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Tasks

Question Answering

Question Answering models can retrieve the answer to a question from a given text, which is useful for searching for an answer in a document. Some question answering models can generate answers without context!

Inputs
Question

Which name is also used to describe the Amazon rainforest in English?

Context

The Amazon rainforest, also known in English as Amazonia or the Amazon Jungle

Question Answering Model
Output
Answer

Amazonia

About 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!

Notebooks

Scripts for training

Documentation

Available in Deploy on Inference Endpoints

Compatible libraries

Adapter Transformers allenNLP Transformers Transformers.js
Question Answering demo
using deepset/roberta-base-squad2
Question Answering
Examples
This model can be loaded on the Inference API on-demand.
Models for Question Answering
Browse Models (6,815)

Note A robust baseline model for most question answering domains.

Datasets for Question Answering
Browse Datasets (866)

Note A famous question answering dataset based on English articles from Wikipedia.

Note A dataset of aggregated anonymized actual queries issued to the Google search engine.

Spaces using Question Answering
🌖
deepset/wikipedia-assistant

Note An application that can answer a long question from Wikipedia.

Metrics for Question Answering
exact-match
Exact Match is a metric based on the strict character match of the predicted answer and the right answer. For answers predicted correctly, the Exact Match will be 1. Even if only one character is different, Exact Match will be 0
f1
The F1-Score metric is useful if we value both false positives and false negatives equally. The F1-Score is calculated on each word in the predicted sequence against the correct answer