agentlans/en-document-classification

English Document Classification Dataset

This dataset provides a curated subset of the first 1 million rows from the allenai/c4 (English configuration), enriched with multi-perspective topic annotations. It is designed for researchers exploring document classification, domain adaptation, and label noise in massive web-crawled corpora.

Dataset Summary

The dataset integrates predictions from classification models to provide a holistic view of each document’s content. To ensure utility, the data was stratified across these variables and split into 80/10/10% (Train/Validation/Test) sets, organized into specific configurations based on the source classifier.

Classifier Metadata & Schema

The following classifiers were used to generate the annotation columns:

Classifier	Column Name	Description
nvidia/domain-classifier	nvidia_domain	General web domain categorization.
BAAI/IndustryCorpus2_Classifier	industry_corpus2	General industry categorization.
classla/multilingual-IPTC-news-topic-classifier	classla_news	Standardized news industry topic codes.
classla/ParlaCAP-Topic-Classifier	classla_ParlaCAP	Legislative and parliamentary topic categories.
cardiffnlp/tweet-topic-latest-multi	cardiffnlp_tweet	Social-media style topic classification.
ibm-granite/GneissWeb.*_classifier	gneiss	Science, technology, education, or medical classification.
EssentialAI/eai-distill-0.5b	fdc_label and other columns	Categorical mapping derived from FDC data and content analysis.

FDC (Free Decimal Correspondence) Mapping: Labels were derived from predictions by EssentialAI/eai-distill-0.5b. Call numbers were truncated to the nearest multiple of 10 and mapped to official FDC categories.

Quality Indicators

• pristine column: A boolean flag based on data generated by the EAI classifier. It indicates whether the document is structurally complete or likely missing content/context.
• Configurations: The dataset is partitioned into configs (e.g., classla_ParlaCAP) to allow users to load data stratified by specific model outputs.

Limitations & Biases

Beware of the following caveats in automated labelling:

• Class Imbalance: Significant distribution shifts exist between categories, reflecting the natural frequency of topics in the C4 corpus.
• Silver-Standard Labels: All labels are model-generated (silver-standard). Errors or biases present in the source classifiers will be reflected in this dataset.
• Label Ambiguity: Web documents often overlap multiple domains (for example, a personal blog discussing both geopolitics and cooking). Single-label assignment may oversimplify these documents.

Licensing

This dataset is released under the Open Data Commons Attribution License (ODC-BY). Please attribute the source models and the creators of the allenai/c4 dataset when using this resource.