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import re |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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import streamlit as st |
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@st.cache(allow_output_mutation=True) |
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model = AutoModelForSeq2SeqLM.from_pretrained("ramsrigouthamg/t5-large-paraphraser-diverse-high-quality") |
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tokenizer = AutoTokenizer.from_pretrained("ramsrigouthamg/t5-large-paraphraser-diverse-high-quality") |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = model.to(device) |
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def para(paragraph): |
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sen = [] |
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for i in paragraph: |
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res = len(re.findall(r'\w+', i)) |
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if res == 2: |
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pass |
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else: |
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res = i.replace('"', "'").replace("\n", "") |
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sen.append(res) |
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para = [] |
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for sentence in sen: |
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text = "paraphrase: " + sentence + " </s>" |
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encoding = tokenizer.encode_plus(text,max_length =1024, padding=True, return_tensors="pt") |
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input_ids,attention_mask = encoding["input_ids"].to(device), encoding["attention_mask"].to(device) |
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model.eval() |
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beam_outputs = model.generate( |
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input_ids=input_ids,attention_mask=attention_mask, |
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max_length=1024, |
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early_stopping=True, |
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num_beams=15, |
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num_return_sequences=3) |
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sent = tokenizer.decode(beam_outputs[2], skip_special_tokens=True,clean_up_tokenization_spaces=True) |
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para.append(sent) |
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paras = [] |
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for i in para: |
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resf = i.replace("paraphrasedoutput: ", "") |
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paras.append(resf) |
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return paras |
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