metadata
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
#How To Use
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
@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}
}