BEIR
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About
BEIR (Benchmarking Information Retrieval) is a heterogeneous benchmark designed to facilitate the evaluation of Information Retrieval (IR) models across over 15 diverse IR datasets. It allows users to easily preprocess and evaluate their own models in a common framework, supporting various retrieval architectures such as lexical, dense, sparse, and reranking. BEIR provides well-defined evaluation metrics, making it beneficial for both academia and industry. The tool emphasizes reproducibility and offers a variety of additional resources and datasets for users interested in advancing their information retrieval capabilities.
Platform
Task
Features
• easy to preprocess own ir datasets
• includes 17 preprocessed benchmark datasets
• covers a variety of retrieval tasks
• supports evaluation of custom models
• provides well-known retrieval architectures
FAQs
What is BEIR?
BEIR is a heterogeneous benchmark for evaluating Information Retrieval models across diverse datasets.
How can I evaluate my models using BEIR?
To evaluate your models, you can preprocess your datasets and use the provided evaluation metrics in a common framework.
What datasets are included in BEIR?
BEIR includes 17 preprocessed benchmark datasets for evaluation.
Is there support for custom datasets?
Yes, users can preprocess their own datasets to work with BEIR.
What types of retrieval architectures does BEIR support?
BEIR supports various retrieval architectures including lexical, dense, sparse, and reranking.
Pricing Plans
Open Source
Free Plan• Access to 15+ diverse IR datasets
• Evaluation metrics for retrieval models
• Support for various retrieval architectures
• Documentation and wiki for guidance
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