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--- |
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language: |
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- tr |
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tags: |
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- paraphrasing |
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- encoder-decoder |
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- seq2seq |
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- bert |
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--- |
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#Bert2Bert Turkish Paraphrase Generation |
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#INISTA 2021 |
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#Comparison of Turkish Paraphrase Generation Models |
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#Dataset |
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The dataset used in model training was created with the combination of the translation of the QQP dataset and manually generated dataset. |
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Dataset [Link](https://drive.google.com/file/d/1-2l9EwIzXZ7fUkNW1vdeF3lzQp2pygp_/view?usp=sharing) |
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#How To Use |
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```python |
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from transformers import BertTokenizerFast,EncoderDecoderModel |
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tokenizer=BertTokenizerFast.from_pretrained("dbmdz/bert-base-turkish-cased") |
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model = EncoderDecoderModel.from_pretrained("ahmetbagci/bert2bert-turkish-paraphrase-generation") |
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text="son model arabalar çevreye daha mı az zarar veriyor?" |
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input_ids = tokenizer(text, return_tensors="pt").input_ids |
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output_ids = model.generate(input_ids) |
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print(tokenizer.decode(output_ids[0], skip_special_tokens=True)) |
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#sample output |
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#son model arabalar çevre için daha az zararlı mı? |
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``` |
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#Cite |
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```bibtex |
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@inproceedings{bagci2021paraphrase, |
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title={Comparison of Turkish Paraphrase Generation Models}, |
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author={Ba{\u{g}}c{\i}, Ahmet and Amasyali, Mehmet Fatih}, |
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booktitle={2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)}, |
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year={2021}, |
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organization={IEEE} |
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} |
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``` |
