asgeirtj/system_prompts_leaks
This repository provides a collection of extracted system prompts from various large language models (LLMs) including Anthropic's Claude, OpenAI's ChatGPT, Google's Gemini, and xAI's Grok, updated ...
Awesome AI Agents › Prompt Libraries
DataScienceUIBK/Rankify
Rankify is a comprehensive Python toolkit designed for unified retrieval, re-ranking, and retrieval-augmented generation (RAG) research. It integrates 40 pre-retrieved benchmark datasets and supports over 7 retrieval techniques, 24 state-of-the-art re-ranking models, and multiple RAG methods. The toolkit provides a modular and extensible framework that enables seamless experimentation and benchmarking across retrieval pipelines, making it a powerful resource for researchers and practitioners in information retrieval and natural language processing. Rankify offers pre-retrieved datasets with 1,000 documents per dataset, accessible through Hugging Face repositories, and supports various retrievers such as BM25, DPR, ANCE, ColBERT, and MSS-DPR. It also includes re-ranking models like FirstModelReranker, LiT5ScoreReranker, VicunaReranker, and ZephyrReranker, among others. The toolkit is designed to be efficient and modular, supporting GPU acceleration for optimized evaluation metrics. Installation is straightforward via pip, with options for basic, recommended, or component-specific installations. The project includes comprehensive documentation, demo applications using Streamlit, and encourages community contributions. It is licensed under Apache-2.0 and actively maintained with regular updates and releases. Overall, Rankify aims to facilitate advanced research and development in retrieval and RAG by providing a unified, extensible, and easy-to-use platform.
https://github.com/DataScienceUIBK/Rankify
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