](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore | 90.03 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_1 | 45.81 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_2 | 34.13 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_3 | 25.81 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_4 | 18.61 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| METEOR | 43.1 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| MoverScore | 67.82 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedF1Score (BERTScore) | 90.17 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedF1Score (MoverScore) | 68.22 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedPrecision (BERTScore) | 90.17 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedPrecision (MoverScore) | 68.23 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedRecall (BERTScore) | 90.16 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedRecall (MoverScore) | 68.21 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| ROUGE_L | 51.04 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/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](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/trainer_config.json).
## 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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