CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Repository Index
59,621 visible repositories in the index
1–18 of 18 · Most stars
Topic Modelling for Humans
- gensim
- topic-modeling
- information-retrieval
- machine-learning
A very simple framework for state-of-the-art Natural Language Processing (NLP)
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
- python
- search
- nlp
- semantic-search
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
- bert
- sentence-encoding
- deep-learning
- clip-model
ImageBind One Embedding Space to Bind Them All
Postgres with GPUs for ML/AI apps.
- ml
- machine-learning
- ai
- ann
Train and evaluate embeddings for similarity search with PyTorch. Includes contrastive losses, hard example mining, samplers, and retrieval metrics.
- metric-learning
- deep-learning
- computer-vision
- machine-learning
Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.
- chinese
- computer-vision
- multi-modal-learning
- nlp
PyTorch code and models for V-JEPA self-supervised learning from video.
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
- community-detection
- graph-clustering
- deepwalk
- networkx
🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
- deep-learning
- protein-sequences
- protein-structure
- semi-supervised-learning
A simple deep learning library for estimating a set of tags and extracting semantic feature vectors from given illustrations.
Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.
- ai
- graphs
- synerise
- embeddings
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.
- artificial-intelligence
- clip
- pathology
- vision-and-language
This is a german ELMo deep contextualized word representation. It is trained on a special German Wikipedia Text Corpus.
- bilm
- elmo
- tensorflow
- machine-learning
gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions