Release update

LICENSE

@@ -0,0 +1,202 @@
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META.json

@@ -4,7 +4,7 @@
   "type": "masked_language_model",
   "status": "current",
   "language": "ba",
-  "license": "other",
+  "license": "apache-2.0",
   "source": "a monolingual Bashkir-language dataset",
   "created_at": "2026-09-17T18:20:06Z",
   "architecture": "Pre-LayerNorm Transformer encoder",

README.md

@@ -1,182 +1,184 @@
----
-language:
-- ba
-license: other
-pretty_name: BashkirRoBERTa
-library_name: transformers
-pipeline_tag: fill-mask
-tags:
-- bashkir
-- masked-language-modeling
-- roberta
-- sentencepiece
-- custom-code
-- onnx
-- onnxruntime
----
-
-# BashkirRoBERTa
-
-> A masked language model for Bashkir, for fill-mask, spellchecking and
-> foundation fine-tuning.
-
-## Overview
-
-A masked language model for Bashkir. Given a sentence with one `[MASK]` token, it
-predicts the most probable missing Bashkir token from context. The model is useful
-for fill-mask experiments, spellchecking and as a foundation for further
-fine-tuning. It preserves a custom Pre-LayerNorm architecture rather than the
-stock post-LayerNorm RoBERTa implementation.
-
-| At a glance | |
-| --- | --- |
-| Task | Masked language modelling / fill-mask |
-| Default artifact | `model.safetensors` (Transformers) or `onnx/model_int8.onnx` (ONNX) |
-| Source | A monolingual Bashkir-language dataset |
-| Version / license | v1 / custom terms (`other`) |
-
-## Contents
-
-### Files and Configurations
-
-| File | Purpose | Size |
-| --- | --- | ---: |
-| `model.safetensors` | PyTorch weights for Transformers | 200.2 MB |
-| `onnx/model_fp16.onnx` | FP16 ONNX model for GPU / DirectML | 121.2 MB |
-| `onnx/model_int8.onnx` | INT8 ONNX model for fast CPU / mobile | 60.9 MB |
-| `spm_bashkir_bert_16k.model` | SentencePiece tokenizer | — |
-| `config.json` | Model configuration (`auto_map` for custom code) | — |
-| `configuration_bashkir_roberta.py`, `modeling_bashkir_roberta.py`, `tokenization_bashkir_roberta.py` | Custom Pre-LayerNorm implementation | — |
-| `tokenizer_config.json` | Tokenizer configuration | — |
-| `META.json` | Release passport and artifact hashes | — |
-| `SHA256SUMS` | Release checksums | — |
-
-### Model Architecture
-
-| Property | Value |
-| --- | --- |
-| Task | Masked language modelling / fill-mask |
-| Architecture | Pre-LayerNorm Transformer encoder |
-| Transformer blocks | 8 |
-| Hidden size / attention heads | 640 / 10 |
-| Feed-forward size | 2,560 |
-| Context window | 256 subword tokens |
-| Parameters | 50.04M |
-| Tokenizer | SentencePiece BPE, 16,384 tokens |
-
-The output embedding matrix is tied to the input word embeddings. Token IDs are
-fixed: `<pad>` 0, `<unk>` 1, `<s>` 2, `</s>` 3, `[CLS]` 4, `[SEP]` 5 and
-`[MASK]` 6.
-
-### Examples
-
-Outputs from the INT8 ONNX model on CPU:
-
-| Input | Top prediction |
-| --- | --- |
-| `Мин башҡорт телен [MASK].` | `яратам` |
-| `Башҡортостан — беҙҙең [MASK].` | `республика` |
-| `Өфө — ҙур [MASK].` | `ҡала` |
-| `Бөгөн Өфөлә яңы [MASK] асылды.` | `мәсет` |
-
-## Method
-
-The model was pretrained with dynamic masked-language modelling on a monolingual
-Bashkir-language dataset assembled from encyclopedic, periodical and literary
-sources. The source texts are not distributed in this repository.
-
-### Evaluation
-
-On a held-out Bashkir encyclopedic evaluation set the project reports **24.7%
-top-1** and **54.0% top-5** accuracy for masked subword prediction. These are
-diagnostic MLM results, not a general-purpose language-understanding score: a mask
-may represent a whole word or a SentencePiece subword fragment.
-
-## Quality and Use
-
-This is a research model, not a production language service. Fill-mask predictions
-are ranking suggestions that require context-appropriate review, especially for
-ambiguous or short contexts. The checkpoint is released under custom terms while
-the source-rights audit is completed.
-
-### Limitations
-
-- Diagnostic MLM accuracy only; not fine-tuned for any downstream task.
-- A mask may correspond to a partial subword, not always a full word.
-- Predictions reflect the training corpus and may prefer frequent or encyclopedic phrasing.
-- No training texts are redistributed; provenance or removal requests go through the maintainer.
-
-## Usage
-
-```bash
-pip install transformers torch huggingface_hub
-```
-
-PyTorch (Transformers), which requires `trust_remote_code=True` because of the
-custom Pre-LayerNorm architecture:
-
-```python
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-
-repo_id = "failed09/bashkir-roberta"
-tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
-model = AutoModelForMaskedLM.from_pretrained(repo_id, trust_remote_code=True)
-
-inputs = tokenizer("Мин башҡорт телен [MASK].", return_tensors="pt")
-logits = model(**inputs).logits
-mask_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id)
-prediction_id = logits[0, mask_index].argmax().item()
-print(tokenizer.decode([prediction_id]))  # яратам
-```
-
-ONNX Runtime for CPU and edge deployment:
-
-```python
-import numpy as np
-import onnxruntime as ort
-import sentencepiece as spm
-from huggingface_hub import hf_hub_download
-
-model_path = hf_hub_download("failed09/bashkir-roberta", "onnx/model_int8.onnx")
-sp_path = hf_hub_download("failed09/bashkir-roberta", "spm_bashkir_bert_16k.model")
-
-session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
-sp = spm.SentencePieceProcessor(model_file=sp_path)
-
-tokens = [2] + sp.encode("Мин башҡорт телен ") + [6] + sp.encode(".") + [3]
-mask_idx = tokens.index(6)
-logits = session.run(None, {"input_ids": np.array([tokens], dtype=np.int64)})[0][0, mask_idx]
-top_tokens = np.argsort(logits)[::-1][:5]
-print([sp.decode([int(t)]) for t in top_tokens])  # ['яратам', 'беләм', 'өйрәнә', ...]
-```
-
-## License
-
-The checkpoint is released under custom terms (`other` on the Hub) while the
-source-rights audit is completed. No training texts are redistributed. For
-provenance or removal requests, contact the maintainer through the Hub.
-
-## Citation
-
-```bibtex
-@software{failed09_bashkir_roberta_2026,
-  title = {BashkirRoBERTa},
-  author = {failed09},
-  year = {2026},
-  publisher = {Hugging Face},
-  url = {https://huggingface.co/failed09/bashkir-roberta},
-  note = {Masked language model 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: BashkirRoBERTa
+library_name: transformers
+pipeline_tag: fill-mask
+tags:
+- bashkir
+- masked-language-modeling
+- roberta
+- sentencepiece
+- custom-code
+- onnx
+- onnxruntime
+---
+
+# BashkirRoBERTa
+
+> A masked language model for Bashkir, for fill-mask, spellchecking and
+> foundation fine-tuning.
+
+## Overview
+
+A masked language model for Bashkir. Given a sentence with one `[MASK]` token, it
+predicts the most probable missing Bashkir token from context. The model is useful
+for fill-mask experiments, spellchecking and as a foundation for further
+fine-tuning. It preserves a custom Pre-LayerNorm architecture rather than the
+stock post-LayerNorm RoBERTa implementation.
+
+| At a glance | |
+| --- | --- |
+| Task | Masked language modelling / fill-mask |
+| Default artifact | `model.safetensors` (Transformers) or `onnx/model_int8.onnx` (ONNX) |
+| Source | A monolingual Bashkir-language dataset |
+| Version / license | v1 / Apache-2.0 |
+
+## Contents
+
+### Files and Configurations
+
+| File | Purpose | Size |
+| --- | --- | ---: |
+| `model.safetensors` | PyTorch weights for Transformers | 200.2 MB |
+| `onnx/model_fp16.onnx` | FP16 ONNX model for GPU / DirectML | 121.2 MB |
+| `onnx/model_int8.onnx` | INT8 ONNX model for fast CPU / mobile | 60.9 MB |
+| `spm_bashkir_bert_16k.model` | SentencePiece tokenizer | — |
+| `config.json` | Model configuration (`auto_map` for custom code) | — |
+| `configuration_bashkir_roberta.py`, `modeling_bashkir_roberta.py`, `tokenization_bashkir_roberta.py` | Custom Pre-LayerNorm implementation | — |
+| `tokenizer_config.json` | Tokenizer configuration | — |
+| `META.json` | Release passport and artifact hashes | — |
+| `LICENSE` | Full license text | — |
+| `SHA256SUMS` | Release checksums | — |
+
+### Model Architecture
+
+| Property | Value |
+| --- | --- |
+| Task | Masked language modelling / fill-mask |
+| Architecture | Pre-LayerNorm Transformer encoder |
+| Transformer blocks | 8 |
+| Hidden size / attention heads | 640 / 10 |
+| Feed-forward size | 2,560 |
+| Context window | 256 subword tokens |
+| Parameters | 50.04M |
+| Tokenizer | SentencePiece BPE, 16,384 tokens |
+
+The output embedding matrix is tied to the input word embeddings. Token IDs are
+fixed: `<pad>` 0, `<unk>` 1, `<s>` 2, `</s>` 3, `[CLS]` 4, `[SEP]` 5 and
+`[MASK]` 6.
+
+### Examples
+
+Outputs from the INT8 ONNX model on CPU:
+
+| Input | Top prediction |
+| --- | --- |
+| `Мин башҡорт телен [MASK].` | `яратам` |
+| `Башҡортостан — беҙҙең [MASK].` | `республика` |
+| `Өфө — ҙур [MASK].` | `ҡала` |
+| `Бөгөн Өфөлә яңы [MASK] асылды.` | `мәсет` |
+
+## Method
+
+The model was pretrained with dynamic masked-language modelling on a monolingual
+Bashkir-language dataset assembled from encyclopedic, periodical and literary
+sources. The source texts are not distributed in this repository.
+
+### Evaluation
+
+On a held-out Bashkir encyclopedic evaluation set the project reports **24.7%
+top-1** and **54.0% top-5** accuracy for masked subword prediction. These are
+diagnostic MLM results, not a general-purpose language-understanding score: a mask
+may represent a whole word or a SentencePiece subword fragment.
+
+## Quality and Use
+
+This is a research model, not a production language service. Fill-mask predictions
+are ranking suggestions that require context-appropriate review, especially for
+ambiguous or short contexts. The model weights and release code are available
+under Apache-2.0; source-text provenance remains documented separately.
+
+### Limitations
+
+- Diagnostic MLM accuracy only; not fine-tuned for any downstream task.
+- A mask may correspond to a partial subword, not always a full word.
+- Predictions reflect the training corpus and may prefer frequent or encyclopedic phrasing.
+- No training texts are redistributed; provenance or removal requests go through the maintainer.
+
+## Usage
+
+```bash
+pip install transformers torch huggingface_hub
+```
+
+PyTorch (Transformers), which requires `trust_remote_code=True` because of the
+custom Pre-LayerNorm architecture:
+
+```python
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+repo_id = "failed09/bashkir-roberta"
+tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
+model = AutoModelForMaskedLM.from_pretrained(repo_id, trust_remote_code=True)
+
+inputs = tokenizer("Мин башҡорт телен [MASK].", return_tensors="pt")
+logits = model(**inputs).logits
+mask_index = inputs["input_ids"][0].tolist().index(tokenizer.mask_token_id)
+prediction_id = logits[0, mask_index].argmax().item()
+print(tokenizer.decode([prediction_id]))  # яратам
+```
+
+ONNX Runtime for CPU and edge deployment:
+
+```python
+import numpy as np
+import onnxruntime as ort
+import sentencepiece as spm
+from huggingface_hub import hf_hub_download
+
+model_path = hf_hub_download("failed09/bashkir-roberta", "onnx/model_int8.onnx")
+sp_path = hf_hub_download("failed09/bashkir-roberta", "spm_bashkir_bert_16k.model")
+
+session = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
+sp = spm.SentencePieceProcessor(model_file=sp_path)
+
+tokens = [2] + sp.encode("Мин башҡорт телен ") + [6] + sp.encode(".") + [3]
+mask_idx = tokens.index(6)
+logits = session.run(None, {"input_ids": np.array([tokens], dtype=np.int64)})[0][0, mask_idx]
+top_tokens = np.argsort(logits)[::-1][:5]
+print([sp.decode([int(t)]) for t in top_tokens])  # ['яратам', 'беләм', 'өйрәнә', ...]
+```
+
+## License
+
+The model weights, tokenizer and release code are distributed under the
+[Apache-2.0 license](LICENSE). Training texts are not redistributed; their
+rights remain with their respective owners. For provenance or removal requests,
+contact the maintainer through the Hub.
+
+## Citation
+
+```bibtex
+@software{failed09_bashkir_roberta_2026,
+  title = {BashkirRoBERTa},
+  author = {failed09},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/failed09/bashkir-roberta},
+  note = {Masked language model 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.

SHA256SUMS

@@ -1,5 +1,6 @@
-a56541821a9afbbacaab9e6ce0322748543b3ada068e42956b8993f927ddea27  META.json
-b077d3131f87aa776cecd0c4feb214686c9dc961a0f76cd15658f5f2a6c2fc96  README.md
+cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30  LICENSE
+f6d59748711ddcc45af84d09cf7970a276ee93820a83dda4fb5fda8209f878ca  META.json
+e55a82e7ce32715e0d71e3b9fc809a623ad1d987b4c39e7458534d34439ff149  README.md
 59b773953af5b2a69adb7e3c5d345a90f34f848f6a1543309d5acc7fb78b28c4  config.json
 e9f179aad7619153724920c86a90ecc3c161fd15ffc3332f200e9f2073a07f72  configuration_bashkir_roberta.py
 48e4d4e7b8af675d222e58e14776abac24b7e83a937d7884bd9d735ea9dd2173  model.safetensors