# Table Question Answering

A financial table question answering model based on the LayoutLM architecture, specifically designed to extract and answer structured questions from financial tables.

OmniTab is a table question answering model based on the BART architecture, achieving few-shot table QA capabilities through pre-training with natural and synthetic data

OmniTab is a table-based question answering model that achieves few-shot table QA capabilities through pre-training on natural and synthetic data.

TAPEX is a table pre-training model that learns through neural SQL executors, specifically designed for table question answering tasks.

TAPEX is a model designed for table reasoning tasks, learning through neural SQL executors for table pretraining, based on the BART architecture.

TAPEX is a table pre-training model learned through neural SQL executors, based on the BART architecture, specifically designed for table reasoning tasks.

TAPAS (Table Parser) is a table-based pre-trained language model developed by Google Research, specifically designed for processing tabular data and natural language queries.

This model is a small version of TAPAS, which has undergone intermediate pre-training and fine-tuning on the SQA dataset. It is suitable for table question answering tasks in dialogue scenarios.

TAPEX is a model designed for table question answering tasks, learning table pre-training through neural SQL executors and based on the BART architecture.

TAPEX-large is a large-scale language model pre-trained on tabular data, specifically fine-tuned for table question answering tasks. It achieves table understanding through a neural SQL executor and can answer natural language questions about table content.

TAPAS is a Transformer-based table question answering model pre-trained in a self-supervised manner on Wikipedia tables and associated text, supporting table understanding and question answering tasks.

TAPAS is a BERT-like model based on the Transformer architecture, specifically designed for processing tabular data and related text, pretrained in a self-supervised manner on Wikipedia table data.

TAPAS (Table Parsing) is a pre-trained language model developed by Google specifically for handling table-related tasks.

TAPAS is a table-based pretrained language model specifically designed for tasks related to tabular data.

TAPAS is a pre-trained language model based on tabular data, specifically designed for natural language tasks related to tables.

TAPAS is a Transformer-based table question answering model pre-trained in a self-supervised manner on English Wikipedia table data, supporting table QA and entailment tasks.

TAPAS is a Transformer-based table question answering model, pre-trained in a self-supervised manner on English Wikipedia table data and fine-tuned with supervision on the WikiSQL dataset.

TAPAS is a BERT-based Transformer model specifically designed for table question answering tasks. It is pre-trained in a self-supervised manner on English Wikipedia table data and supports weakly supervised table parsing.

TAPEX is a table pre-training model that learns through a neural SQL executor, capable of handling table reasoning tasks.

A table understanding model based on BERT architecture, pretrained on Wikipedia table data through self-supervised learning, supporting table question answering and statement verification tasks

A table question answering model based on BERT architecture, enhanced with intermediate pretraining for numerical reasoning, fine-tuned on the SQA dataset

A table-based question answering model based on the Transformer architecture, pretrained in a self-supervised manner on English Wikipedia tables and associated text

This model is the large version of TAPAS, fine-tuned for sequential question answering (SQA) tasks, suitable for table-related question answering scenarios.

The TAPAS mini model is a table question answering model that underwent intermediate pretraining and fine-tuning on the SQA dataset, utilizing relative position embedding technology.

TAPAS is a table-based pre-trained language model specifically designed for processing tabular data and related queries.

This model is a medium-sized table question answering model based on TAPAS architecture, fine-tuned on WikiTable Questions dataset, suitable for table data QA tasks.

TAPAS is a tiny Transformer model optimized for table question answering tasks, achieving table comprehension capabilities through intermediate pretraining and chained multi-dataset fine-tuning

This model is a small version of TAPAS, specifically fine-tuned on the WikiTable Questions dataset for table-based question answering tasks.

TAPAS is a BERT-like model based on the Transformer architecture, specifically designed for processing tabular data and related text. It is pre-trained through self-supervised learning on a massive collection of English Wikipedia tables and associated text.

TAPAS is a Transformer-based table question answering model, pre-trained on Wikipedia table data through self-supervised learning and fine-tuned on datasets like WTQ.

This model is a mini version based on the TAPAS architecture, specifically fine-tuned for the WikiTable Questions (WTQ) dataset for table question answering tasks.