Practical overview
When it fits
Use Beir when retrieval quality must be compared across multiple datasets through a common evaluation workflow, especially during model selection or pre-deployment testing.
Who it is for
- Information-retrieval researchers
- NLP and search engineers
- Teams comparing retrieval models before deployment
- Organizations building repeatable retrieval evaluations
Documented uses
- Evaluate retrieval models across diverse benchmark datasets.
- Compare lexical, dense, sparse and reranking approaches.
- Measure retrieval quality with standard metrics at selected result cutoffs.
- Save evaluation results and TREC runfiles for later comparison.
- Benchmark retrieval performance on domain-specific datasets.
Requirements & limits
The repository is publicly visible and documented as open source under the Apache-2.0 license. Installation is documented through pip or from a cloned source tree; Python 3.9 and later are stated as tested.
- It includes 17 preprocessed benchmark datasets according to the repository documentation.
- It evaluates lexical, dense, sparse and reranking-based retrieval architectures.
- Its example reports NDCG, MAP, Recall, Precision and MRR.
- It can save evaluation results and TREC runfiles.
- Custom-domain dataset support is indicated as likely compatible, but exact integration steps are not documented.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Beir and run the documented SciFact evaluation example
Set up Beir and evaluate a retrieval model on the SciFact test dataset.
Before you start
- Python 3.9 or later
- A Python environment in which Beir can be installed
- The Sentence-BERT example dependencies and model access documented by the repository
Steps
Install Beir with pip, or clone the repository and install it in editable mode.
Download and unzip the SciFact dataset with Beir's documented utility.
Load the corpus, queries and test relevance judgments with GenericDataLoader.
Run the documented Sentence-BERT dense-retrieval example and calculate NDCG, MAP, Recall, Precision and MRR.
Save the evaluation results and TREC runfile using the documented utility functions.
Expected outcome
A retrieval evaluation for SciFact with reported metric values and saved result files.
Guide limitations
- The recipe follows the supplied SciFact example and does not document how to integrate an arbitrary custom dataset.
- The supplied material does not provide a verified execution result.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 1 October 2026
- Original website title
- beir-cellar/beir · Official homepage title · retained in its source language