# Document QA

Donut is a Transformer-based document understanding model specifically designed for document question-answering tasks.

CogVLM2 is a multimodal dialogue model based on Meta-Llama-3-8B-Instruct, supporting both Chinese and English, with 8K context length and 1344*1344 resolution image processing capabilities.

This model is a document visual question answering (VQA) specialized model based on Microsoft's LayoutLMv2 architecture, fine-tuned for document understanding tasks

A document QA model fine-tuned based on microsoft/layoutlmv2-base-uncased, suitable for document layout understanding and QA tasks

LiLT is a pre-trained model for Document Visual Question Answering (DocVQA) tasks, specifically designed for handling question-answering tasks in English documents.

This is a fine-tuned multimodal LayoutLM model for document question answering tasks, capable of understanding both text and layout information in documents to answer questions.

BERT-based model fine-tuned on Document Visual Question Answering (DocVQA) tasks

A document visual question answering model based on the LayoutLMv2 architecture, fine-tuned for document understanding tasks