
# mt5-small for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.
```python
from core.api import GenerationAPI
generation_api = GenerationAPI('mt5-small-3task-prepend-tquad2', qg_format='prepend')
```

##  Citation 📜
```
@article{akyon2022questgen,
    author = {Akyon, Fatih Cagatay and Cavusoglu, Ali Devrim Ekin and Cengiz, Cemil and Altinuc, Sinan Onur and Temizel, Alptekin},
    doi = {10.3906/elk-1300-0632.3914},
    journal = {Turkish Journal of Electrical Engineering and Computer Sciences},
    title = {{Automated question generation and question answering from Turkish texts}},
    url = {https://journals.tubitak.gov.tr/elektrik/vol30/iss5/17/},
    year = {2022}
}
```

## Overview ✔️
**Language model:** mt5-small  
**Language:** Turkish  
**Downstream-task:** Extractive QA/QG, Answer Extraction  
**Training data:** TQuADv2-train
**Code:**  https://github.com/obss/turkish-question-generation  
**Paper:**  https://journals.tubitak.gov.tr/elektrik/vol30/iss5/17/ 

## Hyperparameters
```
batch_size = 256
n_epochs = 15
base_LM_model = "mt5-small"
max_source_length = 512
max_target_length = 64
learning_rate = 1.0e-3
task_lisst = ["qa", "qg", "ans_ext"]
qg_format = "prepend"
``` 

## Performance
Refer to [paper](https://arxiv.org/abs/2111.06476).

## Usage 🔥
```python
from core.api import GenerationAPI

generation_api = GenerationAPI('mt5-small-3task-prepend-tquad2', qg_format='prepend')

context = """
Bu modelin eğitiminde, Türkçe soru cevap verileri kullanılmıştır.
Çalışmada sunulan yöntemle, Türkçe metinlerden otomatik olarak soru ve cevap
üretilebilir. Bu proje ile paylaşılan kaynak kodu ile Türkçe Soru Üretme
/ Soru Cevaplama konularında yeni akademik çalışmalar yapılabilir.
Projenin detaylarına paylaşılan Github ve Arxiv linklerinden ulaşılabilir.
"""

# a) Fully Automated Question Generation
generation_api(task='question-generation', context=context)

# b) Question Answering
question = "Bu model ne işe yarar?"
generation_api(task='question-answering', context=context, question=question)

# b) Answer Extraction
generation_api(task='answer-extraction', context=context)
```

{"language": "tr", "license": "cc-by-4.0", "tags": ["text2text-generation", "question-generation", "answer-extraction", "question-answering", "text-generation"], "datasets": ["tquad1", "tquad2", "xquad"], "pipeline_tag": "text2text-generation", "widget": [{"text": "answer: film ve TV haklar\u0131n\u0131 context: Legendary Entertainment, 2016 y\u0131l\u0131nda bilimkurgu roman\u0131 Dune'un film ve TV haklar\u0131n\u0131 sat\u0131n ald\u0131. Geli\u015ftirme k\u0131sa bir s\u00fcre sonra ba\u015flad\u0131. Villeneuve projeye olan ilgisini dile getirdi ve resmi olarak y\u00f6netmen olarak imza att\u0131. Roth ve Spaihts ile birlikte \u00e7al\u0131\u015farak senaryoyu iki b\u00f6l\u00fcme ay\u0131rd\u0131 ve 1965 roman\u0131n\u0131n 21. y\u00fczy\u0131la g\u00fcncellenmi\u015f bir uyarlamas\u0131n\u0131 ekledi.", "example_title": "Question Generation (Movie)"}, {"text": "answer: bir antla\u015fma yaparak context: Fatih Sultan Mehmet, Cenevizlilerin \u00f6nemli \u00fcslerinden Amasra\u2019y\u0131 ald\u0131. 1479\u2019da bir antla\u015fma yaparak Venedik'le  16 y\u0131ll\u0131k sava\u015fa son verdi.", "example_title": "Question Generation (History)"}, {"text": "answer: Venedik'le context: Cenevizlilerin \u00f6nemli \u00fcslerinden Amasra\u2019y\u0131 ald\u0131. 1479\u2019da bir antla\u015fma yaparak Venedik'le 16 y\u0131ll\u0131k sava\u015fa sona verdi.", "example_title": "Question Generation (History 2)"}, {"text": "extract answers: Cenevizlilerin \u00f6nemli \u00fcslerinden Amasra\u2019y\u0131 ald\u0131. <hl> 1479\u2019da bir antla\u015fma yaparak Venedik'le 16 y\u0131ll\u0131k sava\u015fa sona verdi. <hl>", "example_title": "Answer Extraction (History)"}, {"text": "question: Bu model ne ise yarar? context: \u00c7al\u0131\u015fmada sunulan y\u00f6ntemle, T\u00fcrk\u00e7e metinlerden otomatik olarak soru ve cevap \u00fcretilebilir. Bu proje ile payla\u015f\u0131lan kaynak kodu ile T\u00fcrk\u00e7e Soru \u00dcretme / Soru Cevaplama konular\u0131nda yeni akademik \u00e7al\u0131\u015fmalar yap\u0131labilir. Projenin detaylar\u0131na payla\u015f\u0131lan Github ve Arxiv linklerinden ula\u015f\u0131labilir.", "example_title": "Answer Extraction (Open Domain)"}]}
transformers pytorch mt5 text2text-generation question-generation answer-extraction question-answering text-generation tr dataset:tquad1 dataset:tquad2 dataset:xquad arxiv:2111.06476 license:cc-by-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# mt5-small for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.





## Overview ️
Language model: mt5-small  
Language: Turkish  
Downstream-task: Extractive QA/QG, Answer Extraction  
Training data: TQuADv2-train
Code:  URL  
Paper:  URL 

## Hyperparameters
 

## Performance
Refer to paper.

## Usage 


# mt5-small for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI. ## Overview ️
Language model: mt5-small  
Language: Turkish  
Downstream-task: Extractive QA/QG, Answer Extraction  
Training data: TQuADv2-train
Code:  URL  
Paper:  URL ## Hyperparameters ## Performance
Refer to paper. ## Usage
TAGS
#transformers #pytorch #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # mt5-small for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI. ## Overview ️
Language model: mt5-small  
Language: Turkish  
Downstream-task: Extractive QA/QG, Answer Extraction  
Training data: TQuADv2-train
Code:  URL  
Paper:  URL ## Hyperparameters ## Performance
Refer to paper. ## Usage
passage: TAGS
#transformers #pytorch #mt5 #text2text-generation #question-generation #answer-extraction #question-answering #text-generation #tr #dataset-tquad1 #dataset-tquad2 #dataset-xquad #arxiv-2111.06476 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# mt5-small for Turkish Question Generation
Automated question generation and question answering using text-to-text transformers by OBSS AI.## Overview ️
Language model: mt5-small  
Language: Turkish  
Downstream-task: Extractive QA/QG, Answer Extraction  
Training data: TQuADv2-train
Code:  URL  
Paper:  URL## Hyperparameters## Performance
Refer to paper.## Usage