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| 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", "Training", | |
| "Evaluation"], index=0) | |
| if menu == "Demo": | |
| 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 an example | |
| NLU dataset (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. | |
| Feel free to query the pre-trained QA-NLU model using the buttons below. | |
| *Please note that this model has been trained on ATIS and may be need to be further fine-tuned to support intents and slots that are not covered in ATIS*. | |
| ''') | |
| tokenizer = AutoTokenizer.from_pretrained("AmazonScience/qanlu") | |
| model = AutoModelForQuestionAnswering.from_pretrained("AmazonScience/qanlu") | |
| qa_pipeline = pipeline('question-answering', model=model, tokenizer=tokenizer) | |
| context = st.text_input( | |
| 'Please enter the context (remember to include "Yes. No. " in the beginning):', | |
| value="Yes. No. I want a cheap flight to Boston." | |
| ) | |
| question = st.text_input( | |
| 'Please enter the intent question:', | |
| value="Are they looking for a flight?" | |
| ) | |
| qa_input = { | |
| 'context': context, | |
| 'question': question | |
| } | |
| if st.button('Ask QANLU'): | |
| answer = qa_pipeline(qa_input) | |
| st.write(answer) | |
| elif menu == "Parsing NLU data into SQuAD 2.0": | |
| st.header('QA-NLU Data Parsing') | |
| st.markdown(''' | |
| Here, we show a small example of how NLU data can be transformed into QANLU data. | |
| The same method can be used 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. | |
| Here is an example dataset with three intents and two examples per intent: | |
| ```` | |
| restaurant, I am looking for some Vietnamese food | |
| restaurant, What is there to eat around here? | |
| music, Play my workout playlist | |
| music, Can you find Bob Dylan songs? | |
| flight, Show me flights from Oakland to Dallas | |
| flight, I want two economy tickets from Miami to Chicago | |
| ```` | |
| Now, we need to define some questions, per intent. We can use free-form questions or use templates. | |
| ```` | |
| { | |
| 'restaurant': [ | |
| 'Did they ask for a restaurant?', | |
| 'Did they mention a restaurant?' | |
| ], | |
| 'music': [ | |
| 'Did they ask for music?', | |
| 'Do they want to play music?' | |
| ], | |
| 'flight': [ | |
| 'Did they ask for a flight?', | |
| 'Do they want to book a flight?' | |
| ] | |
| } | |
| ```` | |
| The next step is to run the `atis.py` script from the [QA-NLU Amazon Research repository](https://github.com/amazon-research/question-answering-nlu). | |
| That script will produce a json file that looks like this: | |
| ```` | |
| { | |
| "version": 1.0, | |
| "data": [ | |
| { | |
| "title": "MultiATIS++", | |
| "paragraphs": [ | |
| { | |
| "context": "yes. no. i am looking for some vietnamese food", | |
| "qas": [ | |
| { | |
| "question": "did they ask for a restaurant?", | |
| "id": "49f1180cb9ce4178a8a90f76c21f69b4", | |
| "is_impossible": false, | |
| "answers": [ | |
| { | |
| "text": "yes", | |
| "answer_start": 0 | |
| } | |
| ], | |
| "slot": "", | |
| "intent": "restaurant" | |
| }, | |
| { | |
| "question": "did they ask for music?", | |
| "id": "a7ffe039fb3e4843ae16d5a68194f45e", | |
| "is_impossible": false, | |
| "answers": [ | |
| { | |
| "text": "no", | |
| "answer_start": 5 | |
| } | |
| ], | |
| "slot": "", | |
| "intent": "restaurant" | |
| }, | |
| ... <More questions> | |
| ... <More paragraphs> | |
| ```` | |
| There are many tunable parameters when generating the above file, such as how many negative examples to include per question. Follow the same process for training a slot-tagging model. | |
| ''') | |
| elif menu == "Training": | |
| st.header('QA-NLU Training') | |
| st.markdown(''' | |
| To train a QA-NLU model on the data we created, we use the `run_squad.py` script from [huggingface](https://github.com/huggingface/transformers/blob/master/examples/legacy/question-answering/run_squad.py) and a SQuAD-trained QA model as our base. As an example, we can use `deepset/roberta-base-squad2` model from [here](https://huggingface.co/deepset/roberta-base-squad2) (assuming 8 GPUs are present): | |
| ''') | |
| st.code(''' | |
| mkdir models | |
| python -m torch.distributed.launch --nproc_per_node=8 run_squad.py \\ | |
| --model_type roberta \\ | |
| --model_name_or_path deepset/roberta-base-squad2 \\ | |
| --do_train \\ | |
| --do_eval \\ | |
| --do_lower_case \\ | |
| --train_file data/matis_en_train_squad.json \\ | |
| --predict_file data/matis_en_test_squad.json \\ | |
| --learning_rate 3e-5 \\ | |
| --num_train_epochs 2 \\ | |
| --max_seq_length 384 \\ | |
| --doc_stride 64 \\ | |
| --output_dir models/qanlu/ \\ | |
| --per_gpu_train_batch_size 8 \\ | |
| --overwrite_output_dir \\ | |
| --version_2_with_negative \\ | |
| --save_steps 100000 \\ | |
| --gradient_accumulation_steps 8 \\ | |
| --seed $RANDOM | |
| ''') | |
| elif menu == "Evaluation": | |
| st.header('QA-NLU Evaluation') | |
| st.markdown(''' | |
| To assess the performance of the trained model, we can use the `calculate_pr.py` script from the [QA-NLU Amazon Research repository](https://github.com/amazon-research/question-answering-nlu). | |
| Feel free to query the pre-trained QA-NLU model in the Demo section. | |
| ''') | |