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