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| import streamlit as st | |
| import transformers | |
| from transformers import pipeline | |
| from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline | |
| model_name = 'deepset/xlm-roberta-large-squad2' | |
| model = AutoModelForQuestionAnswering.from_pretrained(model_name) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| # a) Get predictions | |
| ctx = st.text_area('Context') | |
| if ctx: | |
| q = st.text_area('Ask your question :)') | |
| nlp = pipeline('question-answering', model=model_name, tokenizer=model_name) | |
| #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(context=ctx, question=q) | |
| st.json(res) | |
| #from transformers import pipeline | |
| #model_name = "deepset/xlm-roberta-large-squad2" | |
| #qa_pl = pipeline('question-answering', model=model_name, tokenizer=model_name, device=0) | |
| #predictions = [] | |
| # batches might be faster | |
| #ctx = st.text_area('Gib context') | |
| #q = st.text_area('Gib question') | |
| #if context: | |
| # result = qa_pl(context=ctx, question=q) | |
| # st.json(result["answer"]) | |
| #for ctx, q in test_df[["context", "question"]].to_numpy(): | |
| # result = qa_pl(context=ctx, question=q) | |
| # predictions.append(result["answer"]) | |
| #model = AutoModelForQuestionAnswering.from_pretrained(model_name) | |
| #tokenizer = AutoTokenizer.from_pretrained(model_name) | |