SpanishWikiClustering

Clustering benchmark for Spanish embedding models, designed for MTEB as SpanishWikiClusteringP2P.

Given opening paragraphs from Spanish Wikipedia articles, models must group them by topic without seeing the labels. This task format is inspired by WikiClusteringP2P (Rystrøm, 2024), which covers 14 languages — but not Spanish. This dataset fills that gap.

Dataset

• Source: Spanish Wikipedia via CirrusSearch dump (December 2025)
• Format: MTEB clustering — sentences (list of texts) + labels (list of categories)
• Samples: 30 independent clustering tasks
• Articles per sample: 900 (100 per category, stratified)
• Split: test

Categories (9)

Category	Pool size
Psicología	768K articles
Lenguaje	210K articles
Ciencias_naturales	210K articles
Geografía	176K articles
Historia	63K articles
Organizaciones	23K articles
Literatura	21K articles
Deporte	12K articles
Fenómenos_naturales	6K articles

Usage

from datasets import load_dataset

dataset = load_dataset("ClementeH/SpanishWikiClustering", split="test")
# 30 rows, each with 900 sentences and 900 labels

sample = dataset[0]
print(len(sample["sentences"]))  # 900
print(set(sample["labels"]))     # 9 categories

Evaluate with MTEB

import mteb

model = mteb.get_model("intfloat/multilingual-e5-base")
task = mteb.get_task("SpanishWikiClusteringP2P")
results = mteb.evaluate(model, tasks=[task])

Benchmark Results

Evaluated with MTEB's v_measure metric (higher is better). 30 samples of 900 articles each.

Model	v_measure
intfloat/multilingual-e5-large-instruct	0.3679
BAAI/bge-m3	0.3308
sentence-transformers/paraphrase-multilingual-mpnet-base-v2	0.3296
intfloat/multilingual-e5-large	0.3249
intfloat/multilingual-e5-base	0.3226
intfloat/multilingual-e5-small	0.3194
nomic-ai/nomic-embed-text-v1.5	0.3183
sentence-transformers/distiluse-base-multilingual-cased-v2	0.2999
sentence-transformers/stsb-xlm-r-multilingual	0.2904
Snowflake/snowflake-arctic-embed-m-v1.5	0.2883
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2	0.2863
sentence-transformers/LaBSE	0.2861
minishlab/potion-multilingual-128M	0.2843
sentence-transformers/static-similarity-mrl-multilingual-v1	0.2527

For reference, WikiClusteringP2P.v2 (e5-base) scores for comparable languages: Czech 0.3198, Albanian 0.2936, Danish 0.1920.

Replication

The pipeline to regenerate the dataset from scratch is at Clemente-H/clustering_embeddings_es, adapted from jhrystrom/wiki-clustering to support Spanish Wikipedia's MediaWiki 1.42+ schema.

git clone https://github.com/Clemente-H/clustering_embeddings_es
cd clustering_embeddings_es
pip install -r requirements.txt

# Full pipeline (~10 GB download, ~20 GB RAM)
python run.py

# Skip download if dumps are already in local_data/
python run.py --skip-download --n-articles 900 --n-turns 30

Key adaptations vs. original wiki-clustering

The original pipeline targets MediaWiki ≤1.41. Spanish Wikipedia runs MW 1.42+, which introduced breaking changes:

1. categorylinks schema change: cl_to (category name string) was replaced by cl_target_id (foreign key to the new linktarget table)
2. SQL encoding: category names in SQL dumps are stored as latin1-misinterpreted UTF-8 — matching requires re-encoding before joining
3. CirrusSearch over XML: the XML dump requires ~64 GB RAM; CirrusSearch is NDJSON, streamable with ~8 GB
4. Category curation: Spanish Wikipedia's automatic top-level categories are too coarse (5 nodes). We use 9 thematic categories from the 2nd level, curated to avoid nesting, inflated person-profession categories, and semantic ambiguity
5. Two-list config: curated_categories anchors the tree walk (prevents pool contamination from excluded categories); sample_categories is the subset used for labeling. Excluded categories must remain as anchors so their articles are not reassigned to neighboring pools

Citation

If you use this dataset, please cite:

@misc{henriquez2026spanishwikiclustering,
  author       = {Henriquez, Clemente},
  title        = {{SpanishWikiClustering}: A Wikipedia-based Clustering Benchmark for Spanish},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/ClementeH/SpanishWikiClustering}},
}

If you use the replication pipeline, please also cite the original wiki-clustering work it is based on:

@misc{rystrom2024wikiclustering,
  author       = {Rystr{\o}m, Jonathan},
  title        = {Wiki Clustering},
  year         = {2024},
  howpublished = {\url{https://github.com/jhrystrom/wiki-clustering}},
}

If you use this dataset via MTEB, please also cite:

@article{muennighoff2022mteb,
  author  = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"i}c and Reimers, Nils},
  title   = {{MTEB}: Massive Text Embedding Benchmark},
  journal = {arXiv preprint arXiv:2210.07316},
  year    = {2022},
  url     = {https://arxiv.org/abs/2210.07316},
}

@article{enevoldsen2025mmteb,
  title   = {{MMTEB}: Massive Multilingual Text Embedding Benchmark},
  author  = {Enevoldsen, Kenneth and others},
  journal = {arXiv preprint arXiv:2502.13595},
  year    = {2025},
  url     = {https://arxiv.org/abs/2502.13595},
}