CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Repository Index
47,133 repositories indexed
1–17 of 17 · Most stars
State-of-the-Art Embeddings, Retrieval, and Reranking
Topic Modelling for Humans
- gensim
- topic-modeling
- information-retrieval
- machine-learning
Pretrained models for TensorFlow.js
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
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
- community-detection
- graph-clustering
- deepwalk
- networkx
ProtTrans is providing state of the art pretrained language models for proteins. ProtTrans was trained on thousands of GPUs from Summit and hundreds of Google TPUs using Transformers Models.
Fast vectorization, topic modeling, distances and GloVe word embeddings in R.
- word2vec
- text-mining
- natural-language-processing
- glove
🚀 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.
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.