# Information Extraction

Super Rainbow Cuckoo is a small-scale information extraction model based on the Next Token Extraction (NTE) paradigm, achieving efficient information extraction by mimicking the prediction methods of large language models.

Cuckoo is a small (300M parameters) information extraction model that efficiently extracts information by mimicking the next-word prediction paradigm of large language models

A model specifically designed for text summarization, capable of automatically extracting key information from input text and generating concise summaries

The information of this model is unknown and no specific introduction can be provided

A fine-tuned version of the microsoft/layoutlmv3-base model for document image classification tasks, achieving an F1 score of 0.8177 on the evaluation set

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

This is a text summarization model supporting Portuguese, suitable for generating concise text summaries.

A document understanding model fine-tuned on the nielsr/funsd-layoutlmv3 dataset based on microsoft/layoutlmv3-base

MarkupLM is a multimodal pretrained model designed for rich visual document understanding and information extraction tasks, combining text and markup language information.

A document understanding model fine-tuned on the FUNSD dataset based on Microsoft's LayoutLMv2

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

This is a BERT-based Turkish question-answering model, specifically designed for handling Turkish Q&A tasks.

Optimus is a summary generation model focused on extracting key information from text to produce concise summaries.