A minimalist yet highly performant, lightweight, lightning fast, multisource, multimodal and local embedding solution, built in rust.

neural network model using TensorFlow and Keras to predict the rating user would give to a movie based on their past rating history. Utilized embeddings to encode high-cardinality categorical features.

This repository provides programs to build Retrieval Augmented Generation (RAG) code for Generative AI with LlamaIndex, Deep Lake, and Pinecone leveraging the power of OpenAI and Hugging Face models for generation and evaluation.

Generation Co-expression Network Embeddings (CxNEs) for plant genes using Graph Attention Networks (GAT))

Plugin that lets you use LM Studio to ask questions about your documents including audio and video files.

Generative Representational Instruction Tuning

[EMNLP 2024] This is the code for our paper "BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers".

Local file search using embedding techniques

Natural Language Processing(NLP) Toolbox

This project is an effort to characterize the sensor especially ultrasonic sensor using machine learning method. This later could be used in various application such as defective sensor detection.

Hackathon : This project compares machine learning models like Fasttext, LASER, Camembert, Multilang_Bert, and Croissant for retrieving similar text solutions using embeddings. It includes database setup, dataset structure, and usage instructions for evaluating results and interacting with models via a web interface.

한글 텍스트 임베딩 모델 리더보드

PandaChat-RAG benchmark for evaluation of RAG systems on a non-synthetic Slovenian test dataset.

DocChat: Langchain Retrieval System, seamlessly navigate and converse with your documents using Langchain-powered AI, transforming PDF content into actionable insights through natural language interactions.

C++ and Python library for Polarizable Embedding

Understand and build embedding models, focusing on word and sentence embeddings, dual encoder architectures. Learn to train embedding models using contrastive loss, implement them in semantic search and RAG systems.

Supplementary materials for McLevey 2021 Doing Computational Social Science (Sage, UK).

An open sourced approach to One-Shot Learning for Mouse Dynamics recognition in PyTorch. This includes tools for data preprocessing, training both classification and embedding models, and evaluating model performance on a Minecraft dataset.