There are 12 repositories under embedding-models topic.
A curated list of pretrained sentence and word embedding models
A curated list of awesome embedding models tutorials, projects and communities.
Generative Representational Instruction Tuning
Implementations of Embedding-based methods for Knowledge Base Completion tasks
Plugin that creates a ChromaDB vector database to work with LM Studio running in server mode!
Web-ify your word2vec: framework to serve distributional semantic models online
tensorflow prediction using c++ api
A client side vector search library that can embed, store, search, and cache vectors. Works on the browser and node. It outperforms OpenAI's text-embedding-ada-002 and is way faster than Pinecone and other VectorDBs.
ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms, including but not limited to click-through-rate (CTR) prediction, learning-to-ranking (LTR), and Matrix/Tensor Embedding. The project objective is to develop an ecosystem to experiment, share, reproduce, and deploy in real-world in a smooth and easy way.
Encoding position with the word embeddings.
langchain-chat is an AI-driven Q&A system that leverages OpenAI's GPT-4 model and FAISS for efficient document indexing. It loads and splits documents from websites or PDFs, remembers conversations, and provides accurate, context-aware answers based on the indexed data. Easy to set up and extend.
A monolingual and cross-lingual meta-embedding generation and evaluation framework
Generates a set of property-specific entity embeddings from knowledge graphs using node2vec
🐸 KERMIT - A lightweight library to encode and interpret Universal Syntactic Embeddings
STransE: a novel embedding model of entities and relationships in knowledge bases (NAACL 2016)
Learning node representation using edge semantics
PyTorch implementation of paper "Visual Concept-Metaconcept Learner", NeruIPS 2019
Code for paper: Learning to Build User-tag Profile in Recommendation System
Code and resources showcasing the Retrieval-Augmented Generation (RAG) technique, a solution for enhancing data freshness in Large Language Models (LLMs). Incorporate up-to-date external knowledge into LLM-generated responses. Additionally, this repository includes a Gradio-based user interface for seamless model deployment.
Semantic product search on Databricks
PetPS: Supporting Huge Embedding Models with Tiered Memory
GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embeddings
C++ and Python library for Polarizable Embedding
Supplementary materials for McLevey 2021 Doing Computational Social Science (Sage, UK).
Creation of an embedding space using unsupervised triplet loss and Tile2Vec that can be used for a variety of downstream tasks
Piecewise Flat Embedding for Image Segmentation