Create README.md

README.md

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+---
+
+license: apache-2.0
+inference: false
+
+---
+```
+from transformers import AutoTokenizer, AutoModelWithLMHead 
+
+import torch
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else :
+    device = "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained("salesken/paraphrase_generation")  
+model = AutoModelWithLMHead.from_pretrained("salesken/paraphrase_generation").to(device)
+
+input_query="every moment is a fresh beginning"
+query= input_query + " ~~ "
+
+input_ids = tokenizer.encode(query.lower(), return_tensors='pt').to(device)
+sample_outputs = model.generate(input_ids,
+                                do_sample=True,
+                                num_beams=1, 
+                                max_length=128,
+                                temperature=0.9,
+                                top_n= 0.99,
+                                top_k = 30,
+                                num_return_sequences=40)
+paraphrases = []
+for i in range(len(sample_outputs)):
+    r = tokenizer.decode(sample_outputs[i], skip_special_tokens=True).split('||')[0]
+    r = r.split(' ~~ ')[1]
+    if r not in paraphrases:
+        paraphrases.append(r)
+
+print(paraphrases)
+
+
+```
+
+
+To evaluate if a paraphrase is a semantic variation to the input query or just a surface level variation & rank the generated paraphrases, use the following model:
+
+https://huggingface.co/salesken/paraphrase_diversity_ranker
+
+