Table Question Answering

Table Question Answering (Table QA) is the answering a question about an information on a given table.

For more details about the table-question-answering task, check out its dedicated page! You will find examples and related materials.

microsoft/tapex-base: A table question answering model that is capable of neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table.
google/tapas-base-finetuned-wtq: A robust table question answering model.

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Headers
authorization	string	Authentication header in the form `'Bearer: hf_**'` when `hf_**` is a personal user access token with “Inference Providers” permission. You can generate one from your settings page.

Payload
inputs*	object	One (table, question) pair to answer
table*	object	The table to serve as context for the questions
question*	string	The question to be answered about the table
parameters	object
padding	enum	Possible values: do_not_pad, longest, max_length.
sequential	boolean	Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the inference to be done sequentially to extract relations within sequences, given their conversational nature.
truncation	boolean	Activates and controls truncation.

Body
(array)	object[]	Output is an array of objects.
answer	string	The answer of the question given the table. If there is an aggregator, the answer will be preceded by `AGGREGATOR >`.
coordinates	array[]	Coordinates of the cells of the answers.
cells	string[]	List of strings made up of the answer cell values.
aggregator	string	If the model has an aggregator, this returns the aggregator.

Inference Providers