readme: ---
  language: 
  - tr
  tags:
  - paraphrasing
  - encoder-decoder
  - seq2seq
  - bert
  ---
  
  #Bert2Bert Turkish Paraphrase Generation
  
  #INISTA 2021
  
  #Comparison of Turkish Paraphrase Generation Models
  
  #Dataset
  
  The dataset used in model training was created with the combination of the translation of the QQP dataset and manually generated dataset.
  Dataset [Link](https://drive.google.com/file/d/1-2l9EwIzXZ7fUkNW1vdeF3lzQp2pygp_/view?usp=sharing) 
  
  #How To Use
  ```python
  from transformers import BertTokenizerFast,EncoderDecoderModel
  tokenizer=BertTokenizerFast.from_pretrained("dbmdz/bert-base-turkish-cased")
  model = EncoderDecoderModel.from_pretrained("ahmetbagci/bert2bert-turkish-paraphrase-generation")
  
  text="son model arabalar çevreye daha mı az zarar veriyor?"
  input_ids = tokenizer(text, return_tensors="pt").input_ids
  output_ids = model.generate(input_ids)
  print(tokenizer.decode(output_ids[0], skip_special_tokens=True))
  #sample output
  #son model arabalar çevre için daha az zararlı mı?
  ```
  #Cite
  ```bibtex
  
  @INPROCEEDINGS{9548335,  
  author={Bağcı, Ahmet and Amasyali, Mehmet Fatih},  
  booktitle={2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)},   
  title={Comparison of Turkish Paraphrase Generation Models},   
  year={2021},  
  volume={},  
  number={},  
  pages={1-6},  
  doi={10.1109/INISTA52262.2021.9548335}
  }
  ```