|
--- |
|
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} |
|
} |
|
``` |
