MTEB: Massive Text Embedding Benchmark
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Updated
Jun 28, 2024 - Python
MTEB: Massive Text Embedding Benchmark
Embeddings from sentence-transformers in Android! Uses all-MiniLM-L6-V2 model currently
Content Optimization code for Health Hub Articles
A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.
This repository is dedicated to exploring and implementing vector-based retrieval methods and reranking algorithms. It includes Jupyter notebooks with practical examples and code snippets that demonstrate how these techniques can be applied for efficient information retrieval in various datasets.
Web Application to retrieve relevant sentences from PDF files using sentence transformers
Backend for the AI-copilot
AI powered ytp/sentence mixing for audio and video.
Naive RAG implementation using LangChain + OpenAI GPT 3.5 + Sentence_Transformer + FAISS
Find Python Packages on PyPI with the help of vector embeddings
Advanced algorithms for a grocery coupon recommendation system that increase customer engagement and revenue through personalized product recommendations and strategic discounts.
The project's goal is to help job seekers understand the basic qualifications for specific jobs and evaluate the suitability of their skills for those positions. Additionally, the program aims to assist recruiters in enhancing their resume selection processes by analyzing and understanding job advertisements ....
Local-GenAI-Search is a generative search engine based on Llama 3, langchain and qdrant that answers questions based on your local files
A library to calculate similarity scores between two collections of text sequences encoded using transformer models for bitext mining, dense retrieval, retrieval-based classification, and retrieval-augmented generation (RAG).
MINERS ⛏️: The semantic retrieval benchmark for evaluating multilingual language models.
NLP Sentiment Classification Project
PL-MTEB: Polish Massive Text Embedding Benchmark
Movie Recommendation System using LLMs: Performed similarity search using Weaviate and LangChain on vector embeddings generated by Sentence Transformer. Summarized movie using Phi-3.
A convenient way to link, deduplicate, aggregate and cluster data(frames) in Python using deep learning
LLM is a very powerful tool. It often performs more than required (hallucinations) and may tend to generate output in a pattern it finds best. We need RAG to harness the power of LLM in a controlled manner. In this work we implement a simple RAG system with Codegemma and an in-memory Vector Database.
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