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import gradio as gr |
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import random |
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import torch |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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from question_generation import question_generation_sampling |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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g1_tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer") |
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g1_model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer") |
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g2_tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-race-Distractor") |
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g2_model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-race-Distractor") |
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g1_model.eval() |
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g2_model.eval() |
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g1_model.to(device) |
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g2_model.to(device) |
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def generate_multiple_choice_question( |
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context |
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): |
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num_questions = 1 |
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question_item = question_generation_sampling( |
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g1_model, g1_tokenizer, |
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g2_model, g2_tokenizer, |
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context, num_questions, device |
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)[0] |
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question = question_item['question'] |
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options = question_item['options'] |
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options[0] = f"{options[0]} [ANSWER]" |
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random.shuffle(options) |
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output_string = f"Question: {question}\n[A] {options[0]}\n[B] {options[1]}\n[C] {options[2]}\n[D] {options[3]}" |
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return output_string |
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demo = gr.Interface( |
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fn=generate_multiple_choice_question, |
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inputs=gr.Textbox(lines=8, placeholder="Context Here..."), |
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outputs=gr.Textbox(lines=5, placeholder="Question: \n[A] \n[B] \n[C] \n[D] "), |
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title="Multiple-choice Question Generator", |
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description="Provide some context (e.g. news article or any passage) in the context box and click **Submit**. The models currently support English only. This demo is a part of MQAG - https://github.com/potsawee/mqag0.", |
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allow_flagging='never' |
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) |
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demo.launch() |