---
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-base-ruquad
  results:
  - task:
      name: Text2text Generation
      type: text2text-generation
    dataset:
      name: lmqg/qg_ruquad
      type: default
      args: default
    metrics:
    - name: BLEU4
      type: bleu4
      value: 0.1762872999938764
    - name: ROUGE-L
      type: rouge-l
      value: 0.33020466659222064
    - name: METEOR
      type: meteor
      value: 0.28478453419683164
    - name: BERTScore
      type: bertscore
      value: 0.8581777676594822
    - name: MoverScore
      type: moverscore
      value: 0.6455603400134543
---

# Model Card of `lmqg/mt5-base-ruquad`
This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) 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).


Please cite our paper if you use the model ([TBA](TBA)).

```

@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration: {A} {U}nified {B}enchmark and {E}valuation",
    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",
}

```

### Overview
- **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)   
- **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:** [TBA](TBA)

### Usage
```python

from transformers import pipeline

model_path = 'lmqg/mt5-base-ruquad'
pipe = pipeline("text2text-generation", model_path)

# Question Generation
question = 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 Metrics


### Metrics

| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
| [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | default | 0.176 | 0.33 | 0.285 | 0.858 | 0.646 | [link](https://huggingface.co/lmqg/mt5-base-ruquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) | 




## 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-base
 - max_length: 512
 - max_length_output: 32
 - epoch: 16
 - batch: 4
 - lr: 0.0005
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 16
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-ruquad/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: {A} {U}nified {B}enchmark and {E}valuation",
    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",
}