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language: en
license: odc-by
tags:
- text-classification
- domain-classification
- c4
task_categories:
- text-classification
- text-retrieval
configs:
- config_name: default
data_files:
- split: train
path: all.jsonl.zst
- config_name: pristine
data_files:
- split: train
path: pristine/train.jsonl.zst
- split: validation
path: pristine/validation.jsonl.zst
- split: test
path: pristine/test.jsonl.zst
- config_name: classla_news
data_files:
- split: train
path: classla_news/train.jsonl.zst
- split: validation
path: classla_news/validation.jsonl.zst
- split: test
path: classla_news/test.jsonl.zst
- config_name: classla_ParlaCAP
data_files:
- split: train
path: classla_ParlaCAP/train.jsonl.zst
- split: validation
path: classla_ParlaCAP/validation.jsonl.zst
- split: test
path: classla_ParlaCAP/test.jsonl.zst
- config_name: doc_type_v1_primary
data_files:
- split: train
path: doc_type_v1_primary/train.jsonl.zst
- split: validation
path: doc_type_v1_primary/validation.jsonl.zst
- split: test
path: doc_type_v1_primary/test.jsonl.zst
- config_name: doc_type_v2_primary
data_files:
- split: train
path: doc_type_v2_primary/train.jsonl.zst
- split: validation
path: doc_type_v2_primary/validation.jsonl.zst
- split: test
path: doc_type_v2_primary/test.jsonl.zst
- config_name: fdc_label
data_files:
- split: train
path: fdc_label/train.jsonl.zst
- split: validation
path: fdc_label/validation.jsonl.zst
- split: test
path: fdc_label/test.jsonl.zst
- config_name: nvidia_domain
data_files:
- split: train
path: nvidia_domain/train.jsonl.zst
- split: validation
path: nvidia_domain/validation.jsonl.zst
- split: test
path: nvidia_domain/test.jsonl.zst
---
# English Document Classification Dataset
This dataset provides a curated subset of the first **1 million rows** from the [allenai/c4](https://huggingface.co/datasets/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 five distinct 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`](https://huggingface.co/nvidia/domain-classifier) | **nvidia_domain** | General web domain categorization. |
| [`classla/multilingual-IPTC-news-topic-classifier`](https://huggingface.co/classla/multilingual-IPTC-news-topic-classifier) | **classla_news** | Standardized news industry topic codes. |
| [`classla/ParlaCAP-Topic-Classifier`](https://huggingface.co/classla/ParlaCAP-Topic-Classifier) | **classla_ParlaCAP** | Legislative and parliamentary topic categories. |
| [`cardiffnlp/tweet-topic-latest-multi`](https://huggingface.co/cardiffnlp/tweet-topic-latest-multi) | **cardiffnlp_tweet** | Social-media style topic classification. |
| [`EssentialAI/eai-distill-0.5b`](https://www.google.com/search?q=https://huggingface.co/EssentialAI/eai-distill-0.5b) | **fdc_label** and other columns | Categorical mapping derived from FDC data and content analysis. |
> [!NOTE]
> **FDC (Free Decimal Correspondence) Mapping:** Labels were derived from predictions by [EssentialAI/eai-distill-0.5b](https://www.google.com/search?q=https://huggingface.co/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 (e.g., 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](https://huggingface.co/datasets/allenai/c4) dataset when using this resource. |