README.md

---
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: 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: 90.17
    - 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: 90.16
    - 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: 90.17
    - 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: 68.22
    - 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: 68.21
    - 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: 68.23
    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]
      type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
      value: 76.96
    - name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]
      type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
      value: 81.05
    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]
      type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer
      value: 73.41
    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]
      type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
      value: 55.53
    - name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]
      type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer
      value: 58.25
    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]
      type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer
      value: 53.24
---

# 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, Reference Answer)***: Each question is generated from *the gold 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                                                          |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| 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) |


- ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mt5-small-ruquad-ae`](https://huggingface.co/lmqg/mt5-small-ruquad-ae). [raw metric file](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.lmqg_mt5-small-ruquad-ae.json)

|                                 |   Score | Type    | Dataset                                                          |
|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   76.96 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedF1Score (MoverScore)   |   55.53 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedPrecision (BERTScore)  |   73.41 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedPrecision (MoverScore) |   53.24 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedRecall (BERTScore)     |   81.05 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| QAAlignedRecall (MoverScore)    |   58.25 | 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",
}

```