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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): | |
| prompt = "context: {} answer: {}".format(sentence,answer) | |
| print (prompt) | |
| 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 | |
| Text = "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. You may also paste or write a short answer, preferably a noun or noun phrase. Submit and the machine will attempt to generate a coherent question." | |
| Text = 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") | |
| examples = [ | |
| ["""Fears of a new Covid-19 cluster linked to a hotpot restaurant have surfaced amid Hong Kong’s Omicron-fuelled fifth wave, while infections tied to an investment bank continued to expand, triggering the evacuation of residents in a building after vertical transmission of the virus was detected. | |
| On Wednesday, hundreds thronged Covid-19 testing stations in Tuen Mun, with some residents complaining of long waiting times and chaotic arrangements. Authorities have deemed the district a high-risk area because of a higher number of infections. | |
| Health officials said sewage testing would be conducted in Tuen Mun to monitor the spread of the coronavirus, but a string of preliminary-positive cases detected across the city suggested a wider, more worrying situation. | |
| """, "a higher number of infections"], | |
| ["""Squid Game made history on Wednesday as the first non-English-language television series and the first Korean series to score a nomination for a Screen Actors Guild Award. | |
| The hit Netflix show, created by Hwang Dong-hyuk, is nominated for ensemble in a drama series alongside The Handmaid’s Tale, The Morning Show, Succession and Yellowstone. | |
| Squid Game stars Lee Jung-jae and Jung Ho-yeon also landed individual nominations for actor and actress in a drama series, respectively. | |
| """, "Yellowstone"] | |
| ] | |
| def generate_question(Text,Answer): | |
| return get_question(Text,Answer,question_model,question_tokenizer) | |
| iface = gr.Interface( | |
| fn=generate_question, | |
| inputs=[Text,Answer], | |
| outputs=question, title=title, description=description, examples=examples) | |
| iface.launch(debug=False) |