Beir

Visit Tool

beir is an open-source benchmark for information retrieval, providing an easy framework to evaluate NLP-based retrieval models across 15+ diverse IR datasets. It supports various retrieval architectures and evaluation metrics.

Claim this tool

1View

At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is beir?

BEIR (Benchmarking Information Retrieval) is an open-source, heterogeneous benchmark designed for evaluating NLP-based retrieval models across a wide array of information retrieval tasks. It offers a common and user-friendly framework for researchers and developers to assess their models against over 15 diverse IR datasets. The tool supports various retrieval architectures, including lexical, dense, sparse, and reranking-based models, and allows for the evaluation of custom models using state-of-the-art metrics like NDCG@k, MAP@K, Recall@K, Precision@K, and MRR. BEIR simplifies the preprocessing of IR datasets and integrates with popular platforms like Hugging Face, making it a valuable resource for both academia and industry.

Best used for

Ideal for researchers and professors who need to benchmark new information retrieval models, compare the performance of different NLP-based retrieval architectures, and conduct reproducible research. Especially valuable for those working with diverse IR datasets and requiring robust evaluation metrics.

Common actions

benchmark retrieval models

evaluate NLP models

analyze IR datasets

compare retrieval architectures

deepfakeworkflowslow-code/no-codeopen-sourceautomated workflowcollaboration"AI Agents"github copilotface swapping

Capabilities

Key features

Evaluate NLP retrieval models
15+ diverse IR datasets
Supports multiple retrieval architectures
State-of-the-art evaluation metrics
Preprocess IR datasets
Hugging Face integration

Target Audience

professorresearcher

Integrations

hugging-face

Pricing & Plans

Open Source

Free

FAQs

What types of retrieval models can be evaluated with BEIR?

BEIR supports the evaluation of various retrieval architectures, including lexical, dense, sparse, and reranking-based models. This allows for comprehensive benchmarking of different NLP-based retrieval approaches within a unified framework.

How many datasets are available for benchmarking in BEIR?

BEIR provides access to over 15 diverse information retrieval datasets, which can be used for evaluating models. These datasets cover a range of IR tasks, making the benchmark heterogeneous and suitable for varied research needs.

What evaluation metrics does BEIR support?

BEIR supports several state-of-the-art evaluation metrics for retrieval models, including NDCG@k, MAP@K, Recall@K, Precision@K, and MRR. These metrics provide a thorough assessment of model performance across different aspects of information retrieval.

Trending

Subcategories trending in Research & Education

Study Assistants Knowledge Management Course Creation Scientific Computing Summarization Language Learning

Trending

Explore

Browse AI tools by category

Content & Design Productivity & Business Coding & Development AI Agents & Automation Research & Education Wellness & Lifestyle Career Development Marketing & Growth Data & Analytics Customer Support & CX Finance E-commerce