{"categories":[{"label":"Text Processing","url":"https://skillfed.io/packages/category/text-processing/2"},{"label":"Indexing","url":"https://skillfed.io/packages/category/text-processing-indexing"}],"enrichment":{"capability":"Provides a unified Python interface to download, parse, and iterate over information retrieval benchmarks and training datasets (documents, queries, relevance judgments) from many public sources.","skillfed_tags":["information-retrieval","benchmark-datasets","nlp-data"],"use_cases":["Build and evaluate information retrieval models using standard benchmarks like TREC, MS-MARCO, or BEIR without manually downloading and parsing each dataset.","Process large document collections in parallel by using efficient iter slicing to partition data across multiple devices or workers.","Quickly prototype ranking algorithms by accessing queries, documents, and relevance judgments through a unified iterator interface.","Access multilingual IR datasets (e.g., HC4, CLIRMatrix) for cross-lingual retrieval research without managing separate download and format conversion steps.","Build dataset-agnostic evaluation pipelines that work across multiple benchmarks by relying on the common interface and automatic data normalization."],"what_it_does":"ir_datasets is a Python package that centralizes access to information retrieval benchmarks and training datasets used in academic research and industry. It abstracts away the complexity of locating, downloading, and parsing datasets from multiple sources\u2014handling format differences, encoding issues, and file extraction automatically. The package provides both a Python API and command-line interface, allowing you to iterate over documents, queries, and relevance judgments in a consistent format.\n\nThe package handles datasets ranging from small benchmarks to massive collections like ClueWeb (approximately 1 billion documents) by using iterators that avoid loading entire datasets into memory. It also provides fast random-access lookups through document stores, supports slicing operations for parallel processing, and automatically fixes known data quality issues in popular datasets like MS-MARCO. When datasets are not publicly available, it provides instructions on how to obtain them.","worth_installing":"Yes. ir_datasets is actively maintained, has no known vulnerabilities, and solves a real problem for IR researchers and practitioners\u2014centralizing access to fragmented benchmark datasets. The low install friction and permissive MIT license make adoption straightforward. Install it if you work with information retrieval datasets or need to prototype ranking models against standard benchmarks."},"id":"ir-datasets","links":{"html":"https://skillfed.io/packages/ir-datasets","md":"https://skillfed.io/packages/ir-datasets.md","pypi":"https://pypi.org/project/ir-datasets/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-18","license_spdx":null,"license_treatment":"permissive","name":"ir-datasets","python_support":"supports_current","summary":"provides a common interface to many IR ad-hoc ranking benchmarks, training datasets, etc."},"popularity":{"monthly_downloads":752161,"position":5153,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.6.3"}