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empathyai/books-ner-dataset-categories

published by @empathyai

Alignment dataset tracked in the /datasets sub-catalog.

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Created on HF
2025-05-23
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Description

Books Named Entity Recognition (NER) Dataset

A small Named‑Entity‑Recognition (NER) corpus built from categories contained in Project Gutenberg’s public catalogues. It is intended for training or benchmarking entity extractors such as GLiNER on bibliographic metadata.

1 Provenance

This dataset provenance originates from Project Gutenberg's public catalogue.

2 Quick facts

Records (total)

2364

Train split

2127 queries

Eval split… See the full description on the dataset page: https://huggingface.co/datasets/empathyai/books-ner-dataset-categories.

Tags

language:enlicense:cc0-1.0size_categories:1K<n<10Kformat:jsonmodality:textlibrary:datasetslibrary:pandaslibrary:mlcroissantlibrary:polarsregion:eu

README current version from Hugging Face


license: cc0-1.0
configs:

  • config_name: default
    data_files:
    • split: train
      path: entities_train.jsonl
    • split: eval
      path: entities_eval.jsonl
      language:
  • en

Books Named Entity Recognition (NER) Dataset

A small Named‑Entity‑Recognition (NER) corpus built from categories contained in Project Gutenberg’s public catalogues. It is intended for training or benchmarking entity extractors such as GLiNER on bibliographic metadata.


1 Provenance

This dataset provenance originates from Project Gutenberg's public catalogue.


2 Quick facts

Records (total) 2364
Train split 2127 queries
Eval split 237 queries
NER labels category
Source gutenberg.org
Language coverage English only
License CC0 1.0 Public‑Domain Dedication

3 Dataset description

3.1 Origin of the data

  • We parsed Project Gutenberg's public catalogues and turned each category into several synthetic “user queries” (patterns such as “Find books related to the category [category]” or “I'm interested in reading more about the book type [category]”).

3.2 Fields

Field Type Description
tokenized_text List[str] The query tokens.
ner List[[int,int,str]] Spans in token indices (inclusive) and the label category.

Example:

{"tokenized_text":["find","books","related","to","the","category","archaeology","."],"ner":[{"start":6,"end":6,"label":"category"}]}

3.3 Splits

The corpus is provided in two separate files:

  • entities_train.jsonl — 2127 synthetic queries
  • eval.jsonl — 237 synthetic queries

4 How to load

from datasets import load_dataset

ds_train = load_dataset("empathyai/books-ner-dataset-categories", split="train")
ds_eval  = load_dataset("empathyai/books-ner-dataset-categories", split="eval")

5 Licensing and legal basis (EU)

5.1 Why the data are in the public domain

  1. Factual nature — Book titles and author names are mere facts. Under EU law (CJEU Infopaq C‑5/08 and Art. 2 InfoSoc‑Directive 2001/29/EC) facts and very short expressions lacking originality are not protected by copyright.
  2. No sui generis database right — The maker of Project Gutenberg’s catalogue is a U.S. entity; the EU database right (Directive 96/9/EC) only protects databases whose maker is established in the EU.

5.2 License chosen

To reflect the public‑domain status worldwide we apply the Creative Commons CC0 1.0 Public‑Domain Dedication. You may copy, modify, distribute and use the dataset for any purpose without asking permission.

5.3 Trademarks and attribution

  • “Project Gutenberg” is a registered trademark of the Project Gutenberg Literary Archive Foundation. This dataset is not endorsed by or affiliated with PG.
  • If you build on this dataset, please leave this disclaimer intact and do not use “Project Gutenberg” in a way that suggests endorsement.

6 Acknowledgements

Project Gutenberg volunteers for maintaining the free catalogue; HuggingFace for the dataset hosting.

README history 4 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

  1. 2025-05-26feat: add README file810e0a43.4 KB
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  2. 2025-05-26feat: add train dataset29c4df428 B
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  3. 2025-05-26feat: add eval dataset04cc6c328 B
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  4. 2025-05-23initial commit0a8ce0228 B
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Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2025-05-27[bot] Conversion to Parquetopen1 💬#1
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Metadata

License
cc0-1.0

Repository

Download the dataset

Gated dataset. You need to accept the author's terms on Hugging Face before downloading. Visit the repository, click Agree and access repository, then log in locally with huggingface-cli login and use one of the snippets below.

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python)
from datasets import load_dataset
ds = load_dataset("empathyai/books-ner-dataset-categories")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="empathyai/books-ner-dataset-categories", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/empathyai/books-ner-dataset-categories
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