failed09
/

bashkir-fasttext

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README.md CHANGED Viewed

@@ -1,165 +1,166 @@
----
-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 |
-| `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 → Unicode NFC tokenization → skip-gram 300d (char n-grams 3–6)
-    → prune (top-50k words, top-100k subwords) → PQ FP16 → 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://www.apache.org/licenses/LICENSE-2.0). 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.

+---
+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 → Unicode NFC tokenization → skip-gram 300d (char n-grams 3–6)
+    → prune (top-50k words, top-100k subwords) → PQ FP16 → 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](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.

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