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mt5-base for Turkish Question Generation

Automated question generation and question answering using text-to-text transformers by OBSS AI.

from core.api import GenerationAPI
generation_api = GenerationAPI('mt5-base-3task-highlight-combined3')

Citation 📜

@article{akyon2021automated,
  title={Automated question generation and question answering from Turkish texts using text-to-text transformers},
  author={Akyon, Fatih Cagatay and Cavusoglu, Devrim and Cengiz, Cemil and Altinuc, Sinan Onur and Temizel, Alptekin},
  journal={arXiv preprint arXiv:2111.06476},
  year={2021}
}

Overview ✔️

Language model: mt5-base
Language: Turkish
Downstream-task: Extractive QA/QG, Answer Extraction
Training data: TQuADv2-train, TQuADv2-val, XQuAD.tr
Code: https://github.com/obss/turkish-question-generation
Paper: https://arxiv.org/abs/2111.06476

Hyperparameters

batch_size = 256
n_epochs = 15
base_LM_model = "mt5-base"
max_source_length = 512
max_target_length = 64
learning_rate = 1.0e-3
task_lisst = ["qa", "qg", "ans_ext"]
qg_format = "highlight"

Performance

Refer to paper.

Usage 🔥

from core.api import GenerationAPI

generation_api = GenerationAPI('mt5-base-3task-highlight-combined3')

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)
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Dataset used to train obss/mt5-base-3task-highlight-combined3