In the world of information retrieval, one common challenge is document reranking. It’s a powerful technique but can be intimidating for those unfamiliar with it. Trying a new reranking approach often means reworking your entire pipeline, even if the goal remains the same. This complexity can discourage experimentation and innovation in the field.

Existing solutions to this problem are limited. Many reranking methods are implemented in separate libraries with quirks and dependencies. This fragmented landscape makes it difficult for users to integrate different reranking models into their workflows seamlessly.

Meet Rerankers, a lightweight library designed to address these challenges. With rerankers, users can effortlessly incorporate various reranking methods into their pipelines using a unified API. The library aims to simplify the process of trying out different reranking models, empowering users to experiment freely without fear of disrupting their existing workflows.

One of the key advantages of rerankers is its simplicity. The library provides a straightforward API with just a handful of calls to learn. Regardless of the reranking model being used, users can expect consistent input/output formats, making it easy to swap between different methods.

Despite its lightweight nature, rerankers delivers impressive performance. Metrics demonstrate its capabilities in improving the relevance and ranking of search results. Whether it’s using standard SentenceTransformer models, T5-based pointwise rankers, or APIs like Cohere and Jina, rerankers consistently deliver reliable results.

In conclusion, rerankers offers a user-friendly solution to the challenges of document reranking. By providing a unified API and simplifying the integration process, it empowers users to explore and experiment with different reranking methods with ease. Rerankers’ focus on simplicity, flexibility, and performance makes it a catalyst for innovation in information retrieval.

Installation is very simple. The core package ships with just two dependencies, tqdm and pydantic, so as to avoid any conflict with your current environment. You may then install only the dependencies required by the models you want to try out:

# Core package only, will require other dependencies already installed
pip install rerankers

# All transformers-based approaches (cross-encoders, t5, colbert)
pip install “rerankers[transformers]”

# RankGPT
pip install “rerankers[gpt]”

# API-based rerankers (Cohere, Jina, soon MixedBread)
pip install “rerankers[api]”

# All of the above
pip install “rerankers[all]”

Document reranking is powerful, but daunting to get started with. Moreover, trying a new approach requires modifying your pipeline, even though it does the same thing!Introducing rerankers: a lightweight library to provide a unified way to use various reranking methods1/? pic.twitter.com/FbclPwnXLF— Benjamin Clavié (@bclavie) March 14, 2024

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