#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

#python #deep_learning #pre_trained #model #awesome #nlp #vision #paddlehub #ai_models

https://github.com/PaddlePaddle/PaddleHub

#python #ai #deep_learning #natural_language_processing #natural_language_understanding #nlp #nlp_library #nlp_machine_learning #pre_trained_language_models #pre_trained_model #pytorch #transformer

https://github.com/thunlp/OpenPrompt

#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

#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