tapas-base-finetuned-wtq

Maintainer: google

Total Score

183

Last updated 5/28/2024

📉

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The tapas-base-finetuned-wtq model is a fine-tuned version of the TAPAS base model, pre-trained on a combination of tasks including SQuAD, WikiSQL, and the WikiTable Questions (WTQ) dataset. This model is designed for the task of table-based question answering, where the goal is to answer questions based on the content of a given table.

Model inputs and outputs

Inputs

  • Table: A relational table with headers and cell values
  • Question: A natural language question about the contents of the table

Outputs

  • Answer: The model generates a natural language answer to the input question, based on the information contained in the table.

Capabilities

The tapas-base-finetuned-wtq model can effectively answer questions about the contents of tables, leveraging its understanding of table structure and semantics. It is capable of handling a variety of table-based question types, including those that require reasoning across multiple cells or columns.

What can I use it for?

This model can be useful for building applications that involve question-answering over tabular data, such as customer support chatbots, business intelligence tools, or educational resources. By integrating this model, you can enable users to quickly find answers to their questions without needing to manually search through tables.

Things to try

One interesting aspect of the tapas-base-finetuned-wtq model is its ability to handle questions that require reasoning across multiple cells or columns of a table. Try experimenting with questions that reference different parts of the table, and observe how the model is able to understand the relationships between the various elements and provide a relevant answer.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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