README.md

metadata

license: cc-by-sa-4.0
language:
  - ba
  - ru
pretty_name: Bashkir-Russian Wikipedia Parallel Corpus
task_categories:
  - translation
configs:
  - config_name: cleaned
    data_files:
      - split: train
        path: cleaned/data.parquet
  - config_name: precleaned
    data_files:
      - split: train
        path: precleaned/data.parquet
  - config_name: scored
    data_files:
      - split: train
        path: scored/data.parquet
  - config_name: filtered
    data_files:
      - split: train
        path: filtered/data.parquet
tags:
  - bashkir
  - russian
  - wikipedia
  - cyrillic
  - low-resource
  - parallel-corpus
  - sentence-level
  - bitext-mining

Bashkir-Russian Wikipedia Parallel Corpus

Sentence-level Bashkir-Russian parallel data extracted from the Bashkir and Russian Wikipedia dumps dated 2026-08-01.

This repository provides four configurations. The default cleaned configuration is recommended for machine translation training. The precleaned configuration is an earlier, less filtered extraction provided for alternative preprocessing and research. The scored configuration adds LASER-based quality scores to the cleaned pairs. The filtered configuration applies a conservative two-encoder filter on top of scored.

Configurations

cleaned

• 72,007 sentence pairs
• 34,566 linked article pairs
• URLs, MediaWiki markup, card fragments and malformed punctuation removed
• duplicate and identical pairs removed
• sentence indices retained

Fields:

• ba_title: Bashkir Wikipedia article title
• ru_title: Russian Wikipedia article title
• section: lead or facts
• ba: Bashkir sentence
• ru: Russian sentence
• ba_sentence_index: sentence index in the Bashkir article
• ru_sentence_index: sentence index in the Russian article

precleaned

• 91,949 sentence pairs
• earlier extraction with lighter filtering
• contains older Wiki markup, links and alignment noise

Fields:

• ba_title, ru_title, section, ba, ru

scored

• 72,007 sentence pairs — identical rows to cleaned, nothing added or removed
• quality scores computed with Meta LASER sentence embeddings:
  • laser_cos: cosine similarity between LASER embeddings of the pair
  • laser_margin: margin normalized by same-language kNN density
  • laser_margin_xling: margin normalized by cross-lingual kNN density (Artetxe & Schwenk, 2018) — the recommended LASER filter score

Encoders: LASER3 bak_Cyrl (NLLB) for Bashkir, LASER2 for Russian, 1024-dim L2-normalized embeddings, k=4 neighbours (laser_encoders, facebookresearch/LASER).

filtered

• 69,706 sentence pairs (−3.20% from scored)
• conservative two-encoder filter: a pair is removed only when both scorers place it in their bottom 5%: laser_margin_xling < 0.876 AND labse_margin_xling < 0.723
• second encoder: LaBSE (Google, 481M parameters, fp16) — independent architecture and training data, so its errors are largely uncorrelated with LASER's
• all score columns retained: laser_cos, laser_margin, laser_margin_xling, labse_cos, labse_margin_xling
• removed pairs are dominated by template mismatch sentences (river-tributary stubs with different facts) and cross-article topic matches

Score distributions over the full 72,007 pairs:

score	p01	p05	p25	p50	p75	p95	p99
laser_cos	0.597	0.705	0.828	0.863	0.887	0.914	0.928
laser_margin_xling	0.760	0.876	0.988	1.033	1.063	1.102	1.129
labse_cos	0.206	0.408	0.698	0.789	0.859	0.932	0.963
labse_margin_xling	0.373	0.723	1.035	1.132	1.202	1.295	1.358

Source and Processing

The data was extracted from corresponding Bashkir and Russian Wikipedia articles using the bawiki-20260801 and ruwiki-20260801 dumps.

The cleaned configuration uses article-local sentence indices, numeric anchors for factual pairs, length-ratio checks, language checks, duplicate removal and service-fragment filtering. The scored and filtered configurations add multilingual sentence-embedding scoring on top of the same alignment without modifying any text.

License

The source text is derived from Wikipedia and is distributed under the Creative Commons Attribution-ShareAlike 4.0 International license.

Please preserve Wikipedia attribution when redistributing or publishing derivative datasets.

Loading

from datasets import load_dataset

cleaned = load_dataset("failed09/bashkir-wikipedia-parallel", "cleaned")
scored = load_dataset("failed09/bashkir-wikipedia-parallel", "scored")
filtered = load_dataset("failed09/bashkir-wikipedia-parallel", "filtered")

# strict MT-training subset with your own threshold
df = scored["train"].to_pandas()
strict = df[(df.laser_margin_xling >= 0.90) & (df.labse_margin_xling >= 1.0)]

Citation

@dataset{failed09_bashkir_russian_wikipedia_parallel_2026,
  title = {Bashkir-Russian Wikipedia Parallel Corpus},
  author = {failed09},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/failed09/bashkir-wikipedia-parallel}
}