README.md

---
language:
- ba
license: apache-2.0
pretty_name: Bashkir FastText Word Embeddings (300d, Mini Beta)
pipeline_tag: feature-extraction
tags:
- fasttext
- embeddings
- word-embeddings
- feature-extraction
- bashkir
- turkic
- subwords
- nlp
---

# Bashkir FastText Word Embeddings (300d, Mini Beta)

> Compact 17 MB subword word embeddings for Bashkir, built for semantic search,
> lexical tools and on-device NLP.

## Overview

Compact subword word embeddings for Bashkir (`ba`), trained on a monolingual
Bashkir-language dataset. The release ships a single
quantized model (`bashkir_fasttext_300d_mini.bin`) intended for semantic search,
lexical tools and lightweight NLP experiments on CPU, mobile and edge devices.

| At a glance | |
| --- | --- |
| Task | Feature extraction / word embeddings |
| Default artifact | `bashkir_fasttext_300d_mini.bin` |
| Source | A monolingual Bashkir-language dataset |
| Version / license | v0.1-beta / Apache-2.0 |

## Contents

### Files and Configurations

| File | Description |
| --- | --- |
| `bashkir_fasttext_300d_mini.bin` | Quantized 300-dimensional FastText model; recommended for most users |
| `config.json` | Architecture and runtime parameters |
| `META.json` | Release passport: version, size, source and artifact hashes |
| `LICENSE` | Full license text |
| `SHA256SUMS` | SHA-256 checksums for public release files |

The full model and text-vector export are not included in this release.

### Model Architecture

| Property | Value |
| --- | --- |
| Architecture | FastText Skip-gram with character subwords |
| Embedding dimension | 300 |
| Character n-grams | 3–6 |
| Active vocabulary | top 50,000 words and top 100,000 subword n-grams |
| Compression | Product Quantization with FP16 codebooks |
| Model size | 17.01 MB |
| OOV support | vectors synthesized for unseen word forms and suffixes |
| Format | `compress-fasttext` / Gensim-compatible |
| Runtime | CPU; no GPU required |

### Examples

Subword training lets the model connect a root with its inflected forms, for
example `мәктәп` → `мәктәптәр` → `мәктәптәребеҙҙә`, and return semantic
neighbours for words such as `матур`, `атай`, `тел` and `ҡояш`, even when a
form was never seen during training.

## Method

```text
monolingual Bashkir text (clean corpus)
  ├── 1. Unicode NFC tokenization preserving 9 Bashkir letters
  ├── 2. train skip-gram 300d with subword character n-grams (3–6)
  ├── 3. prune vocabulary to top-50k words and top-100k subwords
  └── 4. product quantization (PQ FP16) export to mini .bin
```

The model was trained from a monolingual Bashkir-language dataset.
Corpus size, source reference and training parameters are recorded in
[`config.json`](config.json). Text preparation uses normalized Unicode tokens and
preserves Bashkir-specific letters. This release is a compact derivative of the
full 300-dimensional model: it retains subword information while reducing the
vocabulary and model footprint for local, mobile and edge use.

## Quality and Use

This is a **beta diagnostic release**, not a standardized linguistic benchmark.
Independent stress tests showed strong morphology and OOV behaviour, and the full
and compact variants produced near-identical results. The official Facebook
Bashkir vectors are a useful general-purpose baseline:
[`facebook/fasttext-ba-vectors`](https://huggingface.co/facebook/fasttext-ba-vectors).

### Limitations

- Large web-derived corpora may contain borrowed vocabulary, foreign names,
  technical terms, OCR errors and concatenated tokens.
- Raw nearest-neighbour lookups can occasionally return concatenated headlines or
  compound hyphenations from the source text.
- Similarity scores are embeddings-based, not human judgments.
- Nearest-neighbour results should be validated before use in a dictionary,
  spellchecker or production pipeline; combine the model with frequency data,
  canonical word lists and language filtering where appropriate.

## Related Resources

- [Bashkir Multiclass LID](https://huggingface.co/failed09/bashkir-lid-multiclass) —
  gate mixed Bashkir/Tatar/Russian input before embedding-based processing.
- [Bashkir Frequency Index](https://huggingface.co/datasets/failed09/bashkir-frequency-index) —
  canonical word frequencies to validate nearest neighbours and rank spellcheck candidates.

## Usage

```bash
pip install compress-fasttext huggingface_hub
```

```python
from huggingface_hub import hf_hub_download
from compress_fasttext.models import CompressedFastTextKeyedVectors

path = hf_hub_download(
    repo_id="failed09/bashkir-fasttext",
    filename="bashkir_fasttext_300d_mini.bin",
    revision="main",  # pin a reviewed commit for reproducible deployments
)
model = CompressedFastTextKeyedVectors.load(path)

vector = model.get_vector("мәктәптәребеҙҙә")
print(vector.shape)  # (300,)
print(model.most_similar("матур", topn=5))
print(model.similarity("атай", "әсәй"))
```

## License

The model and release code are provided under the
[Apache-2.0 license](https://huggingface.co/failed09/bashkir-fasttext/blob/main/LICENSE). The model
is derived from upstream and project corpora; this release does not claim
ownership of source texts or other training materials. Upstream rights and
licensing conditions remain applicable.

## Citation

```bibtex
@software{failed09_bashkir_fasttext_embeddings_2026,
  title = {Bashkir FastText Word Embeddings},
  author = {failed09},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/failed09/bashkir-fasttext},
  note = {Compact beta subword embeddings for Bashkir}
}
```

## Open Bashkir Data and Sources 🐝

This release is part of an open-source effort to support the development,
preservation and practical use of the Bashkir language. Other related models,
datasets and tools are available on the author's Hugging Face profile.

The author does not claim ownership or authorship of the source texts or other
materials used to derive this release; rights and licensing remain with the
original authors, publishers and dataset providers. Source texts are not
redistributed in this repository, so users should follow the licenses and
attribution requirements of the relevant upstream resources.