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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)
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