All datasets / eilamc14
eilamc14/wikilarge-clean
The checks could not identify a recognised licence ID for at least one declared source dataset.
- Declared licence
- cc-by-sa-4.0, from the tagEvidence: README.md at 216fedb
- Licence files
- No licence file at the top level of the repositoryEvidence: file list at 216fedb
- Declared sources
- 3
- Access gated on Hugging Face
- No
Sources listed in the card metadata
- wikipediacc-by-sa-3.0Share-alikeEvidence: README.md at 97a0b05
- simple-wikipedianot foundUnclearEvidence: Hub page
- WikiLargenot foundUnclearEvidence: Hub page
Tasks
Citation text from the card
- **Provenance:** Derived from English Wikipedia and Simple English Wikipedia content.
- **License:** `CC BY-SA 4.0` (ShareAlike applies). You must provide attribution (TASL: Title, Author(s), Source, License) and distribute derivatives under the same license.
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Please cite **both** the widely used WikiLarge reference **and** this cleaned subset:
**Original alignment (WikiLarge):**
Zhang, X., & Lapata, M. (2017). *Sentence Simplification with Deep Reinforcement Learning*. In EMNLP 2017 (pp. 584–594). Association for Computational Linguistics. doi:10.18653/v1/D17-1062
**This dataset (cleaned subset):**
Cohen, E., Bul, I., Inbar, D., & Loewenbach, O. (2026). Simplify-This: A Comparative Analysis of Prompt-Based and Fine-Tuned LLMs. arXiv. arxiv.org/abs/2601.05794
WikiLarge BibTeX:
@inproceedings{zhang-lapata-2017-sentence,
title = {Sentence Simplification with Deep Reinforcement Learning},
author = {Zhang, Xingxing and Lapata, Mirella},
booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
pages = {584--594},
year = {2017},
address = {Copenhagen, Denmark},
publisher = {Association for Computational Linguistics},
doi = {10.18653/v1/D17-1062},
url = {https://aclanthology.org/D17-1062/}
}
Wikilarge cleaned BibTeX:
@misc{simplifythis2025,
author = {Cohen, Eilam and others},
title = {Simplify-This: A Com
The same facts for your agent
A free MCP server at mcp.datalicense.uk/mcp. Connect it from an MCP client such as Claude or Cursor.
dataset_licence({ "id": "eilamc14/wikilarge-clean" })
signal: "unclear"
stated_licence: "cc-by-sa-4.0"
sources: 3