changed

README.md

@@ -1,38 +0,0 @@
-!pip install transformers
-!pip install spacy
-
-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))