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import transformers | |
import sentencepiece | |
import torch | |
import numpy as np | |
from transformers import T5ForConditionalGeneration,T5Tokenizer | |
question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_squad_v1') | |
question_tokenizer = T5Tokenizer.from_pretrained('ramsrigouthamg/t5_squad_v1') | |
def get_question(sentence,answer,mdl,tknizer): | |
text = "context: {} answer: {}".format(sentence,answer) | |
print (text) | |
max_len = 256 | |
encoding = tknizer.encode_plus(text,max_length=max_len, pad_to_max_length=False,truncation=True, return_tensors="pt") | |
input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"] | |
outs = mdl.generate(input_ids=input_ids, | |
attention_mask=attention_mask, | |
early_stopping=True, | |
num_beams=5, | |
num_return_sequences=1, | |
no_repeat_ngram_size=2, | |
max_length=300) | |
dec = [tknizer.decode(ids,skip_special_tokens=True) for ids in outs] | |
Question = dec[0].replace("question:","") | |
Question= Question.strip() | |
return Question | |
context = "Elon Musk said that Tesla will not accept payments in Bitcoin because of environmental concerns." | |
answer = "Elon Musk" | |
ques = get_question(context,answer,question_model,question_tokenizer) | |
print ("question: ",ques) | |
import gradio as gr | |
title = "Question Generator Three" | |
description = "Paste or write a text. Provide a short answer or noun keywords. Submit and the machine will attempt to generate a coherent question" | |
context = gr.inputs.Textbox(lines=5, placeholder="Enter paragraph/context here...") | |
answer = gr.inputs.Textbox(lines=3, placeholder="Enter answer/keyword here...") | |
question = gr.outputs.Textbox( type="auto", label="Question") | |
def generate_question(context,answer): | |
return get_question(context,answer,question_model,question_tokenizer) | |
iface = gr.Interface( | |
fn=generate_question, | |
inputs=[context,answer], | |
outputs=question) | |
iface.launch(debug=False) |