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--- |
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language: "en" |
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tags: |
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- paraphrase-generation |
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- text-generation |
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- Conditional Generation |
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inference: false |
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--- |
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# Paraphrase-Generation |
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## Model description |
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T5 Model for generating paraphrases of english sentences. Trained on the [Google PAWS](https://github.com/google-research-datasets/paws) dataset. |
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## How to use |
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## Requires sentencepiece: # !pip install sentencepiece |
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PyTorch and TF models available |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws") |
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model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws").to('cuda') |
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sentence = "This is something which i cannot understand at all" |
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text = "paraphrase: " + sentence + " </s>" |
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encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt") |
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input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda") |
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outputs = model.generate( |
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input_ids=input_ids, attention_mask=attention_masks, |
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max_length=256, |
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do_sample=True, |
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top_k=120, |
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top_p=0.95, |
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early_stopping=True, |
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num_return_sequences=5 |
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) |
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for output in outputs: |
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line = tokenizer.decode(output, skip_special_tokens=True,clean_up_tokenization_spaces=True) |
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print(line) |
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``` |
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For more reference on training your own T5 model or using this model, do check out [Paraphrase Generation](https://github.com/Vamsi995/Paraphrase-Generator). |
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