Release update

LICENSE

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

@@ -7,7 +7,7 @@
   "language": [
     "ba"
   ],
-  "license": "unknown",
+  "license": "apache-2.0",
   "source": "Fine-tuned BashkirRoBERTa encoder using Bashkir person-entity annotations; source texts are not redistributed.",
   "base_model": "failed09/bashkir-roberta",
   "created_at": "2026-09-29T19:47:12+00:00",

README.md

@@ -1,185 +1,185 @@
----
-language:
-- ba
-license: unknown
-pretty_name: BashkirRoBERTa NER
-library_name: onnxruntime
-pipeline_tag: token-classification
-tags:
-- bashkir
-- named-entity-recognition
-- person
-- onnx
-- onnxruntime
-- low-resource
----
-
-# BashkirRoBERTa NER
-
-> ONNX models for finding person names in Bashkir text, with a compact CPU default.
-
-## Overview
-
-BashkirRoBERTa NER is a person-name tagger fine-tuned from the BashkirRoBERTa
-masked-language encoder. It recognizes people (`PER`) and returns spans in the
-original text. The release includes FP32 and dynamically quantized INT8 ONNX
-graphs, the original SentencePiece tokenizer, and a small standalone runtime.
-The INT8 model is the default for CPU use.
-
-| At a glance | |
-| --- | --- |
-| Task | Bashkir person named entity recognition (`PER`) |
-| Default artifact | `onnx/model_int8.onnx` |
-| Source | A Bashkir-language encoder and person-entity training annotations |
-| Version / license | v1 / unresolved (`unknown`) |
-
-## Contents
-
-### Files and Configurations
-
-| File | Purpose | Size |
-| --- | --- | ---: |
-| **`onnx/model_int8.onnx`** | CPU default, dynamic INT8 | 82.4 MB |
-| `onnx/model_fp32.onnx` | FP32 reference with PyTorch tag parity | 200.3 MB |
-| `spm_bashkir_bert_16k.model` | SentencePiece tokenizer | 0.59 MB |
-| `runtime.py` | Word alignment, BIO spans, character offsets | — |
-| `config.json` | Architecture, label order, runtime contract | — |
-| `benchmark_summary.json` | Aggregate evaluation without source text | — |
-| `example.py` | Download and run the default model | — |
-| `META.json` | Release passport and artifact hashes | — |
-| `SHA256SUMS` | Checksums of the public files | — |
-
-### Model Architecture
-
-| Property | Value |
-| --- | --- |
-| Encoder | BashkirRoBERTa, eight Pre-LayerNorm Transformer blocks |
-| Head | Linear word-level classifier |
-| Labels | `O`, `B-PER`, `I-PER` |
-| Tokenization | SentencePiece, first piece of each original word is labeled |
-| Input | `input_ids`, int64, dynamic batch and sequence dimensions |
-| Output | `logits`, float, `[batch, sequence, 3]` |
-| Maximum context | 256 SentencePiece tokens, including boundary tokens |
-
-### Examples
-
-Outputs below were produced by the included INT8 runtime:
-
-| Input | Found people |
-| --- | --- |
-| `Рәми Ғарипов һәм Мостай Кәрим тураһында һөйләштек.` | `Рәми Ғарипов`; `Мостай Кәрим` |
-| `Мәрйәм менән Илнур мәктәпкә китте.` | `Мәрйәм`; `Илнур` |
-| `Өфө ҡалаһында яңы мәктәп асылды.` | None |
-
-## Method
-
-```text
-BashkirRoBERTa encoder → person-entity fine-tuning → FP32 ONNX
-    → dynamic INT8 quantization → word-level BIO decoding
-```
-
-The encoder was fine-tuned with Bashkir person-entity annotations; its encoder
-layers and classification head changed. The original masked-language ONNX graph
-cannot be combined with this head to reproduce the release. Training data and
-evaluation text are not included. The tokenizer and first-subword alignment are
-provided so ONNX scores can be mapped back to complete words.
-
-### Benchmark
-
-On a held-out, human-verified person-entity set, exact person-span micro F1 is
-**0.8594** for FP32 and **0.8589** for INT8 when run one sentence at a time
-through the included adapter. FP32 makes no word-tag changes relative to the
-PyTorch checkpoint. The separately authored 100-sentence stress suite gives span F1
-**0.9294** (FP32) and **0.9231** (INT8), with 92 sentences entirely correct in
-each format. The stress suite is a targeted diagnostic, not an estimate of
-population accuracy. Dynamic INT8 outputs can vary slightly with batch
-composition; the figures above use the public single-sentence runtime. Counts
-and the BIO scoring convention are in
-[`benchmark_summary.json`](benchmark_summary.json).
-
-## Quality and Use
-
-Use this model to find mentions of people in Bashkir text, including inflected
-names and multiword names. The `runtime.py` adapter returns each span with
-character offsets. `predict_tokens` also accepts pre-tokenized words for
-evaluation workflows. The default INT8 graph is smaller while preserving nearly
-all of the observed gold-set score; FP32 is available when exact checkpoint
-parity matters.
-
-### Limitations
-
-- Only person entities are labeled. Places and organizations have no output class.
-- Rare names can be missed. Long names and adjacent people can be split or merged.
-- Names inside institution names can be mistaken for person mentions.
-- Inputs beyond the context limit are truncated; the runtime reports `truncated: true`.
-- Word boundaries from noisy OCR, unusual punctuation, and mixed writing systems
-  can differ from the training tokenization.
-- The authored stress suite is too small and structured to replace the held-out
-  human-verified benchmark.
-
-## Related Resources
-
-- [BashkirRoBERTa](https://huggingface.co/failed09/bashkir-roberta) is the
-  underlying masked-language model; use it for fill-mask work and new fine-tuning
-  tasks rather than person-name extraction.
-
-## Usage
-
-```bash
-pip install onnxruntime sentencepiece numpy huggingface_hub
-```
-
-```python
-import sys
-from huggingface_hub import snapshot_download
-
-model_dir = snapshot_download(
-    "failed09/bashkir-roberta-ner",
-    allow_patterns=["onnx/model_int8.onnx", "spm_bashkir_bert_16k.model",
-                    "config.json", "runtime.py"],
-)
-sys.path.insert(0, model_dir)
-from runtime import BashkirPersonNER
-
-ner = BashkirPersonNER(model_dir)
-print(ner.predict("Рәми Ғарипов һәм Мостай Кәрим тураһында һөйләштек."))
-```
-
-For the FP32 graph, download `onnx/model_fp32.onnx` as well and construct
-`BashkirPersonNER(model_dir, precision="fp32")`. The included `example.py`
-runs the default configuration.
-
-## License
-
-The license for this checkpoint has not yet been determined. The weights are
-available for download, but public availability alone does not grant reuse or
-redistribution rights. We are reviewing the provenance and rights of the
-[BashkirRoBERTa base model](https://huggingface.co/failed09/bashkir-roberta)
-and training sources before selecting a license. Training texts and benchmark
-sentences are not included. Contact the maintainer through the Hub for
-provenance, intended-use, or removal requests.
-
-## Citation
-
-```bibtex
-@software{failed09_bashkir_roberta_ner_2026,
-  title = {BashkirRoBERTa NER},
-  author = {failed09},
-  year = {2026},
-  publisher = {Hugging Face},
-  url = {https://huggingface.co/failed09/bashkir-roberta-ner},
-  note = {ONNX person named entity recognition 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 NER
+library_name: onnxruntime
+pipeline_tag: token-classification
+tags:
+- bashkir
+- named-entity-recognition
+- person
+- onnx
+- onnxruntime
+- low-resource
+---
+
+# BashkirRoBERTa NER
+
+> ONNX models for finding person names in Bashkir text, with a compact CPU default.
+
+## Overview
+
+BashkirRoBERTa NER is a person-name tagger fine-tuned from the BashkirRoBERTa
+masked-language encoder. It recognizes people (`PER`) and returns spans in the
+original text. The release includes FP32 and dynamically quantized INT8 ONNX
+graphs, the original SentencePiece tokenizer, and a small standalone runtime.
+The INT8 model is the default for CPU use.
+
+| At a glance | |
+| --- | --- |
+| Task | Bashkir person named entity recognition (`PER`) |
+| Default artifact | `onnx/model_int8.onnx` |
+| Source | A Bashkir-language encoder and person-entity training annotations |
+| Version / license | v1 / Apache-2.0 |
+
+## Contents
+
+### Files and Configurations
+
+| File | Purpose | Size |
+| --- | --- | ---: |
+| **`onnx/model_int8.onnx`** | CPU default, dynamic INT8 | 82.4 MB |
+| `onnx/model_fp32.onnx` | FP32 reference with PyTorch tag parity | 200.3 MB |
+| `spm_bashkir_bert_16k.model` | SentencePiece tokenizer | 0.59 MB |
+| `runtime.py` | Word alignment, BIO spans, character offsets | — |
+| `config.json` | Architecture, label order, runtime contract | — |
+| `benchmark_summary.json` | Aggregate evaluation without source text | — |
+| `example.py` | Download and run the default model | — |
+| `META.json` | Release passport and artifact hashes | — |
+| `LICENSE` | Full license text | — |
+| `SHA256SUMS` | Checksums of the public files | — |
+
+### Model Architecture
+
+| Property | Value |
+| --- | --- |
+| Encoder | BashkirRoBERTa, eight Pre-LayerNorm Transformer blocks |
+| Head | Linear word-level classifier |
+| Labels | `O`, `B-PER`, `I-PER` |
+| Tokenization | SentencePiece, first piece of each original word is labeled |
+| Input | `input_ids`, int64, dynamic batch and sequence dimensions |
+| Output | `logits`, float, `[batch, sequence, 3]` |
+| Maximum context | 256 SentencePiece tokens, including boundary tokens |
+
+### Examples
+
+Outputs below were produced by the included INT8 runtime:
+
+| Input | Found people |
+| --- | --- |
+| `Рәми Ғарипов һәм Мостай Кәрим тураһында һөйләштек.` | `Рәми Ғарипов`; `Мостай Кәрим` |
+| `Мәрйәм менән Илнур мәктәпкә китте.` | `Мәрйәм`; `Илнур` |
+| `Өфө ҡалаһында яңы мәктәп асылды.` | None |
+
+## Method
+
+```text
+BashkirRoBERTa encoder → person-entity fine-tuning → FP32 ONNX
+    → dynamic INT8 quantization → word-level BIO decoding
+```
+
+The encoder was fine-tuned with Bashkir person-entity annotations; its encoder
+layers and classification head changed. The original masked-language ONNX graph
+cannot be combined with this head to reproduce the release. Training data and
+evaluation text are not included. The tokenizer and first-subword alignment are
+provided so ONNX scores can be mapped back to complete words.
+
+### Benchmark
+
+On a held-out, human-verified person-entity set, exact person-span micro F1 is
+**0.8594** for FP32 and **0.8589** for INT8 when run one sentence at a time
+through the included adapter. FP32 makes no word-tag changes relative to the
+PyTorch checkpoint. The separately authored 100-sentence stress suite gives span F1
+**0.9294** (FP32) and **0.9231** (INT8), with 92 sentences entirely correct in
+each format. The stress suite is a targeted diagnostic, not an estimate of
+population accuracy. Dynamic INT8 outputs can vary slightly with batch
+composition; the figures above use the public single-sentence runtime. Counts
+and the BIO scoring convention are in
+[`benchmark_summary.json`](benchmark_summary.json).
+
+## Quality and Use
+
+Use this model to find mentions of people in Bashkir text, including inflected
+names and multiword names. The `runtime.py` adapter returns each span with
+character offsets. `predict_tokens` also accepts pre-tokenized words for
+evaluation workflows. The default INT8 graph is smaller while preserving nearly
+all of the observed gold-set score; FP32 is available when exact checkpoint
+parity matters.
+
+### Limitations
+
+- Only person entities are labeled. Places and organizations have no output class.
+- Rare names can be missed. Long names and adjacent people can be split or merged.
+- Names inside institution names can be mistaken for person mentions.
+- Inputs beyond the context limit are truncated; the runtime reports `truncated: true`.
+- Word boundaries from noisy OCR, unusual punctuation, and mixed writing systems
+  can differ from the training tokenization.
+- The authored stress suite is too small and structured to replace the held-out
+  human-verified benchmark.
+
+## Related Resources
+
+- [BashkirRoBERTa](https://huggingface.co/failed09/bashkir-roberta) is the
+  underlying masked-language model; use it for fill-mask work and new fine-tuning
+  tasks rather than person-name extraction.
+
+## Usage
+
+```bash
+pip install onnxruntime sentencepiece numpy huggingface_hub
+```
+
+```python
+import sys
+from huggingface_hub import snapshot_download
+
+model_dir = snapshot_download(
+    "failed09/bashkir-roberta-ner",
+    allow_patterns=["onnx/model_int8.onnx", "spm_bashkir_bert_16k.model",
+                    "config.json", "runtime.py"],
+)
+sys.path.insert(0, model_dir)
+from runtime import BashkirPersonNER
+
+ner = BashkirPersonNER(model_dir)
+print(ner.predict("Рәми Ғарипов һәм Мостай Кәрим тураһында һөйләштек."))
+```
+
+For the FP32 graph, download `onnx/model_fp32.onnx` as well and construct
+`BashkirPersonNER(model_dir, precision="fp32")`. The included `example.py`
+runs the default configuration.
+
+## License
+
+The model weights, tokenizer and runtime are distributed under the
+[Apache-2.0 license](LICENSE). The model is fine-tuned from
+[BashkirRoBERTa](https://huggingface.co/failed09/bashkir-roberta), which is also
+released under Apache-2.0. Training texts and benchmark sentences are not
+included; rights in the original texts remain with their respective owners.
+Contact the maintainer through the Hub for provenance or removal requests.
+
+## Citation
+
+```bibtex
+@software{failed09_bashkir_roberta_ner_2026,
+  title = {BashkirRoBERTa NER},
+  author = {failed09},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/failed09/bashkir-roberta-ner},
+  note = {ONNX person named entity recognition 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 @@
-228193ce4bcbb931f1c4b5cfd6b42e4f2a30bd7d6989ee3c50421008b87e46f3  META.json
-39726094d6dce7072f05604cd9723e6124a02cb6e3c87c36519224ffa83af1d4  README.md
+cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30  LICENSE
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 0ed527c20e4be58304e32521470243841f1e3fb1bd76df203e57c0f17e04628b  benchmark_summary.json
 4116a520ec1537633120ccd74aa167581b9a9fd2b50f5a47abfbe5171242341c  config.json
 bbe06259d2a8343523f6b8abc4c219bc776b18cb7df8161f86ab6c533136302c  example.py