aparecium / ATTRIBUTIONS.md
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Attributions

Aparecium's code and released prefix-mapper and LoRA-adapter weights are distributed under the MIT License. Upstream models and training datasets retain their respective licenses. Aparecium does not relicense or redistribute the training corpus.

Models

Component Source Upstream license
Pretrained language model GPT-2, OpenAI MIT
Sentence encoder and candidate scorer all-mpnet-base-v2, Sentence Transformers Apache-2.0
Independent evaluation encoder bge-small-en-v1.5, BAAI MIT

The GPT-2 and MPNet base weights are obtained separately from their upstream repositories. BGE is used for evaluation and is not required for inference.

The implementation uses PyTorch, Transformers, PEFT, and Sentence Transformers. Their copyright and license notices remain with their distributions.

Training sources

The table identifies the source collections and the license information recorded for them. Dataset-level license labels do not replace the terms or attribution attached to individual source documents.

Collection Source and acknowledgment License information
C4, realnewslike Allen Institute for AI C4 distribution, derived from Common Crawl ODC-By
Common Pile News Common Pile contributors and original news authors Document-level CC-BY filtering; retained documents record Attribution 4.0
HuffPost News Category News Category Dataset by Rishabh Misra, via heegyu/news-category-dataset CC-BY-4.0, as labeled by the collection
Wikinews Wikinews contributors, via Izumi Lab's English collection CC-BY-2.5
Wikipedia sentences Wikipedia contributors, via Sentence Transformers' English collection Wikipedia CC-BY-SA terms; the collection does not specify a version
FineWeb-Edu Hugging Face FineWeb-Edu, derived from Common Crawl ODC-By
Webis TL;DR Webis Group TL;DR collection and original contributors CC-BY-4.0

Aparecium extracts short headlines, summaries, and sentences for training. The release contains model parameters, not copies of these datasets. Source authors, corpus maintainers, and their upstream notices remain the authorities for the original content and its terms.

Evaluation sources

Evaluation includes held-out texts from the training sources, CNN/DailyMail and XSum summaries, BBC news descriptions, social posts, Q&A titles, and crypto headlines. The independent similarity threshold is calibrated using STS Benchmark. Aggregate scores and illustrative reconstructions do not change the licenses of those sources.