# Numerical Reasoning

MatCha is a vision-language model focused on chart understanding and mathematical reasoning, enhancing processing capabilities through joint modeling of charts and language data

MatCha is a pre-trained model that enhances the ability of vision-language models to process chart and language data, excelling in chart question answering tasks

MatCha is a vision-language model based on the Pix2Struct architecture, specifically optimized for chart comprehension and numerical reasoning tasks, excelling in chart-based question answering.

TAPAS is a table-based question answering model that handles conversational QA tasks on tabular data through pre-training and fine-tuning.

TAPAS is a table question answering model based on the BERT architecture, pre-trained in a self-supervised manner on Wikipedia table data, supporting natural language question answering on table content

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.

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 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.

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

TAPAS is a BERT-like Transformer model designed for table-based question answering tasks. It is pre-trained in a self-supervised manner on English Wikipedia table corpora and fine-tuned on the WikiSQL 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.

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

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

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.

TAPAS is a QA model for tabular data. This tiny version is fine-tuned on the SQA dataset, suitable for table-based QA tasks in conversational scenarios.