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Update app.py
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app.py
CHANGED
@@ -11,8 +11,9 @@ model = T5ForConditionalGeneration.from_pretrained('Sehong/t5-large-QuestionGene
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text = "answer:Saint Bern ##ade ##tte So ##ubi ##rous content:Architectural ##ly , the school has a Catholic character . At ##op the Main Building ' s gold dome is a golden statue of the Virgin Mary . Immediately in front of the Main Building and facing it , is a copper statue of Christ with arms up ##rai ##sed with the legend "" V ##eni ##te Ad Me O ##m ##nes "" . Next to the Main Building is the Basilica of the Sacred Heart . Immediately behind the b ##asi ##lica is the G ##rot ##to , a Marian place of prayer and reflection . It is a replica of the g ##rot ##to at Lou ##rdes , France where the Virgin Mary reputed ##ly appeared to Saint Bern ##ade ##tte So ##ubi ##rous in 1858 . At the end of the main drive ( and in a direct line that connects through 3 statues and the Gold Dome ) , is a simple , modern stone statue of Mary ."
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'''
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raw_input_ids = tokenizer.encode(text)
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input_ids = [tokenizer.bos_token_id] + raw_input_ids + [tokenizer.eos_token_id]
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text = "answer:Saint Bern ##ade ##tte So ##ubi ##rous content:Architectural ##ly , the school has a Catholic character . At ##op the Main Building ' s gold dome is a golden statue of the Virgin Mary . Immediately in front of the Main Building and facing it , is a copper statue of Christ with arms up ##rai ##sed with the legend "" V ##eni ##te Ad Me O ##m ##nes "" . Next to the Main Building is the Basilica of the Sacred Heart . Immediately behind the b ##asi ##lica is the G ##rot ##to , a Marian place of prayer and reflection . It is a replica of the g ##rot ##to at Lou ##rdes , France where the Virgin Mary reputed ##ly appeared to Saint Bern ##ade ##tte So ##ubi ##rous in 1858 . At the end of the main drive ( and in a direct line that connects through 3 statues and the Gold Dome ) , is a simple , modern stone statue of Mary ."
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'''
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context = st.text_area('Enter Context')
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answer = st.text_area('Enter answer')
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text = "answer:{} content:{}".format(answer, context)
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raw_input_ids = tokenizer.encode(text)
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input_ids = [tokenizer.bos_token_id] + raw_input_ids + [tokenizer.eos_token_id]
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