State-of-the-Art Embeddings, Retrieval, and Reranking
Repository Index
57,138 visible repositories in the index
1–19 of 19 · 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
Retrieval and Retrieval-augmented LLMs
- embeddings
- information-retrieval
- llm
- sentence-embeddings
Translated100+ Chinese Word Vectors
- chinese
- chinese-word-segmentation
- embeddings
- word-embeddings
Source code for Twitter's Recommendation Algorithm
ImageBind One Embedding Space to Bind Them All
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
Translated:herb: Chinese synonyms: chatbot, intelligent question answering toolkit
Translatedtext2vec, text to vector. A text vector representation tool that converts text into vector matrices. It implements text representation and text similarity calculation models such as Word2Vec, RankBM25, Sentence-BERT, and CoSENT, and works out of the box.
- similarity
- nlp
- text-similarity
- text2vec
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
- community-detection
- graph-clustering
- deepwalk
- networkx
repository for Publicly Available Clinical BERT Embeddings
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.
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
UCE is a zero-shot foundation model for single-cell gene expression data
gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions