Generation Co-expression Network Embeddings (CxNEs) for plant genes using Graph Attention Networks (GAT))
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Updated
Sep 30, 2024 - Python
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.
[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
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.
PandaChat-RAG benchmark for evaluation of RAG systems on a non-synthetic Slovenian test dataset.
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.
⚡️Framework for fast persistent storage of multiple document embeddings and metadata into Pinecone for source-traceable, production-level RAG.
Neural Code Comprehension: A Learnable Representation of Code Semantics
Unstract's interface to LLMs, Embeddings and VectorDBs.
Topic modeling and document clustering
Web-ify your word2vec: framework to serve distributional semantic models online
Hindi-Text-summarization-major project
M3E-Embedder 是一个基于 Docker 的服务,旨在方便地部署和运行 m3e embedding嵌入模型,支持多种嵌入模型快速集成和高效计算。
Testing Embedding Server (Compatible OpenAI API). model from LLaMa/Mistral
Semantic product search on Databricks
GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embeddings
A tool that converts long audio files into a thorough, summarized report. Leverages OpenAI and its API (ChatGPT backend), Langchain for text processing, and Pinecone for vector database facilitation.
Toolkit designed for developers to evaluate, select, and deploy embedding models. It streamlines the lifecycle from model evaluation to data embedding and querying.
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