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+---
+language:
+- bo
+library_name: transformers
+tags:
+- image-classification
+- dinov3
+- tibetan
+- manuscript
+- binary-classification
+- vision
+datasets:
+- OpenPecha/BDRC-Script-Data
+metrics:
+- accuracy
+- f1
+- auc_roc
+base_model: facebook/dinov3-vits16-pretrain-lvd1689m
+---
+
+# Uchen-Ume Binary Script Classifier
+
+This model is a fine-tuned version of **Meta's DINOv3-ViT-S/16** for binary classification of Tibetan scripts (Uchen vs. Ume). It serves as the "Router" stage for a hierarchical classification pipeline.
+
+## Model Details
+
+### Model Description
+
+The model was developed to provide a high-reliability baseline for separating formal block scripts (**Uchen**) from cursive script families (**Ume**). By focusing on global page geometry rather than local character patches, it achieves high accuracy on whole-page manuscript scans.
+
+- **Developed by:** OpenPecha / [Your Name]
+- **Model type:** Vision Transformer (ViT)
+- **Language(s):** Tibetan (Classical/Manuscript)
+- **Finetuned from model:** facebook/dinov3-vits16-pretrain-lvd1689m
+
+## Uses
+
+### Direct Use
+
+This model is intended to be used as a **pre-processing filter** or **router**. It can automatically sort large digital archives into Uchen or Ume folders to be processed by specialized downstream OCR engines.
+
+### Out-of-Scope Use
+
+- Classification of modern printed Tibetan fonts (untested).
+- Recognition of non-Tibetan scripts (Sanskrit, Lantsa, etc.).
+- Character-level recognition (OCR).
+
+## Bias, Risks, and Limitations
+
+The model was trained primarily on BDRC (Buddhist Digital Resource Center) manuscript scans. It may struggle with:
+- Extremely faint or damaged woodblock prints.
+- Pages containing a roughly equal mix of both Uchen and Ume (Multi-script).
+
+## How to Get Started with the Model
+
+```python
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+import torch
+from PIL import Image
+
+processor = AutoImageProcessor.from_pretrained("your-username/uchen-ume-classifier")
+model = AutoModelForImageClassification.from_pretrained("your-username/uchen-ume-classifier")
+
+image = Image.open("manuscript_page.jpg").convert("RGB")
+inputs = processor(images=image, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    prediction = outputs.logits.argmax(-1).item()
+
+print(f"Detected Script: {model.config.id2label[prediction]}")