Question Answering

Use Cases

Frequently Asked Questions

You can use Question Answering (QA) models to automate the response to frequently asked questions by using a knowledge base (documents) as context. Answers to customer questions can be drawn from those documents.

⚡⚡ If you’d like to save inference time, you can first use passage ranking models to see which document might contain the answer to the question and iterate over that document with the QA model instead.

Task Variants

There are different QA variants based on the inputs and outputs:

• Extractive QA: The model extracts the answer from a context. The context here could be a provided text, a table or even HTML! This is usually solved with BERT-like models.
• Open Generative QA: The model generates free text directly based on the context. You can learn more about the Text Generation task in its page.
• Closed Generative QA: In this case, no context is provided. The answer is completely generated by a model.

The schema above illustrates extractive, open book QA. The model takes a context and the question and extracts the answer from the given context.

You can also differentiate QA models depending on whether they are open-domain or closed-domain. Open-domain models are not restricted to a specific domain, while closed-domain models are restricted to a specific domain (e.g. legal, medical documents).

Inference

You can infer with QA models with the 🤗 Transformers library using the question-answering pipeline. If no model checkpoint is given, the pipeline will be initialized with distilbert-base-cased-distilled-squad. This pipeline takes a question and a context from which the answer will be extracted and returned.

from transformers import pipeline

qa_model = pipeline("question-answering")
question = "Where do I live?"
context = "My name is Merve and I live in İstanbul."
qa_model(question = question, context = context)
## {'answer': 'İstanbul', 'end': 39, 'score': 0.953, 'start': 31}

Useful Resources

Would you like to learn more about QA? Awesome! Here are some curated resources that you may find helpful!

• Course Chapter on Question Answering
• Question Answering Workshop
• How to Build an Open-Domain Question Answering System?
• Blog Post: ELI5 A Model for Open Domain Long Form Question Answering

Notebooks

• PyTorch
• TensorFlow

Scripts for training

• PyTorch
• TensorFlow
• Flax

Documentation

• Question answering task guide