Model card for Vamsi/T5_Paraphrase_Paws (#6037)

* Model card for Vamsi/T5_Paraphrase_Paws

* Update model_cards/Vamsi/T5_Paraphrase_Paws/README.md
Co-authored-by: Julien Chaumond <chaumond@gmail.com>

model_cards/Vamsi/T5_Paraphrase_Paws/README.md

+---
+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).