Maintainer: google

Total Score


Last updated 5/28/2024


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


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


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


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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The tapas-large-finetuned-wtq is a large version of the TAPAS model, which was fine-tuned on the WikiTable Questions (WTQ) dataset. TAPAS is a BERT-like transformer model that was pretrained on a large corpus of English data from Wikipedia, with the goal of learning to understand and reason about tables. The tapas-large-finetuned-wtq model was first pretrained on masked language modeling (MLM) and an "intermediate pretraining" task, then fine-tuned sequentially on the SQA, WikiSQL, and finally the WTQ datasets. This allows the model to learn to effectively answer questions about the contents of tables. There are also smaller versions of the TAPAS model available, ranging from tapas-base-finetuned-wtq to tapas-tiny-finetuned-wtq, which trade off model size and performance. The tapas-large-finetuned-wtq model achieves the highest performance on the WTQ dataset, with a dev accuracy of 50.97%. Model inputs and outputs Inputs Question**: A natural language question about the contents of a table Table**: A tabular dataset, represented as a flattened sequence of tokens Outputs Answer**: The predicted answer to the input question, generated by the model Capabilities The tapas-large-finetuned-wtq model is capable of answering questions about the contents of tables, leveraging its pretraining on large corpora of tabular data and fine-tuning on datasets like WTQ. This allows it to understand the semantics of tables and extract relevant information to answer questions. For example, given a table about countries and their populations, the model could answer questions like "What is the population of China?" or "Which country has the largest population?". The model's strong performance on the WTQ benchmark demonstrates its ability to handle a wide range of table-based question answering tasks. What can I use it for? You can use the tapas-large-finetuned-wtq model for a variety of table-based question answering applications. Some potential use cases include: Building intelligent search or question-answering systems that can understand and reason about tabular data, such as financial reports, scientific datasets, or product information. Enhancing business intelligence and data analysis tools by allowing users to query tables using natural language. Developing educational or tutoring applications that can help students learn by answering questions about data presented in tables. The model could also be fine-tuned further on domain-specific datasets to adapt it to particular applications or industries. Things to try One interesting thing to try with the tapas-large-finetuned-wtq model is to explore how it handles different types of tables and questions. For example, you could try feeding it tables with varying structures (e.g., wide vs. tall, sparse vs. dense) and see how its performance changes. You could also experiment with different types of questions, such as those requiring numerical reasoning, aggregation, or multi-hop inference. Additionally, you could try comparing the performance of the different TAPAS model sizes (tapas-base-finetuned-wtq, tapas-medium-finetuned-wtq, etc.) to see how the trade-off between model size and accuracy plays out for your particular use case.

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