# BART Architecture

Tessar is an advanced table reasoning model developed by SVECTOR, based on groundbreaking research that continuously expands the boundaries of neural table understanding.

BART Large CNN is a pre-trained model based on the BART architecture, specifically designed for text summarization tasks.

A large-scale abstract generation model based on the BART architecture, specifically optimized for medical literature summarization tasks

A fine-tuned text summarization model based on the BART architecture, designed to generate concise and accurate summaries

A table summarization model based on the BART architecture, optimized for query-focused table summarization tasks.

MentalBART is a mental health analysis model fine-tuned based on facebook/bart-large, part of the MentaLLaMA project, focusing on explainable mental health analysis.

A text summarization model trained on the AutoTrain platform, utilizing the BART-Longformer architecture

This model is a fine-tuned version of led-base-16384 on the billsum dataset, specifically designed for long document summarization tasks.

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.

A question-answering model based on the BART-base architecture, fine-tuned on the SQuAD dataset, suitable for few-shot learning scenarios

A fine-tuned version of the facebook/bart-base model on the SQuAD dataset, suitable for question-answering tasks

CGRE is a Chinese end-to-end relation extraction model based on BART, employing a generative approach to achieve entity relation extraction, demonstrating excellent performance on multiple Chinese relation extraction datasets.

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

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

TAPEX is a table pre-training model that learns via neural SQL executor, based on the BART architecture, specifically designed for table reasoning 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.

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

An automatic summarization model for news articles based on mbart-large-50 architecture, fine-tuned using the MLSUM French news dataset

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

IndoBART-v2 is an Indonesian language model based on the BART architecture, specifically designed for natural language generation tasks.

TAPEX is a model that learns table reasoning capabilities through neural SQL executor training.

A speech processing model based on HuBERT and BART architecture, supporting Automatic Speech Recognition (ASR) tasks

A dialogue summarization model based on the BART-large architecture, specifically fine-tuned on the SAMSum dialogue dataset, capable of generating high-quality dialogue summaries.

A text detoxification model based on the BART architecture, capable of rewriting toxic text into neutral expressions

KeyBART is a pre-trained text generation model based on the BART architecture, specifically designed to generate concatenated keyphrase sequences in the CatSeqD format.