Align BashkirRoBERTa model card with release template

README.md

@@ -17,21 +17,20 @@ tags:
 
 # BashkirRoBERTa
 
-> A masked language model for Bashkir, for fill-mask, spellchecking and
-> foundation fine-tuning.
+> A Bashkir masked language model for fill-mask suggestions and further 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.
+BashkirRoBERTa predicts masked SentencePiece tokens from context. It can provide
+fill-mask suggestions and serve as an encoder for further fine-tuning, including
+the related person-name model. The release includes Transformers weights and FP16
+and INT8 ONNX graphs. Its custom Pre-LayerNorm architecture requires the included
+model code when loaded with Transformers.
 
 | At a glance | |
 | --- | --- |
 | Task | Masked language modelling / fill-mask |
-| Default artifact | `model.safetensors` (Transformers) or `onnx/model_int8.onnx` (ONNX) |
+| Default artifact | `model.safetensors` (Transformers); `onnx/model_int8.onnx` (CPU ONNX) |
 | Source | A monolingual Bashkir-language dataset |
 | Version / license | v1 / Apache-2.0 |
 
@@ -41,9 +40,9 @@ stock post-LayerNorm RoBERTa implementation.
 
 | File | Purpose | Size |
 | --- | --- | ---: |
-| `model.safetensors` | PyTorch weights for Transformers | 200.2 MB |
+| **`model.safetensors`** | Transformers weights and fine-tuning starting point | 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 |
+| **`onnx/model_int8.onnx`** | Compact INT8 ONNX graph for CPU use | 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 | — |
@@ -82,11 +81,16 @@ Outputs from the INT8 ONNX model on CPU:
 
 ## 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
+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.
+
+```text
+Bashkir text → SentencePiece tokens → masked-language pretraining
+    → Transformers checkpoint → FP16 and INT8 ONNX exports
+```
+
+### Benchmark
 
 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
@@ -95,19 +99,24 @@ 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.
+Use the Transformers checkpoint for fill-mask experiments or fine-tuning and the
+INT8 ONNX graph for compact CPU inference. Predictions are token suggestions that
+need review in context, especially when the mask represents only part of a word.
 
 ### 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
+- No training texts are redistributed; provenance or removal requests go through the maintainer.
+
+## Related Resources
+
+- [BashkirRoBERTa NER](https://huggingface.co/failed09/bashkir-roberta-ner)
+  fine-tunes this encoder to find person names and return their text spans. Use it
+  when the task is person-name extraction rather than masked-token prediction.
+
+## Usage
 
 ```bash
 pip install transformers torch huggingface_hub

SHA256SUMS

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