Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
- nlp
- pre-trained-model
- unilm
- minilm
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Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
TranslatedChinese LLaMA & Alpaca LLMs + local CPU/GPU training and deployment
PaddleFormers is an easy-to-use library of pre-trained large language model zoo based on PaddlePaddle.
The official GitHub page for the survey paper "A Survey of Large Language Models".
Silero VAD: pre-trained enterprise-grade Voice Activity Detector
Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.
GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
Official Implementation of OCR-free Document Understanding Transformer (Donut) and Synthetic Document Generator (SynthDoG), ECCV 2022
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
An Open-Source Framework for Prompt-Learning.
Pre-trained Deep Learning models and demos (high quality and extremely fast)
Bioinformatics'2020: BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Official Repository for the Uni-Mol Series Methods
High level network definitions with pre-trained weights in TensorFlow
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
BlueBERT, pre-trained on PubMed abstracts and clinical notes (MIMIC-III).
Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.