---
pretty_name: SNeG-data-1.0
license: odc-by
language:
- alt
- atv
- ba
- be
- bxr
- ce
- cv
- de
- en
- kbd
- kca
- kjh
- kk
- koi
- kpv
- krc
- krl
- kum
- ky
- lez
- lud
- mdf
- mhr
- mns
- mrj
- myv
- nog
- olo
- ru
- sah
- tt
- tyv
- udm
- uz
- vep
- xal
task_categories:
- text-generation
language_creators:
- found
annotations_creators:
- machine-generated
multilinguality:
- multilingual
size_categories:
- 1M<n<10M
tags:
- text
- corpus
- multilingual
- low-resource
- pretraining
- news
- literature
- folklore
- cis
- post-soviet
- regional-languages
source_datasets:
- d0rj/ilibrary
configs:
- config_name: unfiltered
  data_files:
  - split: train
    path: data/unfiltered/train-*.parquet
- config_name: filtered
  data_files:
  - split: train
    path: data/filtered/train-*.parquet
---

![Filtered tokens: 3.99B](https://img.shields.io/badge/filtered_tokens-3.99B-2563eb?style=flat-square)
![Source languages: 34](https://img.shields.io/badge/source_languages-34-0f766e?style=flat-square)

# SNeG-data-1.0

**English** · [Русский](./README_RU.md)

SNeG-data-1.0 is a multilingual collection of news articles, literature, folklore, educational and reference materials, and other texts in languages of the CIS and the wider post-Soviet region, with an emphasis on low-resource languages.

## Statistics

Both subsets contain a single shuffled `train` split. `unfiltered` contains the collected texts before cleaning; `filtered` contains the cleaned and deduplicated texts.

| Subset | Records | Gemma 4 tokens |
|---|---:|---:|
| `unfiltered` | 3,046,707 | 4,125,211,521 |
| `filtered` | 2,985,422 | 3,990,012,437 |

Token counts cover the `text` field, using the Gemma 4 tokenizer without special tokens. A record may be an article, a literary work, an excerpt or a sentence, depending on the source.

The distributions below use the declared source language (`source_language`); translations are counted with their source.

<details>
<summary>filtered: distribution by source language</summary>

| Source language | Code | Records | Record share | Tokens | Token share |
|---|---|---:|---:|---:|---:|
| Russian | `rus` | 17,299 | 0.5794% | 789,172,812 | 19.7787% |
| Bashkir | `bak` | 491,794 | 16.4732% | 689,390,975 | 17.2779% |
| Tatar | `tat` | 599,197 | 20.0708% | 535,045,840 | 13.4096% |
| Kazakh | `kaz` | 346,029 | 11.5906% | 487,209,802 | 12.2107% |
| Uzbek | `uzb` | 352,852 | 11.8192% | 407,081,528 | 10.2025% |
| Belarusian | `bel` | 234,752 | 7.8633% | 395,883,536 | 9.9219% |
| Kyrgyz | `kir` | 353,217 | 11.8314% | 229,009,384 | 5.7396% |
| Sakha (Yakut) | `sah` | 94,522 | 3.1661% | 115,948,096 | 2.9060% |
| Kabardian | `kbd` | 21,466 | 0.7190% | 46,175,376 | 1.1573% |
| Chechen | `che` | 41,209 | 1.3803% | 44,161,085 | 1.1068% |
| Lezgi | `lez` | 19,191 | 0.6428% | 30,692,403 | 0.7692% |
| Chuvash | `chv` | 50,393 | 1.6880% | 30,672,857 | 0.7687% |
| Udmurt | `udm` | 25,160 | 0.8428% | 22,535,813 | 0.5648% |
| Tuvan | `tyv` | 15,330 | 0.5135% | 20,724,278 | 0.5194% |
| Khanty | `kca` | 13,135 | 0.4400% | 16,860,557 | 0.4226% |
| Mansi | `mns` | 203,731 | 6.8242% | 16,627,771 | 0.4167% |
| Khakas | `kjh` | 11,351 | 0.3802% | 15,449,350 | 0.3872% |
| Komi-Zyrian | `kpv` | 11,984 | 0.4014% | 15,308,759 | 0.3837% |
| Karachay-Balkar | `krc` | 12,755 | 0.4272% | 15,138,986 | 0.3794% |
| Southern Altai | `alt` | 5,623 | 0.1883% | 13,898,826 | 0.3483% |
| Buryat | `bxr` | 9,265 | 0.3103% | 12,051,140 | 0.3020% |
| Meadow Mari | `mhr` | 12,954 | 0.4339% | 10,156,359 | 0.2545% |
| Erzya | `myv` | 7,679 | 0.2572% | 8,314,047 | 0.2084% |
| Multilingual sources | `mul` | 17,907 | 0.5998% | 7,153,913 | 0.1793% |
| Livvi-Karelian | `olo` | 5,257 | 0.1761% | 5,121,126 | 0.1283% |
| Karelian | `krl` | 5,120 | 0.1715% | 4,328,175 | 0.1085% |
| Veps | `vep` | 3,790 | 0.1270% | 2,811,756 | 0.0705% |
| Kumyk | `kum` | 254 | 0.0085% | 1,136,101 | 0.0285% |
| Moksha | `mdf` | 354 | 0.0119% | 539,479 | 0.0135% |
| Kalmyk | `xal` | 997 | 0.0334% | 528,473 | 0.0132% |
| Ludian | `lud` | 615 | 0.0206% | 492,043 | 0.0123% |
| Hill Mari | `mrj` | 204 | 0.0068% | 177,974 | 0.0045% |
| Komi-Permyak | `koi` | 11 | 0.0004% | 148,487 | 0.0037% |
| Unspecified source language | `und` | 4 | 0.0001% | 30,925 | 0.0008% |
| Nogai | `nog` | 13 | 0.0004% | 19,998 | 0.0005% |
| Northern Altai | `atv` | 8 | 0.0003% | 14,407 | 0.0004% |
| **Total** | | **2,985,422** | **100.0000%** | **3,990,012,437** | **100.0000%** |

</details>

<details>
<summary>unfiltered: distribution by source language</summary>

| Source language | Code | Records | Record share | Tokens | Token share |
|---|---|---:|---:|---:|---:|
| Russian | `rus` | 18,801 | 0.6171% | 809,266,734 | 19.6176% |
| Bashkir | `bak` | 516,532 | 16.9538% | 776,125,605 | 18.8142% |
| Tatar | `tat` | 608,590 | 19.9753% | 545,701,048 | 13.2284% |
| Kazakh | `kaz` | 347,920 | 11.4195% | 490,823,165 | 11.8981% |
| Uzbek | `uzb` | 352,897 | 11.5829% | 407,233,698 | 9.8718% |
| Belarusian | `bel` | 234,906 | 7.7102% | 396,201,630 | 9.6044% |
| Kyrgyz | `kir` | 353,358 | 11.5980% | 229,272,199 | 5.5578% |
| Sakha (Yakut) | `sah` | 95,244 | 3.1261% | 117,509,727 | 2.8486% |
| Kabardian | `kbd` | 25,241 | 0.8285% | 53,874,649 | 1.3060% |
| Chechen | `che` | 41,238 | 1.3535% | 44,196,457 | 1.0714% |
| Lezgi | `lez` | 19,308 | 0.6337% | 31,264,322 | 0.7579% |
| Chuvash | `chv` | 50,400 | 1.6542% | 30,705,760 | 0.7443% |
| Udmurt | `udm` | 25,817 | 0.8474% | 23,038,646 | 0.5585% |
| Tuvan | `tyv` | 15,397 | 0.5054% | 20,827,966 | 0.5049% |
| Khanty | `kca` | 13,155 | 0.4318% | 16,986,127 | 0.4118% |
| Mansi | `mns` | 218,015 | 7.1558% | 16,977,575 | 0.4116% |
| Komi-Zyrian | `kpv` | 12,947 | 0.4250% | 15,797,237 | 0.3829% |
| Karachay-Balkar | `krc` | 13,394 | 0.4396% | 15,782,106 | 0.3826% |
| Khakas | `kjh` | 11,359 | 0.3728% | 15,561,748 | 0.3772% |
| Southern Altai | `alt` | 5,691 | 0.1868% | 14,135,136 | 0.3427% |
| Buryat | `bxr` | 9,271 | 0.3043% | 12,093,101 | 0.2932% |
| Meadow Mari | `mhr` | 12,994 | 0.4265% | 10,193,703 | 0.2471% |
| Erzya | `myv` | 8,375 | 0.2749% | 8,720,583 | 0.2114% |
| Multilingual sources | `mul` | 18,060 | 0.5928% | 7,164,586 | 0.1737% |
| Livvi-Karelian | `olo` | 6,005 | 0.1971% | 5,365,392 | 0.1301% |
| Karelian | `krl` | 5,499 | 0.1805% | 4,464,484 | 0.1082% |
| Veps | `vep` | 3,791 | 0.1244% | 2,813,366 | 0.0682% |
| Kumyk | `kum` | 254 | 0.0083% | 1,136,118 | 0.0275% |
| Moksha | `mdf` | 396 | 0.0130% | 566,103 | 0.0137% |
| Kalmyk | `xal` | 997 | 0.0327% | 528,506 | 0.0128% |
| Ludian | `lud` | 615 | 0.0202% | 492,253 | 0.0119% |
| Hill Mari | `mrj` | 204 | 0.0067% | 177,974 | 0.0043% |
| Komi-Permyak | `koi` | 11 | 0.0004% | 148,487 | 0.0036% |
| Unspecified source language | `und` | 4 | 0.0001% | 30,925 | 0.0007% |
| Nogai | `nog` | 13 | 0.0004% | 19,998 | 0.0005% |
| Northern Altai | `atv` | 8 | 0.0003% | 14,407 | 0.0003% |
| **Total** | | **3,046,707** | **100.0000%** | **4,125,211,521** | **100.0000%** |

</details>

## Filtering

- **Text cleaning.** Confirmed website templates, navigation elements, widgets, utility links and credits are removed. Line breaks, split words and whitespace are repaired.
- **Quality filtering.** Empty and clearly corrupted texts, error and access-challenge pages, unparsed binary documents and degenerate repetition are removed.
- **Exact deduplication.** Document duplicates are identified after Unicode NFC and whitespace normalization.
- **Near-duplicate removal.** MinHash over word 5-grams retrieves candidates, which are compared using their full texts within the same source-language group. Texts must contain at least 80 words, have a length ratio of at least 0.98 and identical numeric sequences. Removal requires either an identical complete normalized word sequence, or a matching title, Jaccard similarity of at least 0.98 and complete ordered containment of the shorter text in the retained text. Source-language groups with fewer than 2 million original tokens require identical complete word sequences.
- **Language annotation.** GlotLID v3, based on fastText, identifies document and paragraph languages. Context and verified publisher labels for individual texts or translation columns help resolve short or ambiguous passages.

Meaningful repetition in verse, refrains, quotations and parallel writing systems is preserved.

Records include source provenance, source category and language annotations. Source rights and license information, where available, is retained in `rights_status` and `license_url`.

## License

The SNeG-data-1.0 database compilation is licensed under the [ODC Attribution License (ODC-By) 1.0](./LICENSE), to the extent of the compiler's rights in the selection and arrangement of the materials. The license permits personal, educational and commercial use of the database. Public use of the database, derivative databases or works produced from it requires attribution linking to [SNeG-data-1.0](https://huggingface.co/datasets/d0rj/SNeG-data-1.0) and [ODC-By 1.0](https://opendatacommons.org/licenses/by/1-0/), as specified in the license.

Individual source texts remain subject to their respective rights holders' copyright.

## Citation

```bibtex
@misc{sneg_data_2026,
  author       = {{d0rj}},
  title        = {{SNeG-data-1.0}},
  year         = {2026},
  howpublished = {Hugging Face},
  url          = {https://huggingface.co/datasets/d0rj/SNeG-data-1.0}
}
```