Create README.md

README.md

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+!pip install transformers
+!pip install spacy
+!pip install gradio
+
+import gradio as gr
+import pandas as pd
+import os
+import time
+from transformers import pipeline, GPT2Tokenizer, OPTForCausalLM
+
+model=OPTForCausalLM.from_pretrained('pushkarraj/pushkar_OPT_paraphaser')
+tokenizer=GPT2Tokenizer.from_pretrained('pushkarraj/pushkar_OPT_paraphaser',truncation=True)
+
+generator=pipeline("text-generation",model=model,tokenizer=tokenizer,device=0)
+
+def cleaned_para(input_sentence):
+  p=generator('<s>'+input_sentence+ '</s>>>>><p>',do_sample=True,max_length=len(input_sentence.split(" "))+200,temperature = 0.8,repetition_penalty=1.2,top_p=0.4,top_k=1)
+  return p[0]['generated_text'].split('</s>>>>><p>')[1].split('</p>')[0]
+
+from __future__ import unicode_literals, print_function
+from spacy.lang.en import English # updated
+
+def sentensizer(raw_text):
+  nlp = English()
+  nlp.add_pipe("sentencizer") # updated
+  doc = nlp(raw_text)
+  sentences = [sent for sent in doc.sents]
+  print(sentences)
+  return sentences
+
+context = "Once, a group of frogs were roaming around the forest in search of water."
+text=context
+def paraphraser(text):
+  begin=time.time()
+  x=[cleaned_para(str(i)) for i in sentensizer(text)]
+  end=time.time()
+  return (".".join(x))
+
+print(paraphraser(text))