RankLLMCastorini
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TILDEielab
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About
RankLLM is a Python toolkit for reproducible information retrieval research using rerankers, with a focus on listwise reranking. It offers a suite of rerankers, pointwise models like MonoT5, pairwise models like DuoT5, and listwise models compatible with vLLM, SGLang, or TensorRT-LLM. Additionally, it supports RankGPT and RankGemini variants, which are proprietary listwise rerankers. It includes modules for retrieval, reranking, evaluation, and response analysis, facilitating end-to-end workflows. RankLLM integrates with Pyserini for retrieval and provides integrated evaluation for multi-stage pipelines. It also includes a module for detailed analysis of input prompts and LLM responses, addressing reliability concerns with LLM APIs and non-deterministic behavior in Mixture-of-Experts (MoE) models. The toolkit supports various backends, including SGLang and TensorRT-LLM, and is compatible with a wide range of LLMs.
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About
TILDE (Term Independent Likelihood moDEl) is a passage re-ranking and expansion framework built on BERT, designed to enhance retrieval performance by combining sparse term matching with deep contextual representations. The original TILDE model pre-computes term weights across the entire BERT vocabulary, which can lead to large index sizes. To address this, TILDEv2 introduces a more efficient approach by computing term weights only for terms present in expanded passages, resulting in indexes that are 99% smaller than those of the original TILDE. This efficiency is achieved by leveraging TILDE as a passage expansion model, where passages are expanded using top-k terms (e.g., top 200) to enrich their content. It provides scripts for indexing collections, re-ranking BM25 results, and training models using datasets like MS MARCO.
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Platforms Supported
Windows
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Mac
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Linux
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Cloud
Supported
On-Premises
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iPhone
Not Supported
iPad
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Android
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Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Academic researchers and developers seeking a solution offering tools for implementing and evaluating listwise reranking with large language models
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Audience
Academic researchers and developers searching for a tool to implement efficient and scalable passage re-ranking and expansion techniques
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Support
Phone Support
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24/7 Live Support
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Online
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Support
Phone Support
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24/7 Live Support
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Online
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API
Offers API
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API
Offers API
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Pricing
Free
Free Version
Supported
Free Trial
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Pricing
No information available.
Free Version
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Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
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Live Online
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In Person
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Training
Documentation
Supported
Webinars
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Live Online
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In Person
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Company InformationCastorini
Canada
github.com/castorini/rank_llm/
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Company Informationielab
United States
github.com/ielab/TILDE/tree/main
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Categories |
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Integrations
Python
Supported
Gemini
Supported
Gemini Enterprise
Supported
Hugging Face
Not Supported
Llama
Supported
Mistral AI
Supported
NVIDIA TensorRT
Supported
OpenAI
Supported
Qwen
Supported
RankGPT
Supported
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Integrations
Python
Supported
Gemini
Not Supported
Gemini Enterprise
Not Supported
Hugging Face
Supported
Llama
Not Supported
Mistral AI
Not Supported
NVIDIA TensorRT
Not Supported
OpenAI
Not Supported
Qwen
Not Supported
RankGPT
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