
# Model Card of `vocabtrimmer/mt5-small-trimmed-ru-15000-ruquad-qg`
This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ru-15000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-15000) 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:** [vocabtrimmer/mt5-small-trimmed-ru-15000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-15000)   
- **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="vocabtrimmer/mt5-small-trimmed-ru-15000-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", "vocabtrimmer/mt5-small-trimmed-ru-15000-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/vocabtrimmer/mt5-small-trimmed-ru-15000-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) 

|            |   Score | Type    | Dataset                                                          |
|:-----------|--------:|:--------|:-----------------------------------------------------------------|
| BERTScore  |   86.51 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_1     |   34.47 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_2     |   27.53 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_3     |   22.43 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| Bleu_4     |   18.47 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| METEOR     |   29.05 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| MoverScore |   65.12 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) |
| ROUGE_L    |   33.91 | 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: vocabtrimmer/mt5-small-trimmed-ru-15000
 - max_length: 512
 - max_length_output: 32
 - epoch: 14
 - batch: 16
 - lr: 0.001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 4
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ru-15000-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",
}

```

{"language": "ru", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_ruquad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-generation", "widget": [{"text": "\u041d\u0435\u043b\u0438\u0448\u043d\u0438\u043c \u0431\u0443\u0434\u0435\u0442 \u043e\u0442\u043c\u0435\u0442\u0438\u0442\u044c, \u0447\u0442\u043e, \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u044f \u044d\u0442\u043e \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435, \u0414. \u0418. \u041c\u0435\u043d\u0434\u0435\u043b\u0435\u0435\u0432, \u043f\u043e\u043d\u0430\u0447\u0430\u043b\u0443 \u0430\u043f\u0440\u0438\u043e\u0440\u043d\u043e \u0432\u044b\u0434\u0432\u0438\u043d\u0443\u0432 \u0438\u0434\u0435\u044e \u043e \u0442\u0435\u043c\u043f\u0435\u0440\u0430\u0442\u0443\u0440\u0435, \u043f\u0440\u0438 \u043a\u043e\u0442\u043e\u0440\u043e\u0439 \u0432\u044b\u0441\u043e\u0442\u0430 \u043c\u0435\u043d\u0438\u0441\u043a\u0430 \u0431\u0443\u0434\u0435\u0442 \u043d\u0443\u043b\u0435\u0432\u043e\u0439, <hl> \u0432 \u043c\u0430\u0435 1860 \u0433\u043e\u0434\u0430 <hl> \u043f\u0440\u043e\u0432\u0451\u043b \u0441\u0435\u0440\u0438\u044e \u043e\u043f\u044b\u0442\u043e\u0432.", "example_title": "Question Generation Example 1"}, {"text": "\u041e\u0434\u043d\u0430\u043a\u043e, \u0444\u0440\u0430\u043d\u043a\u043e\u044f\u0437\u044b\u0447\u043d\u044b\u0439 <hl> \u041a\u0432\u0435\u0431\u0435\u043a <hl> \u043f\u0440\u0430\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u0438 \u043d\u0438\u043a\u043e\u0433\u0434\u0430 \u043d\u0435 \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442\u0441\u044f \u0432 \u0441\u043e\u0441\u0442\u0430\u0432 \u041b\u0430\u0442\u0438\u043d\u0441\u043a\u043e\u0439 \u0410\u043c\u0435\u0440\u0438\u043a\u0438.", "example_title": "Question Generation Example 2"}, {"text": "\u041a\u043b\u0430\u0441\u0441\u0438\u0447\u0435\u0441\u043a\u0438\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u043e\u043c \u043c\u0435\u0436\u0434\u0443\u043d\u0430\u0440\u043e\u0434\u043d\u043e\u0433\u043e \u0441\u0438\u043d\u0434\u0438\u043a\u0430\u0442\u0430 XX \u0432\u0435\u043a\u0430 \u0431\u044b\u043b\u0430 \u0433\u0440\u0443\u043f\u043f\u0430 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0439 <hl> \u0414\u0435 \u0411\u0438\u0440\u0441 <hl> , \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0432 1980-\u0435 \u0433\u043e\u0434\u044b \u043a\u043e\u043d\u0442\u0440\u043e\u043b\u0438\u0440\u043e\u0432\u0430\u043b\u0430 \u0434\u043e 90 % \u043c\u0438\u0440\u043e\u0432\u043e\u0439 \u0442\u043e\u0440\u0433\u043e\u0432\u043b\u0438 \u0430\u043b\u043c\u0430\u0437\u0430\u043c\u0438.", "example_title": "Question Generation Example 3"}], "model-index": [{"name": "vocabtrimmer/mt5-small-trimmed-ru-15000-ruquad-qg", "results": [{"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_ruquad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_generation", "value": 18.47, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 33.91, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 29.05, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 86.51, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 65.12, "name": "MoverScore (Question Generation)"}]}]}]}
transformers pytorch mt5 text2text-generation question generation ru dataset:lmqg/qg_ruquad arxiv:2210.03992 license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #ru #dataset-lmqg/qg_ruquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Model Card of 'vocabtrimmer/mt5-small-trimmed-ru-15000-ruquad-qg'
=================================================================


This model is fine-tuned version of vocabtrimmer/mt5-small-trimmed-ru-15000 for question generation task on the lmqg/qg\_ruquad (dataset\_name: default) via 'lmqg'.


### Overview


* Language model: vocabtrimmer/mt5-small-trimmed-ru-15000
* Language: ru
* Training data: lmqg/qg\_ruquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL


### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



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: vocabtrimmer/mt5-small-trimmed-ru-15000
* max\_length: 512
* max\_length\_output: 32
* epoch: 14
* batch: 16
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 4
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.



### Overview


* Language model: vocabtrimmer/mt5-small-trimmed-ru-15000
* Language: ru
* Training data: lmqg/qg\_ruquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



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: vocabtrimmer/mt5-small-trimmed-ru-15000
* max\_length: 512
* max\_length\_output: 32
* epoch: 14
* batch: 16
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 4
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #ru #dataset-lmqg/qg_ruquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Overview


* Language model: vocabtrimmer/mt5-small-trimmed-ru-15000
* Language: ru
* Training data: lmqg/qg\_ruquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



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: vocabtrimmer/mt5-small-trimmed-ru-15000
* max\_length: 512
* max\_length\_output: 32
* epoch: 14
* batch: 16
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 4
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.
TAGS
#transformers #pytorch #mt5 #text2text-generation #question generation #ru #dataset-lmqg/qg_ruquad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Overview


* Language model: vocabtrimmer/mt5-small-trimmed-ru-15000
* Language: ru
* Training data: lmqg/qg\_ruquad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



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: vocabtrimmer/mt5-small-trimmed-ru-15000
* max\_length: 512
* max\_length\_output: 32
* epoch: 14
* batch: 16
* lr: 0.001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 4
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.