Create pipe.txt

pipe.txt

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+"""
+from transformers import pipeline
+
+x = st.slider('Select a value')
+st.write(x, 'squared is', x * x)
+
+question_answerer = pipeline("question-answering")
+
+context = r" Extractive Question Answering is the task of extracting an answer from a text given a question. 
+An example of a question answering dataset is the SQuAD dataset, which is entirely based on that task. 
+If you would like to fine-tune a model on a SQuAD task, you may leverage the 
+examples/pytorch/question-answering/run_squad.py script."
+question = "What is extractive question answering?" #"What is a good example of a question answering dataset?"
+result = question_answerer(question=question, context=context)
+answer = result['answer']
+score = round(result['score'], 4)
+span = f"start: {result['start']}, end: {result['end']}"
+
+st.write(answer)
+st.write(f"score: {score}")
+st.write(f"span: {span}") 
+"""