Datasets:

BashkirNLPWorld
/

bashkir-russian-parallel

---
license: other
task_categories:
- text-generation
- translation
- text-classification
- feature-extraction
language:
- ba
- ru
tags:
- bashkir
- russian
- parallel-corpus
- machine-translation
- low-resource
- turkic-languages
- opus
- nllb
- til-mt
pretty_name: Bashkir-Russian Parallel Corpus
size_categories:
- 1M<n<10M
dataset_info:
  features:
  - name: rus
    dtype: string
  - name: bak
    dtype: string
  - name: static_sim
    dtype: float64
  - name: length_rus
    dtype: int64
  - name: length_bak
    dtype: int64
  splits:
  - name: train
    num_bytes: 0
    num_examples: 3040085
  configs:
  - config_name: default
    data_files:
    - split: train
      path: data/train-*
---
# Dataset Card for Bashkir-Russian Parallel Corpus

## Dataset Details

### Dataset Description

**Bashkir-Russian Parallel Corpus** is a large-scale sentence-aligned parallel corpus for the Bashkir–Russian language pair, assembled from **authentic human-created translations**. It contains **3,040,085 unique parallel sentence pairs**, where each Bashkir sentence is aligned with its Russian counterpart.

The corpus combines data from three open parallel corpora: **TIL-MT Corpus** (Turkic Interlingua), **NLLB (OPUS)**, and **Wikimedia (OPUS)**. All pairs were filtered by semantic similarity to remove misaligned and noisy entries, keeping only high-confidence translation pairs. Full duplicates were removed during compilation.

It is designed to support NLP research and applications for Bashkir, a low-resource Turkic language.

- **Curated by:** BashkirNLPWorld
- **Language(s) (NLP):** Bashkir (`ba`), Russian (`ru`), Cyrillic script
- **License:** `other` – see **Licensing** below.

### What BashkirNLPWorld contributed

The BashkirNLPWorld team built this dataset end-to-end on top of open sources:

- **Aggregation** of three independent parallel corpora into a single unified dataset.
- **Field normalization** to a minimal two-column schema (`rus`, `bak`).
- **Semantic filtering** using multilingual sentence embeddings (cosine similarity).
- **Deduplication** by the key `(rus, bak)` — full duplicates removed, 0 duplicates in the final release.
- **Noise filtering** — removal of identical strings and pairs with anomalous length ratios.
- **Machine-readable packaging** as UTF-8 JSONL, ready for `datasets.load_dataset`.
- **Documentation** of sources, filtering criteria, statistics, and dataset limitations.

### Dataset Sources

- **Repository:** [https://huggingface.co/datasets/BashkirNLPWorld/bashkir-russian-parallel](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-russian-parallel)
- **Original sources:**
  - **TIL-MT Corpus** — Turkic Interlingua parallel corpus (ba–ru)
  - **NLLB (OPUS)** — NLLB ba–ru parallel corpus from opus.nlpl.eu
  - **Wikimedia (OPUS)** — Wikimedia ba–ru parallel corpus from opus.nlpl.eu
- **Related datasets:**
  - [Bashkir Lexicon (Machine Fund)](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-lexicon)
  - [Bashkir News Binary Classification](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-news-binary)
  - [Bashkir News Multiclass Classification](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-news-multiclass)
  - [Bashkir News Multilabel Classification](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-news-multilabel)
  - [Bashkir Web Corpus](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-web-corpus)

## Licensing

- **Original sentence pairs** belong to their respective source corpora (**TIL-MT**, **NLLB/OPUS**, **Wikimedia/OPUS**) and their contributors. BashkirNLPWorld does not claim ownership of this content.
- **The structured compilation, metadata, unified schema, deduplication, filtering, and documentation** created by BashkirNLPWorld are released under **CC BY-SA 4.0**.
- Users are responsible for complying with applicable law and the licenses of the original sources when reusing the data.
- Rights holders can request removal of specific entries by contacting the maintainers — we respond within 14 business days and remove disputed content in the next release.

## Uses

### Direct Use

- Training and fine-tuning machine translation systems (Bashkir ↔ Russian)
- Cross-lingual representation learning and multilingual model training
- Linguistic research on Bashkir–Russian translation patterns
- Evaluation of translation quality and semantic alignment
- Data augmentation for low-resource Turkic NLP
- Instruction tuning of generative LLMs for Bashkir

### Out-of-Scope Use

- Re-licensing the original sentence pairs under a different license
- Treating the dataset as a gold-standard translation reference without verification against the original sources
- Any use that violates the rights of the original content creators

## Dataset Structure

### Data Fields

| Field | Type | Description | Missing |
|-------|------|-------------|---------|
| `rus` | string | Russian sentence | 0% |
| `bak` | string | Bashkir sentence | 0% |

### Data Splits

The dataset contains a single split (`train`) with all **3,040,085** examples. Users are encouraged to create their own train/validation/test splits.

### Example Record

```json
{
  "rus": "Будто надсмехаясь над ним, повстанцы оглядываются назад.",
  "bak": "Көлгән һымаҡ, артҡа әйләнеп ҡарайҙар."
}
```

## Dataset Statistics

### Overview

| Metric | Records | % |
|--------|--------:|---:|
| Total records | 3,040,085 | 100.00% |
| Unique `(rus, bak)` pairs | 3,040,085 | 100.00% |
| Full duplicates | 0 | 0.00% |

### Semantic Similarity (`static_sim`)
| Statistic | Value |
|-----------|------:|
| Count | 3,040,085 |
| Min | 0.2500 |
| Max | 1.0000 |
| Mean | 0.6270 |
| Median | 0.6393 |
| Std Dev | 0.1923 |
| p1 | 0.2614 |
| p5 | 0.3031 |
| p25 | 0.4708 |
| p50 | 0.6393 |
| p75 | 0.7887 |
| p95 | 0.9130 |
| p99 | 0.9708 |
### Russian Sentence Length (`length_rus`, characters)

| Statistic | Value |
|-----------|------:|
| Count | 3,040,085 |
| Min | 1 |
| Max | 511 |
| Mean | 84.65 |
| Median | 66 |
| Std Dev | 65.25 |
| p1 | 12 |
| p5 | 19 |
| p25 | 39 |
| p50 | 66 |
| p75 | 109 |
| p95 | 213 |
| p99 | 329 |

### Bashkir Sentence Length (`length_bak`, characters)
| Statistic | Value |
|-----------|------:|
| Count | 3,040,085 |
| Min | 1 |
| Max | 511 |
| Mean | 84.79 |
| Median | 68 |
| Std Dev | 62.56 |
| p1 | 12 |
| p5 | 21 |
| p25 | 43 |
| p50 | 68 |
| p75 | 107 |
| p95 | 208 |
| p99 | 320 |
### Russian Length Distribution (bins)
| Range (chars) | Records | % |
|---------------|--------:|---:|
| 0–20 | 153,605 | 5.05% |
| 20–40 | 612,774 | 20.16% |
| 40–60 | 580,963 | 19.11% |
| 60–80 | 459,889 | 15.13% |
| 80–100 | 342,592 | 11.27% |
| 100–150 | 489,456 | 16.10% |
| 150–200 | 215,316 | 7.08% |
| 200–300 | 140,162 | 4.61% |
| 300–512 | 45,328 | 1.49% |
### Pairs by Semantic Similarity Threshold
| Threshold | Remaining Pairs | % of total |
|-----------|----------------:|-----------:|
| `sim >= 0.25` | 3,040,085 | 100.00% |
| `sim >= 0.5` | 2,162,120 | 71.12% |
| `sim >= 0.6` | 1,713,096 | 56.35% |
| `sim >= 0.7` | 1,219,305 | 40.11% |
| `sim >= 0.8` | 698,411 | 22.97% |
| `sim >= 0.9` | 198,125 | 6.52% |
## Dataset Creation
### Curation Rationale
Bashkir is a low-resource Turkic language with limited parallel data. The goal was to consolidate the most reliable openly available Bashkir–Russian parallel corpora into a single clean, deduplicated, and filtered resource suitable for machine translation and linguistic research.
### Data Collection and Processing
1. Collected parallel data from three sources:
   - **TIL-MT Corpus** (Turkic Interlingua, ba–ru)
   - **NLLB (OPUS)** (ba–ru)
   - **Wikimedia (OPUS)** (ba–ru)
2. Normalized the schema to two fields: `rus`, `bak`.
3. Filtered by **semantic similarity** using multilingual sentence embeddings.
4. Removed pairs where `rus == bak` (non-translations, duplicated strings).
5. Removed pairs shorter than 5 characters.
6. Removed pairs with length ratio greater than 3 (anomalous alignment).
7. Deduplicated by `(rus, bak)`.
8. Saved as UTF-8 JSONL.
**Tools:** Python 3, Hugging Face `datasets`, `sentence-transformers`.
### Source Data Producers
The original sentence pairs were produced by the authors, compilers, and contributors of the **TIL-MT Corpus**, **NLLB**, and **Wikimedia** projects. BashkirNLPWorld is responsible for aggregation, filtering, deduplication, unified schema, metadata, and packaging.
### Annotations
No manual annotations were added. All sentence pairs come from the original parallel corpora.
### Personal and Sensitive Information
The dataset contains publicly available sentence pairs and does not intentionally include personally identifiable information.
## Bias, Risks, and Limitations
- **Source imbalance:** the three sources contribute different volumes and domains; the corpus is not domain-balanced.
- **Domain skew:** Wikimedia and NLLB data are skewed toward encyclopedic, religious, and technical texts.
- **Length skew:** 44.3% of Russian sentences are shorter than 60 characters; long sentences (>300 chars) account for only 1.49%.
- **Semantic similarity skew:** 28.9% of pairs have `static_sim < 0.5`, which may indicate loose or noisy translations.
- **Residual noise:** despite semantic filtering, a small fraction of pairs may be imperfect translations (e.g., non-translated proper nouns such as "Любомир!" — "Любомир!").
- **No document context:** sentences are aligned out of their original document context.
- **Source snapshot:** reflects the state of the upstream corpora at collection time.

### Recommendations

- Account for semantic similarity distribution when training models — consider a higher threshold (`static_sim >= 0.6` or `>= 0.7`) for higher precision.
- Balance domains or sources if training a domain-specific MT system.
- Filter out pairs with identical Russian and Bashkir strings before training.
- For commercial use, verify the license status of specific source components.
- Report issues via the Hugging Face repository.

## Citation

**BibTeX:**
```bibtex
@dataset{bashkirnlpworld2026bashkirrussianparallel,
  author       = {BashkirNLPWorld},
  title        = {Bashkir-Russian Parallel Corpus},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/BashkirNLPWorld/bashkir-russian-parallel}
}
```

**APA:**
BashkirNLPWorld. (2026). *Bashkir-Russian Parallel Corpus* [Data set]. Hugging Face. https://huggingface.co/datasets/BashkirNLPWorld/bashkir-russian-parallel

## Glossary

- **Parallel corpus** – a collection of texts aligned across two or more languages at the sentence level.
- **Sentence alignment** – the task of matching sentences across languages that are translations of each other.
- **Semantic similarity (`static_sim`)** – cosine similarity between multilingual sentence embeddings, ranging from 0 to 1.
- **OPUS** – an open collection of parallel corpora (opus.nlpl.eu).
- **NLLB** – No Language Left Behind, a multilingual translation project by Meta AI.
- **TIL-MT** – Turkic Interlingua Machine Translation, a project focused on Turkic languages.
- **Low-resource language** – a language with limited digital resources and tools.
## Dataset Card Authors
- BashkirNLPWorld
## Dataset Card Contact
For questions, takedown requests, or collaboration, please open an issue:
[https://huggingface.co/datasets/BashkirNLPWorld/bashkir-russian-parallel/discussions](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-russian-parallel/discussions)

	---
	license: other
	task_categories:
	- text-generation
	- translation
	- text-classification
	- feature-extraction
	language:
	- ba
	- ru
	tags:
	- bashkir
	- russian
	- parallel-corpus
	- machine-translation
	- low-resource
	- turkic-languages
	- opus
	- nllb
	- til-mt
	pretty_name: Bashkir-Russian Parallel Corpus
	size_categories:
	- 1M<n<10M
	dataset_info:
	features:
	- name: rus
	dtype: string
	- name: bak
	dtype: string
	- name: static_sim
	dtype: float64
	- name: length_rus
	dtype: int64
	- name: length_bak
	dtype: int64
	splits:
	- name: train
	num_bytes: 0
	num_examples: 3040085
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train-*
	---

	# Dataset Card for Bashkir-Russian Parallel Corpus

	## Dataset Details

	### Dataset Description

	Bashkir-Russian Parallel Corpus is a large-scale sentence-aligned parallel corpus for the Bashkir–Russian language pair, assembled from authentic human-created translations. It contains 3,040,085 unique parallel sentence pairs, where each Bashkir sentence is aligned with its Russian counterpart.

	The corpus combines data from three open parallel corpora: TIL-MT Corpus (Turkic Interlingua), NLLB (OPUS), and Wikimedia (OPUS). All pairs were filtered by semantic similarity to remove misaligned and noisy entries, keeping only high-confidence translation pairs. Full duplicates were removed during compilation.

	It is designed to support NLP research and applications for Bashkir, a low-resource Turkic language.

	- Curated by: BashkirNLPWorld
	- Language(s) (NLP): Bashkir (`ba`), Russian (`ru`), Cyrillic script
	- License: `other` – see Licensing below.

	### What BashkirNLPWorld contributed

	The BashkirNLPWorld team built this dataset end-to-end on top of open sources:

	- Aggregation of three independent parallel corpora into a single unified dataset.
	- Field normalization to a minimal two-column schema (`rus`, `bak`).
	- Semantic filtering using multilingual sentence embeddings (cosine similarity).
	- Deduplication by the key `(rus, bak)` — full duplicates removed, 0 duplicates in the final release.
	- Noise filtering — removal of identical strings and pairs with anomalous length ratios.
	- Machine-readable packaging as UTF-8 JSONL, ready for `datasets.load_dataset`.
	- Documentation of sources, filtering criteria, statistics, and dataset limitations.

	### Dataset Sources

	- Repository: [https://huggingface.co/datasets/BashkirNLPWorld/bashkir-russian-parallel](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-russian-parallel)
	- Original sources:
	- TIL-MT Corpus — Turkic Interlingua parallel corpus (ba–ru)
	- NLLB (OPUS) — NLLB ba–ru parallel corpus from opus.nlpl.eu
	- Wikimedia (OPUS) — Wikimedia ba–ru parallel corpus from opus.nlpl.eu
	- Related datasets:
	- [Bashkir Lexicon (Machine Fund)](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-lexicon)
	- [Bashkir News Binary Classification](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-news-binary)
	- [Bashkir News Multiclass Classification](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-news-multiclass)
	- [Bashkir News Multilabel Classification](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-news-multilabel)
	- [Bashkir Web Corpus](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-web-corpus)

	## Licensing

	- Original sentence pairs belong to their respective source corpora (TIL-MT, NLLB/OPUS, Wikimedia/OPUS) and their contributors. BashkirNLPWorld does not claim ownership of this content.
	- The structured compilation, metadata, unified schema, deduplication, filtering, and documentation created by BashkirNLPWorld are released under CC BY-SA 4.0.
	- Users are responsible for complying with applicable law and the licenses of the original sources when reusing the data.
	- Rights holders can request removal of specific entries by contacting the maintainers — we respond within 14 business days and remove disputed content in the next release.

	## Uses

	### Direct Use

	- Training and fine-tuning machine translation systems (Bashkir ↔ Russian)
	- Cross-lingual representation learning and multilingual model training
	- Linguistic research on Bashkir–Russian translation patterns
	- Evaluation of translation quality and semantic alignment
	- Data augmentation for low-resource Turkic NLP
	- Instruction tuning of generative LLMs for Bashkir

	### Out-of-Scope Use

	- Re-licensing the original sentence pairs under a different license
	- Treating the dataset as a gold-standard translation reference without verification against the original sources
	- Any use that violates the rights of the original content creators

	## Dataset Structure

	### Data Fields

	\| Field \| Type \| Description \| Missing \|
	\|-------\|------\|-------------\|---------\|
	\| `rus` \| string \| Russian sentence \| 0% \|
	\| `bak` \| string \| Bashkir sentence \| 0% \|

	### Data Splits

	The dataset contains a single split (`train`) with all 3,040,085 examples. Users are encouraged to create their own train/validation/test splits.

	### Example Record

	```json
	{
	"rus": "Будто надсмехаясь над ним, повстанцы оглядываются назад.",
	"bak": "Көлгән һымаҡ, артҡа әйләнеп ҡарайҙар."
	}
	```

	## Dataset Statistics

	### Overview

	\| Metric \| Records \| % \|
	\|--------\|--------:\|---:\|
	\| Total records \| 3,040,085 \| 100.00% \|
	\| Unique `(rus, bak)` pairs \| 3,040,085 \| 100.00% \|
	\| Full duplicates \| 0 \| 0.00% \|

	### Semantic Similarity (`static_sim`)

	\| Statistic \| Value \|
	\|-----------\|------:\|
	\| Count \| 3,040,085 \|
	\| Min \| 0.2500 \|
	\| Max \| 1.0000 \|
	\| Mean \| 0.6270 \|
	\| Median \| 0.6393 \|
	\| Std Dev \| 0.1923 \|
	\| p1 \| 0.2614 \|
	\| p5 \| 0.3031 \|
	\| p25 \| 0.4708 \|
	\| p50 \| 0.6393 \|
	\| p75 \| 0.7887 \|
	\| p95 \| 0.9130 \|
	\| p99 \| 0.9708 \|

	### Russian Sentence Length (`length_rus`, characters)

	\| Statistic \| Value \|
	\|-----------\|------:\|
	\| Count \| 3,040,085 \|
	\| Min \| 1 \|
	\| Max \| 511 \|
	\| Mean \| 84.65 \|
	\| Median \| 66 \|
	\| Std Dev \| 65.25 \|
	\| p1 \| 12 \|
	\| p5 \| 19 \|
	\| p25 \| 39 \|
	\| p50 \| 66 \|
	\| p75 \| 109 \|
	\| p95 \| 213 \|
	\| p99 \| 329 \|

	### Bashkir Sentence Length (`length_bak`, characters)

	\| Statistic \| Value \|
	\|-----------\|------:\|
	\| Count \| 3,040,085 \|
	\| Min \| 1 \|
	\| Max \| 511 \|
	\| Mean \| 84.79 \|
	\| Median \| 68 \|
	\| Std Dev \| 62.56 \|
	\| p1 \| 12 \|
	\| p5 \| 21 \|
	\| p25 \| 43 \|
	\| p50 \| 68 \|
	\| p75 \| 107 \|
	\| p95 \| 208 \|
	\| p99 \| 320 \|

	### Russian Length Distribution (bins)

	\| Range (chars) \| Records \| % \|
	\|---------------\|--------:\|---:\|
	\| 0–20 \| 153,605 \| 5.05% \|
	\| 20–40 \| 612,774 \| 20.16% \|
	\| 40–60 \| 580,963 \| 19.11% \|
	\| 60–80 \| 459,889 \| 15.13% \|
	\| 80–100 \| 342,592 \| 11.27% \|
	\| 100–150 \| 489,456 \| 16.10% \|
	\| 150–200 \| 215,316 \| 7.08% \|
	\| 200–300 \| 140,162 \| 4.61% \|
	\| 300–512 \| 45,328 \| 1.49% \|

	### Pairs by Semantic Similarity Threshold

	\| Threshold \| Remaining Pairs \| % of total \|
	\|-----------\|----------------:\|-----------:\|
	\| `sim >= 0.25` \| 3,040,085 \| 100.00% \|
	\| `sim >= 0.5` \| 2,162,120 \| 71.12% \|
	\| `sim >= 0.6` \| 1,713,096 \| 56.35% \|
	\| `sim >= 0.7` \| 1,219,305 \| 40.11% \|
	\| `sim >= 0.8` \| 698,411 \| 22.97% \|
	\| `sim >= 0.9` \| 198,125 \| 6.52% \|

	## Dataset Creation

	### Curation Rationale

	Bashkir is a low-resource Turkic language with limited parallel data. The goal was to consolidate the most reliable openly available Bashkir–Russian parallel corpora into a single clean, deduplicated, and filtered resource suitable for machine translation and linguistic research.

	### Data Collection and Processing

	1. Collected parallel data from three sources:
	- TIL-MT Corpus (Turkic Interlingua, ba–ru)
	- NLLB (OPUS) (ba–ru)
	- Wikimedia (OPUS) (ba–ru)
	2. Normalized the schema to two fields: `rus`, `bak`.
	3. Filtered by semantic similarity using multilingual sentence embeddings.
	4. Removed pairs where `rus == bak` (non-translations, duplicated strings).
	5. Removed pairs shorter than 5 characters.
	6. Removed pairs with length ratio greater than 3 (anomalous alignment).
	7. Deduplicated by `(rus, bak)`.
	8. Saved as UTF-8 JSONL.

	Tools: Python 3, Hugging Face `datasets`, `sentence-transformers`.

	### Source Data Producers

	The original sentence pairs were produced by the authors, compilers, and contributors of the TIL-MT Corpus, NLLB, and Wikimedia projects. BashkirNLPWorld is responsible for aggregation, filtering, deduplication, unified schema, metadata, and packaging.

	### Annotations

	No manual annotations were added. All sentence pairs come from the original parallel corpora.

	### Personal and Sensitive Information

	The dataset contains publicly available sentence pairs and does not intentionally include personally identifiable information.

	## Bias, Risks, and Limitations

	- Source imbalance: the three sources contribute different volumes and domains; the corpus is not domain-balanced.
	- Domain skew: Wikimedia and NLLB data are skewed toward encyclopedic, religious, and technical texts.
	- Length skew: 44.3% of Russian sentences are shorter than 60 characters; long sentences (>300 chars) account for only 1.49%.
	- Semantic similarity skew: 28.9% of pairs have `static_sim < 0.5`, which may indicate loose or noisy translations.
	- Residual noise: despite semantic filtering, a small fraction of pairs may be imperfect translations (e.g., non-translated proper nouns such as "Любомир!" — "Любомир!").
	- No document context: sentences are aligned out of their original document context.
	- Source snapshot: reflects the state of the upstream corpora at collection time.

	### Recommendations

	- Account for semantic similarity distribution when training models — consider a higher threshold (`static_sim >= 0.6` or `>= 0.7`) for higher precision.
	- Balance domains or sources if training a domain-specific MT system.
	- Filter out pairs with identical Russian and Bashkir strings before training.
	- For commercial use, verify the license status of specific source components.
	- Report issues via the Hugging Face repository.

	## Citation

	BibTeX:
	```bibtex
	@dataset{bashkirnlpworld2026bashkirrussianparallel,
	author = {BashkirNLPWorld},
	title = {Bashkir-Russian Parallel Corpus},
	year = {2026},
	publisher = {Hugging Face},
	url = {https://huggingface.co/datasets/BashkirNLPWorld/bashkir-russian-parallel}
	}
	```

	APA:
	BashkirNLPWorld. (2026). Bashkir-Russian Parallel Corpus [Data set]. Hugging Face. https://huggingface.co/datasets/BashkirNLPWorld/bashkir-russian-parallel

	## Glossary

	- Parallel corpus – a collection of texts aligned across two or more languages at the sentence level.
	- Sentence alignment – the task of matching sentences across languages that are translations of each other.
	- Semantic similarity (`static_sim`) – cosine similarity between multilingual sentence embeddings, ranging from 0 to 1.
	- OPUS – an open collection of parallel corpora (opus.nlpl.eu).
	- NLLB – No Language Left Behind, a multilingual translation project by Meta AI.
	- TIL-MT – Turkic Interlingua Machine Translation, a project focused on Turkic languages.
	- Low-resource language – a language with limited digital resources and tools.

	## Dataset Card Authors

	- BashkirNLPWorld

	## Dataset Card Contact

	For questions, takedown requests, or collaboration, please open an issue:
	[https://huggingface.co/datasets/BashkirNLPWorld/bashkir-russian-parallel/discussions](https://huggingface.co/datasets/BashkirNLPWorld/bashkir-russian-parallel/discussions)