Update README.md

README.md

@@ -1,6 +1,7 @@
 ---
 language:
 - bo
+license: apache-2.0
 library_name: transformers
 tags:
 - image-classification
@@ -10,7 +11,7 @@ tags:
 - binary-classification
 - vision
 datasets:
-- openpecha/tibetan-script-images
+- openpecha/uchen-ume-classification-benchmark
 metrics:
 - accuracy
 - f1
@@ -18,31 +19,57 @@ metrics:
 base_model: facebook/dinov3-vits16-pretrain-lvd1689m
 ---
 
-# Uchen-Ume Binary Script Classifier
+# Tibetan Script Router (DINOv3-ViT-S)
 
-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.
+This model is a fine-tuned version of **Meta's DINOv3-ViT-S/16** specifically designed for high-precision binary classification of Tibetan scripts. It acts as the primary "Router" in a hierarchical classification pipeline, distinguishing between formal block scripts (**Uchen**) and cursive families (**Ume**).
 
-## 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.
+##  Model Details
 
 - **Project Name:** The BDRC Etext Corpus
 - **Developed by:** Dharmaduta
 - **Specifications provided by:** [Buddhist Digital Resource Center (BDRC)](https://www.bdrc.io)
 - **Funded by:** Khyentse Foundation
 - **Model type:** Vision Transformer (ViT)
-- **Language(s):** Tibetan (Classical/Manuscript)
-- **Finetuned from model:** facebook/dinov3-vits16-pretrain-lvd1689m
-
-## Performance Summary
-The model achieved near-perfect discrimination during testing, specifically excelling in identifying formal Uchen with high recall.
+- **License:** Apache 2.0
+- **Fine-tuned from:** `facebook/dinov3-vits16-pretrain-lvd1689m`
+
+## 📊 Dataset & Class Distribution
+
+The model was trained using the [openpecha/uchen-ume-classification](https://huggingface.co/datasets/openpecha/uchen-ume-classification) dataset. This training set consists of **4,572 images** balanced across two major categories. 
+
+The binary classes were mapped from the following granular script types:
+
+### 1. Uchen (Class 0) — 2,286 Total Samples
+| Granular Script Type | Sample Count |
+| :--- | :--- |
+| `uchen_sugdring` | 1,670 |
+| `uchen_sugthung` | 616 |
+
+### 2. Ume (Class 1) — 2,286 Total Samples
+| Granular Script Type | Sample Count |
+| :--- | :--- |
+| `petsuk` | 1,388 |
+| `tsegdrig` | 749 |
+| `peri` | 614 |
+| `druthung` | 207 |
+| `tsumachug` | 178 |
+| `yigchung` | 166 |
+| `drudring` | 132 |
+| `drathung` | 129 |
+| `druring` | 119 |
+| `khyuyig` | 113 |
+| `dhumri` | 98 |
+| `tsugchung` | 77 |
+| `trinyig` | 42 |
+
+*Note: Classes labeled "Difficult," "Multi-script," and "Non-Tibetan" were excluded to maintain a clean training signal for the Uchen/Ume boundary.*
+
+## 📈 Performance Summary
+The model achieved its peak performance at **Stage B** (Partial backbone unfreezing of the last 2 blocks).
 
 - **Test Accuracy:** 98.95%
 - **Macro F1-Score:** 0.984
 - **AUC-ROC:** 0.9988
-- **Best Training Configuration:** Stage B (Partial Backbone Unfreezing)
 
 ### Confusion Matrix
 | Predicted \ Actual | Uchen | Ume |
@@ -50,33 +77,17 @@ The model achieved near-perfect discrimination during testing, specifically exce
 | **Uchen** | 159   | 2   |
 | **Ume** | 6     | 595 |
 
-## Uses
-
-### Direct Use
-
-This model is intended to be used as a **pre-processing filter** or **router** within the **BDRC Etext Corpus** pipeline. It can automatically sort large digital archives into Uchen or Ume categories 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 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
+## 🚀 How to Get Started
 
 ```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")
+# Note: Gated access approval for DINOv3 is required
+model_id = "openpecha/uchen-ume-classifier"
+processor = AutoImageProcessor.from_pretrained(model_id)
+model = AutoModelForImageClassification.from_pretrained(model_id)
 
 image = Image.open("manuscript_page.jpg").convert("RGB")
 inputs = processor(images=image, return_tensors="pt")