#python #document_understanding #language_generation #language_understanding #layoutlm #minilm #nlp #pre_trained_model #s2s_ft #small_pre_trained_model #unilm

https://github.com/microsoft/unilm

#typescript #document_object_model #dom #dom_api #dom_manipulation #html #javascript

https://github.com/phuoc-ng/html-dom

#javascript #api #document #documentation_tool #showdoc

https://github.com/star7th/showdoc

#python #computer_vision #deep_learning #detectron2 #document_image_analysis #document_image_processing #document_layout_analysis #layout_analysis #layout_parser #object_detection #ocr

https://github.com/Layout-Parser/layout-parser

#python #data_mining #data_science #document_similarity #fasttext #gensim #information_retrieval #machine_learning #natural_language_processing #neural_network #nlp #topic_modeling #word_embeddings #word_similarity #word2vec

https://github.com/RaRe-Technologies/gensim

#python #bert #document_embedding #pre_trained_language_models #semantic_search #sentence_encoder #sentence_transformers #text_search #text_semantic_similarity #top2vec #topic_modeling #topic_modelling #topic_search #topic_vector #word_embeddings

https://github.com/ddangelov/Top2Vec

#rust #backend_as_a_service #cloud_database #collaborative #database #database_as_a_service #developer_tools #devtools #distributed #distributed_database #document_database #graph_database #hacktoberfest #iot_database #nosql #realtime_database #serverless #sql #surreal #surrealdb #web

https://github.com/surrealdb/surrealdb

#html #data_pipelines #deep_learning #document_ai #document_image_analysis #document_image_processing #document_parser #document_parsing #docx #donut #information_retrieval #langchain #machine_learning #ml #natural_language_processing #nlp #ocr #pdf #pdf_to_json #pdf_to_text #preprocessing

https://github.com/Unstructured-IO/unstructured

#python #document_retrieval #embeddings #llms

https://github.com/chroma-core/chroma

#go #database #document #ferretdb #golang #mongo #mongo_db #mongodb #mongodb_database #postgres #postgresql

https://github.com/FerretDB/FerretDB

#python #document_ai #document_image_analysis #document_layout_analysis #document_parser #document_understanding #layoutlm #nlp #ocr #publaynet #pubtabnet #pytorch #table_detection #table_recognition #tensorflow

https://github.com/deepdoctection/deepdoctection

#cplusplus #artificial_intelligence #computer_vision #document #document_analysis #document_intelligence #document_recognition #document_understanding #documentai #end_to_end_ocr #multimodal #multimodal_deep_learning #ocr #scene_text_detection #scene_text_detection_recognition #scene_text_recognition #text_detection #text_recognition #vision_language #vision_language_model #vision_language_transformer

https://github.com/AlibabaResearch/AdvancedLiterateMachinery

#python #beit #beit_3 #bitnet #deepnet #document_ai #foundation_models #kosmos #kosmos_1 #layoutlm #layoutxlm #llm #minilm #mllm #multimodal #nlp #pre_trained_model #textdiffuser #trocr #unilm #xlm_e

Microsoft is developing advanced AI models through large-scale self-supervised pre-training across various tasks, languages, and modalities. These models, such as Foundation Transformers (Magneto) and Kosmos-2.5, are designed to be highly generalizable and capable of handling multiple tasks like language understanding, vision, speech, and multimodal interactions. The benefit to users includes state-of-the-art performance in document AI, speech recognition, machine translation, and more, making these models highly versatile and efficient for a wide range of applications. Additionally, tools like TorchScale and Aggressive Decoding enhance stability, efficiency, and speed in model training and deployment.

https://github.com/microsoft/unilm

#python #agent #agents #ai_search #chatbot #chatgpt #data_pipelines #deep_learning #document_parser #document_understanding #genai #graph #graphrag #llm #nlp #pdf_to_text #preprocessing #rag #retrieval_augmented_generation #table_structure_recognition #text2sql

RAGFlow is an open-source tool that helps businesses answer questions accurately using large language models and deep document understanding. It extracts information from various complex data formats, such as Word documents, Excel files, and web pages, and provides grounded citations to support its answers. You can try a demo online or set it up on your own server using Docker. The setup is relatively straightforward, requiring a few steps like cloning the repository, building the Docker image, and configuring the system settings. RAGFlow offers key features like template-based chunking, reduced hallucinations, and compatibility with multiple data sources, making it a powerful tool for truthful question-answering capabilities. This benefits users by providing reliable and explainable answers, streamlining their workflow, and supporting integration with their business systems.

https://github.com/infiniflow/ragflow