# Relative Position Embedding

A large-scale Wav2Vec2-Conformer speech recognition model using relative position embedding, fine-tuned on 100 hours of Librispeech data

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

BEiT is a Vision Transformer (ViT)-based image classification model, pre-trained in a self-supervised manner on ImageNet-22k and fine-tuned on the same dataset.

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.

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

BEiT is a vision Transformer-based model pre-trained on the ImageNet-21k dataset through self-supervised learning for image classification tasks.

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

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