Datasets:
Fix dataset card schema, english documentation and bibtex citation
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README.md
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---
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language:
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- ba
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size_categories:
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- 100K<n<1M
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task_categories:
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tags:
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- bashkir
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- lexicon
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---
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#
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> **Дата обновления**: `31 August 2026`
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> **Базовый корпус**: **6 876 733 предложений** / **105.0+ млн токенов** из 81 источника (пресса, журналы, классическая литература, фольклор, википедия).
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> **Лицензия**: Creative Commons Attribution 4.0 (CC-BY-4.0)
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* 🎯 **Ядро 50% покрытия языка**: **1,730 слов** покрывают половину всей устной и письменной речи (`top_coverage_50.json`).
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* ⚡ **Топ-500 базовых слов**: компактный JSON (`top_500.json`).
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| 4 | **ла** | частица | 538,662 | 5,129.3 |
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| 5 | **ул** | местоимение «он / она / оно» | 524,257 | 4,992.2 |
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| 6 | **да** | частица | 479,660 | 4,567.5 |
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| 7 | **өсөн** | «для / ради» | 463,346 | 4,412.2 |
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| 8 | **был** | «этот» | 448,375 | 4,269.6 |
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| 9 | **тип** | «говоря / как» | 381,217 | 3,630.1 |
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| 10 | **йыл** | «год» | 373,881 | 3,560.2 |
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```python
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df = pd.read_parquet("https://huggingface.co/datasets/failed09/bashkir-frequency-index/resolve/main/frequency_public.parquet")
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print(df.head(10))
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```
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---
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license: cc-by-4.0
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language:
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- ba
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pretty_name: Bashkir Frequency Index (105M tokens, 81 Sources Super Corpus)
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task_categories:
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- text-classification
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configs:
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- config_name: public
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data_files:
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- split: train
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path: frequency_public.parquet
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- config_name: full
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data_files:
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- split: train
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path: frequency_full.parquet
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tags:
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- bashkir
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- frequency
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- wordlist
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- lexicon
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- low-resource
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- cyrillic
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- corpus-statistics
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---
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# Bashkir Frequency Index v8.1
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Word-frequency index for the Bashkir language, built from **105.01 million tokens** of clean monolingual text across **81 diverse authentic sources** (republican newspapers, literary magazines, 28 district regional press editions, digital library Kitaptar, classical literature, folklore archives, speech transcripts, and the complete Bashkir Wikipedia universe). Russian language admixture and OCR scanning noise were removed via sentence-level filtering and Diamond Clean v2 pipeline.
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## Configurations
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| Config | Rows | Cutoff | Use Case |
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|---|---:|---|---|
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| **`public`** (recommended) | **634,193** | count ≥ 3 | Production NLP applications, spellcheckers, Huzhle, Duolingo-style SRS, T9 |
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| **`full`** | **858,284** | count ≥ 2 | Comprehensive morphology studies, dialect research, linguistic analysis |
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Additionally provided in the repository:
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- [`top_coverage_50.json`](top_coverage_50.json): the minimal set of **1,730 word forms covering 50% of all tokens** across the language.
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- [`top_500.json`](top_500.json): clean top-500 core vocabulary list for quick mobile lookups.
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## Schema
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| Column | Type | Description |
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|---|---|---|
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| `rank` | int64 | Position by descending frequency |
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| `word` | string | Word form, lowercase Cyrillic |
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| `count` | int64 | Absolute frequency count (total occurrences) |
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| `doc_freq` | int64 | Number of distinct sentences containing this word form |
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| `freq_per_million` | double | Normalized instances per million tokens (FPM / IPM) |
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## Sample (Top 10 Words)
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| Rank | Word | Translation / Function | Count | FPM | Doc Frequency |
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| 1 | **һәм** | conjunction "and" | 1,419,398 | 13,516.0 | 1,024,812 |
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| 2 | **менән** | postposition "with" | 974,143 | 9,276.1 | 759,204 |
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| 3 | **бер** | numeral "one / a certain" | 662,833 | 6,311.7 | 508,419 |
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| 4 | **ла** | particle "also / and" | 538,662 | 5,129.3 | 431,092 |
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| 5 | **ул** | pronoun "he / she / it / that" | 524,257 | 4,992.2 | 412,850 |
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| 6 | **да** | particle "also / and" | 479,660 | 4,567.5 | 390,114 |
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| 7 | **өсөн** | postposition "for / sake of" | 463,346 | 4,412.2 | 382,905 |
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| 8 | **был** | pronoun "this" | 448,375 | 4,269.6 | 365,712 |
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| 9 | **тип** | quotative gerund "saying / as" | 381,217 | 3,630.1 | 314,098 |
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| 10 | **йыл** | noun "year" | 373,881 | 3,560.2 | 301,450 |
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## Token Coverage
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| Tokens Covered | Forms Needed | Note |
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| **25%** | **197** | Ultra-core grammatical connectives and pronouns |
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| **50%** | **1,730** | Essential high-frequency lexicon |
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| **80%** | **18,315** | Conversational and daily news proficiency |
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| **90%** | **55,287** | Advanced literary & administrative reading |
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| **95%** | **129,354** | Comprehensive vocabulary mastery |
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## Method and Cleanups
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- **Tokenization:** Regex lowercase tokenization over Cyrillic alphabet including all 9 Bashkir-specific letters (`Ә Ғ Ҙ Ҡ Ң Ө Ҫ Ү Һ`).
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- **Base Corpus:** 6,876,733 clean deduplicated sentences (105.01M tokens) from 81 leaf publications and archives.
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- **Russian Function Words Filter:** Context-verified removal of Russian function words (quotes, names, headers) that do not have homographs in Bashkir.
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- **Length and Noise Filters:** Glued words (>24 characters) and isolated non-Bashkir single letters dropped (kept single `ә`).
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## Loading
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```python
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from datasets import load_dataset
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# Load recommended public configuration
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dataset = load_dataset("failed09/bashkir-frequency-index", "public")
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df = dataset["train"].to_pandas()
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# Inspect top-10 words
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print(df.head(10))
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```
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## Citation
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```bibtex
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@dataset{failed09_bashkir_frequency_index_2026,
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title = {Bashkir Frequency Index v8.1},
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author = {failed09},
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year = {2026},
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publisher = {Hugging Face},
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url = {https://huggingface.co/datasets/failed09/bashkir-frequency-index}
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}
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```
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