Create README.md

README.md

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+---
+language: tr
+datasets:
+- TQUAD
+pipeline_tag: 
+- question-answering
+- question-generation
+- multitask-model
+
+license: apache-2.0
+---
+
+# mT5-small based Turkish Multitask (Answer Extraction, Question Generation and Question Answering) System
+
+[Google's Multilingual T5-small](https://github.com/google-research/multilingual-t5) is fine-tuned on [Turkish Question Answering dataset](https://github.com/okanvk/Turkish-Reading-Comprehension-Question-Answering-Dataset) for three downstream task **Answer extraction, Question Generation and Question Answering** served in this single model. mT5 model was also trained for multiple text2text NLP tasks. 
+
+All data processing, training and pipeline codes can be found on my [Github](https://github.com/ozcangundes/multitask-question-generation). I will share the training details in the repo as soon as possible. 
+
+mT5 small model has 300 million parameters and model size is about 1.2GB. Therefore, it takes significant amount of time to fine tune it. 8 epoch and 1e-4 learning rate with 0 warmup steps was applied during training. These hparams can be fine-tuned for much more better results.
+
+## Requirements ❗❗❗
+```
+!pip install transformers==4.4.2
+!pip install sentencepiece==0.1.95
+!git clone https://github.com/ozcangundes/multitask-question-generation.git
+%cd multitask-question-generation/
+```
+
+## Usage 🚀🚀
+```
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+tokenizer = AutoTokenizer.from_pretrained("ozcangundes/mt5-multitask-qa-qg-turkish") 
+model = AutoModelForSeq2SeqLM.from_pretrained("ozcangundes/mt5-multitask-qa-qg-turkish")
+
+from pipelines import pipeline #pipelines.py script in the cloned repo
+multimodel = pipeline("multitask-qa-qg",tokenizer=tokenizer,model=model)
+
+#sample text
+text="Özcan Gündeş, 1993 yılı Tarsus doğumludur. Orta Doğu Teknik Üniversitesi \
+Endüstri Mühendisliği bölümünde 2011 2016 yılları arasında lisans eğitimi görmüştür. \
+Yüksek lisansını ise 2020 Aralık ayında, 4.00 genel not ortalaması ile \
+Boğaziçi Üniversitesi, Yönetim Bilişim Sistemleri bölümünde tamamlamıştır.\
+Futbolla yakından ilgilenmekle birlikte, Galatasaray kulübü taraftarıdır."
+```
+
+## Example - Both Question Generation and Question Answering 💬💬
+```
+multimodel(text)
+
+#output
+=> [{'answer': 'Tarsus', 'question': 'Özcan Gündeş nerede doğmuştur?'},
+ {'answer': '1993', 'question': 'Özcan Gündeş kaç yılında doğmuştur?'},
+ {'answer': '2011 2016',
+  'question': 'Özcan Gündeş lisans eğitimini hangi yıllar arasında tamamlamıştır?'},
+ {'answer': 'Boğaziçi Üniversitesi, Yönetim Bilişim Sistemleri',
+  'question': 'Özcan Gündeş yüksek lisansını hangi bölümde tamamlamıştır?'},
+ {'answer': 'Galatasaray kulübü',
+  'question': 'Özcan Gündeş futbolla yakından ilgilenmekle birlikte hangi kulübü taraftarıdır?'}]
+```
+From this text, 5 questions are generated and they are answered by the model. 
+
+## Example - Question Answering 💭💭
+
+Both text and also, related question should be passed into pipeline.
+```
+multimodel({"context":text,"question":"Özcan hangi takımı tutmaktadır?"})
+
+#output
+=> Galatasaray
+
+multimodel({"context":text,"question":"Özcan, yüksek lisanstan ne zaman mezun oldu?"})
+
+#output
+=> 2020 Aralık ayında
+
+
+multimodel({"context":text,"question":"Özcan'ın yüksek lisans bitirme notu kaçtır?"})
+
+#output
+=> 4.00 
+
+#Sorry for being cocky 😝😝
+```
+
+## ACKNOWLEDGEMENT
+
+This work is inspired from [Suraj Patil's great repo](https://github.com/patil-suraj/question_generation). I would like to thank him for the clean codes and also,[Okan Çiftçi](https://github.com/okanvk) for the Turkish dataset 🙏
+