| --- |
| 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. |
