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| from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline | |
| model_name = "deepset/roberta-base-squad2" | |
| # a) Get predictions | |
| nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) | |
| def main(): | |
| st.title("English to German") | |
| with st.form("text_field"): | |
| text = st.text_area('enter some english word:') | |
| # clicked==True only when the button is clicked | |
| clicked = st.form_submit_button("Submit") | |
| if clicked: | |
| results = classifier([text]) | |
| st.json(results) | |
| if __name__ == "__main__": | |
| main() | |
| QA_input = { | |
| 'question': 'Why is model conversion important?', | |
| 'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.' | |
| } | |
| res = nlp(QA_input) | |
| # b) Load model & tokenizer | |
| model = AutoModelForQuestionAnswering.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |