Datamonger

RCV1 Binary

Browse datasets

libsvm:rcv1-binary@1 · Registry 1.3.0. Default version in this registry.

LIBSVM's binary classification variant of Reuters Corpus Volume 1, with 47,236 sparse text features and predefined training and test sets. Positive labels combine CCAT and ECAT, and negative labels combine GCAT and MCAT. Documents assigned to both groups are removed. The provider's feature values, labels, splits, and row order are preserved.

Dimensions and tasks

Task

Type

Split

Samples

Predictors

default

classification

test

677,399

47,236

default

classification

train

20,242

47,236

Predictor counts follow each task's feature selection and exclude targets and identifiers. Sample counts are reported separately for each split.

Retrieve

These examples pin both the dataset version and the registry's exact index. An uncached dataset requires network access to its source. The CLI retrieves verified artifacts. The language clients also decode them.

Python

from datamonger import Registry, fetch_data

registry = Registry(
    release="1.3.0",
    index_sha256="94639f8d711d64895689d6da87add0f0f56574070262ec2a04f1f1ad43279f85",
    index_url="https://github.com/jolars/datamonger/releases/download/registry-v1.3.0/index.json",
)
data = fetch_data("libsvm:rcv1-binary@1", registry=registry)

R

library(datamonger)

registry <- registry_selector(
  release = "1.3.0",
  index_sha256 = "94639f8d711d64895689d6da87add0f0f56574070262ec2a04f1f1ad43279f85",
  index_url = "https://github.com/jolars/datamonger/releases/download/registry-v1.3.0/index.json"
)
data <- fetch_data("libsvm:rcv1-binary@1", registry = registry)

Julia

using Datamonger

registry = registry_selector(
    "1.3.0",
    "94639f8d711d64895689d6da87add0f0f56574070262ec2a04f1f1ad43279f85",
    "https://github.com/jolars/datamonger/releases/download/registry-v1.3.0/index.json",
)
data = fetch_data("libsvm:rcv1-binary@1"; registry=registry)

CLI

cat > datamonger-registry.json <<'JSON'
{
  "index_sha256": "94639f8d711d64895689d6da87add0f0f56574070262ec2a04f1f1ad43279f85",
  "index_url": "https://github.com/jolars/datamonger/releases/download/registry-v1.3.0/index.json",
  "release": "1.3.0",
  "schema_version": 1
}
JSON
datamonger --selector datamonger-registry.json fetch 'libsvm:rcv1-binary@1'

Representation and variables

Tabular data with a sparse representation. Decoder: libsvm-split version 1.

Component

Kind

Type

Shape

train_features

sparse_matrix

float64

20,242 × 47,236

train_response

vector

int64

20,242

test_features

sparse_matrix

float64

677,399 × 47,236

test_response

vector

int64

677,399

Preprocessing

Upstream

Status: documented.

The provider feature values, labels, splits, and row order are preserved. Earlier text feature preparation is not described here.

response-dichotomization. Combine CCAT and ECAT into the positive class and GCAT and MCAT into the negative class.

Parameters: negative_classes: GCAT, MCAT; positive_classes: CCAT, ECAT.

sample-selection. Remove documents assigned to both the positive and negative groups.

Parameters: predicate: retain documents assigned to only one label group.

Evidence: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#rcv1.binary

Datamonger

Status: none.

Datamonger decodes the provider artifacts without additional statistical preprocessing.

Evidence: spec/decoders/libsvm-v1.md

Provenance and citation

Provider: LIBSVM. Upstream name: rcv1.binary. Retrieved: 2026-10-19.

Original source

Authors: David D. Lewis, Yiming Yang, Tony G. Rose, Fan Li.

David D. Lewis, Yiming Yang, Tony G. Rose, and Fan Li. RCV1: A New Benchmark Collection for Text Categorization Research. Journal of Machine Learning Research, 5:361-397, 2004. https://www.jmlr.org/papers/v5/lewis04a.html

License

Unknown

License information

The LIBSVM dataset page documents the source and binary label mapping but provides no license terms for these artifacts. NIST identifies Reuters copyright and organizational and individual agreements for the source corpus. No license or redistribution grant has been established for the LIBSVM transformation.

Artifacts

Total download: 445.4 MiB. Sizes describe the distributed files, including any compression.

Artifact

Format

Compression

Size

Distribution

Preservation

train

libsvm

bzip2

13.1 MiB

upstream-only

unknown

test

libsvm

bzip2

432.3 MiB

upstream-only

unknown

train: upstream

test: upstream

Versions in this registry

1 (default)

Registry record

Download the complete registry record, registry selector, or full registry index.

Index SHA-256: 94639f8d711d64895689d6da87add0f0f56574070262ec2a04f1f1ad43279f85.