| ---
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| language:
|
| - ba
|
| - tt
|
| - ru
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| license: apache-2.0
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| pretty_name: Bashkir Multiclass LID
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| library_name: onnxruntime
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| pipeline_tag: text-classification
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| tags:
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| - bashkir
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| - tatar
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| - russian
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| - language-identification
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| - multilingual
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| - text-classification
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| - onnx
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| - onnxruntime
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| - low-resource
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| - open-source
|
| ---
|
|
|
| # Bashkir Multiclass LID
|
|
|
| > Compact ONNX language identification for `ba`, `tt`, `ru` and `other`, for
|
| > Bashkir corpus filtering and local CPU inference.
|
|
|
| ## Overview
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|
|
| A compact ONNX language-identification model for Bashkir corpus filtering and
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| local CPU applications. It predicts four classes: `ba` (Bashkir), `tt` (Tatar),
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| `ru` (Russian) and `other` (other languages, code, URLs, technical noise and
|
| corrupted text). It is designed to separate closely related Bashkir and Tatar
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| text while preventing foreign-language and technical material from being silently
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| classified as Bashkir.
|
|
|
| | At a glance | |
|
| | --- | --- |
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| | Task | Four-class language identification |
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| | Default artifact | `model.onnx` + `lid.py` |
|
| | Classes | `ba`, `tt`, `ru`, `other` |
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| | Source | Bashkir, Tatar, Russian and technical text (not redistributed) |
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| | Version / license | v3 / Apache-2.0 |
|
|
|
| ## Contents
|
|
|
| ### Files and Configurations
|
|
|
| | File | Purpose |
|
| | --- | --- |
|
| | `model.onnx` | Exported classifier graph and weights (opset 17) |
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| | `vectorizer.json` | Character and word feature vocabularies |
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| | `lid.py` | Portable ONNX Runtime adapter |
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| | `config.json` | Runtime contract and feature settings |
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| | `META.json` | Model passport, validation results and artifact hashes |
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| | `requirements.txt` | Minimal runtime dependencies |
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| | `LICENSE` | Full license text |
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| | `SHA256SUMS` | Release checksums |
|
|
|
| Training texts and the original `joblib` pipeline are not distributed.
|
|
|
| ### Model Architecture
|
|
|
| | Property | Description |
|
| | --- | --- |
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| | Task | Four-class language identification |
|
| | Classes | `ba`, `tt`, `ru`, `other` |
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| | Character features | TF-IDF `char_wb` n-grams, lengths 2–5 |
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| | Word features | TF-IDF word n-grams, lengths 1–2 |
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| | Classifier | `SGDClassifier(loss="log_loss")` |
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| | Export | ONNX, opset 17 |
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| | Runtime | ONNX Runtime on CPU |
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| | Input handling | Unicode-aware adapter in `lid.py` |
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|
|
| The ONNX graph consumes sparse feature tensors rather than raw strings; the
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| adapter performs tokenization, feature lookup, batching and checksum validation.
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| No custom ONNX operators or Transformers installation are needed.
|
|
|
| ### Examples
|
|
|
| | Text | Prediction | Top probability |
|
| | --- | --- | ---: |
|
| | `Бары бала бара.` | `ba` | 0.829 |
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| | `Бүген Казанда яңа мәдәни үзәк ачылды.` | `tt` | 0.919 |
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| | `В Уфе открыли новый корпус университета.` | `ru` | 0.930 |
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| | `SELECT id FROM users WHERE active = TRUE;` | `other` | 0.982 |
|
|
|
| ## Method
|
|
|
| The model is a dual-channel linear classifier: TF-IDF `char_wb` (2–5) and word
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| (1–2) features are fused and passed to an `SGDClassifier` exported to ONNX. It is
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| trained on Bashkir, Tatar, Russian and other-language material with technical
|
| noise. Training texts are not distributed in this repository.
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|
|
| ```text
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| multilingual training corpus (Bashkir, Tatar, Russian & noise)
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| ├── 1. extract character n-grams: char_wb (2–5)
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| ├── 2. extract word n-grams: words (1–2)
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| ├── 3. feature union & TF-IDF weighting
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| ├── 4. train multi-class SGDClassifier with calibrated log-loss
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| └── 5. export optimized ONNX graph (4-way: ba / tt / ru / other)
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| ```
|
|
|
| ### Evaluation
|
|
|
| The clean multi-source benchmark contains 1,000 examples each from Bashkir,
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| Tatar, Russian and other-language/noise sources; the adversarial set includes
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| closely related Bashkir/Tatar pairs, code-switching, Kazakh, Chuvash, Russian
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| regional text and technical noise.
|
|
|
| | Evaluation | Result |
|
| | --- | ---: |
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| | Clean multi-source benchmark | 92.67% accuracy (3,707/4,000) |
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| | Bashkir subset | 81.70% recall (817/1,000) |
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| | Tatar subset | 100.00% recall (1,000/1,000) |
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| | Russian subset | 91.40% recall (914/1,000) |
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| | Other subset | 97.60% recall (976/1,000) |
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| | Linguistic adversarial stress test | 39/39 (100%) |
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| | ONNX / sklearn label parity | 0 disagreements |
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|
|
| The benchmark is not expert-reviewed Gold and some examples come from the same
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| source pools used during model development; source overlap was not fully
|
| excluded. Exact validation fields are recorded in [`META.json`](META.json).
|
|
|
| ## Quality and Use
|
|
|
| Mixed-language text does not have a separate class: predictions should be read as
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| the dominant language of the sentence. In a corpus pipeline, confident Bashkir can
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| be retained, while uncertain BA/TT or mixed cases should be sent to a review queue
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| instead of being deleted automatically. Short sentences with common Turkic
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| vocabulary remain intrinsically ambiguous.
|
|
|
| ### Limitations
|
|
|
| - A lightweight text classifier, not a universal language detector.
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| - The reported benchmark is not an expert-reviewed Gold benchmark.
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| - Confidence values are model scores, not calibrated human probabilities.
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| - Empty strings, OCR fragments and technical noise should be handled with explicit pipeline rules.
|
|
|
| ## Related Resources
|
|
|
| - [Bashkir LID (Binary BA vs NON_BA)](https://huggingface.co/failed09/bashkir-lid) —
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| a strict `ba` vs `non_ba` gate with configurable thresholds; use it when only a
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| pass/reject decision is needed.
|
|
|
| ## Usage
|
|
|
| ```bash
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| pip install huggingface_hub onnxruntime numpy
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| ```
|
|
|
| ```python
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| import sys
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| from huggingface_hub import snapshot_download
|
|
|
| model_dir = snapshot_download(
|
| "failed09/bashkir-lid-multiclass",
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| allow_patterns=[
|
| "lid.py", "config.json", "META.json", "model.onnx", "vectorizer.json"
|
| ],
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| revision="main", # pin a reviewed commit for reproducible deployments
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| )
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| sys.path.insert(0, model_dir)
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| from lid import LanguageIdentifier
|
|
|
| lid = LanguageIdentifier(model_dir=model_dir)
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| texts = [
|
| "Бары бала бара.",
|
| "Бүген Казанда яңа мәдәни үзәк ачылды.",
|
| "В Уфе открыли новый корпус университета.",
|
| "SELECT id FROM users WHERE active = TRUE;",
|
| ]
|
|
|
| print(lid.predict(texts).tolist())
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| print(lid.predict_proba(texts))
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| print(lid.classes.tolist()) # probability-column order
|
| ```
|
|
|
| After downloading, inference runs locally without network access. The
|
| probability-column order is always the order in [`META.json`](META.json).
|
|
|
| ## License
|
|
|
| Distributed under the [Apache-2.0 license](https://huggingface.co/failed09/bashkir-lid-multiclass/blob/main/LICENSE).
|
| This release contains derived model and tokenizer assets, not the source corpus,
|
| scans or original document layouts.
|
|
|
| ## Citation
|
|
|
| ```bibtex
|
| @software{failed09_bashkir_multiclass_lid_2026,
|
| title = {Bashkir Multiclass LID},
|
| author = {failed09},
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| year = {2026},
|
| publisher = {Hugging Face},
|
| url = {https://huggingface.co/failed09/bashkir-lid-multiclass},
|
| note = {Open-source four-class language identification for Bashkir corpus filtering}
|
| }
|
| ```
|
|
|
| ## 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.
|
|
|
