import streamlit as st from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline st.title('Question-Answering NLU') st.sidebar.title('Navigation') menu = st.sidebar.radio("", options=["Introduction", "Parsing NLU data into SQuAD 2.0", "Generating Questions", "Training", "Evaluation"], index=0) if menu == "Introduction": st.markdown(''' Question Answering NLU (QANLU) is an approach that maps the NLU task into question answering, leveraging pre-trained question-answering models to perform well on few-shot settings. Instead of training an intent classifier or a slot tagger, for example, we can ask the model intent- and slot-related questions in natural language: ``` Context : I'm looking for a cheap flight to Boston. Question: Is the user looking to book a flight? Answer : Yes Question: Is the user asking about departure time? Answer : No Question: What price is the user looking for? Answer : cheap Question: Where is the user flying from? Answer : (empty) ``` Thus, by asking questions for each intent and slot in natural language, we can effectively construct an NLU hypothesis. For more details, please read the paper: [Language model is all you need: Natural language understanding as question answering](https://assets.amazon.science/33/ea/800419b24a09876601d8ab99bfb9/language-model-is-all-you-need-natural-language-understanding-as-question-answering.pdf). In this Space, we will see how to transform [MATIS++](https://github.com/amazon-research/multiatis) NLU data (e.g. utterances and intent / slot annotations) into [SQuAD 2.0 format](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) question-answering data that can be used by QANLU. MATIS++ includes the original English version of ATIS and a translation into eight languages: German, Spanish, French, Japanese, Hindi, Portuguese, Turkish, and Chinese. ''') elif menu == "Evaluation": st.header('QANLU Evaluation') tokenizer = AutoTokenizer.from_pretrained("AmazonScience/qanlu", use_auth_token=True) model = AutoModelForQuestionAnswering.from_pretrained("AmazonScience/qanlu", use_auth_token=True) qa_pipeline = pipeline('question-answering', model=model, tokenizer=tokenizer) context = st.text_input( 'Please enter the context:', value="I want a cheap flight to Boston." ) question = st.text_input( 'Please enter the question:', value="What is the destination?" ) qa_input = { 'context': 'Yes. No. ' + context, 'question': question } if st.button('Ask QANLU'): answer = qa_pipeline(qa_input) st.write(answer)