Update README.md

README.md

@@ -1,3 +1,46 @@
 ---
+language:
+- en
+- si
+- ta
 pipeline_tag: text-classification
+tags:
+- banking
+- intent-classification
+- labse
+- multilingual
+- code-mixed
 ---
+
+# Swift-Support LaBSE Intent Classifier (v1.0)
+
+This is a fine-tuned **Language-Agnostic BERT Sentence Embedding (LaBSE)** model designed for trilingual intent classification in the banking and financial support domain. It was developed as part of the **Swift** Support Ticket Classification project.
+
+## Model Details
+* **Base Architecture:** `sentence-transformers/LaBSE`
+* **Task:** Text Classification (Intent Recognition)
+* **Number of Classes:** 77 (Derived from the BANKING77 taxonomy)
+* **Supported Languages:** English, Sinhala, Tamil, Singlish (Code-mixed), and Tanglish (Code-mixed).
+
+## Use Case
+This model is intended to be used as the **Digital Text Router** in a hybrid multimodal pipeline. When a user submits a typed text ticket (without an image/screenshot), this model deeply analyzes the semantic context of the text, regardless of the language, to instantly categorize the ticket for the correct banking department.
+
+## Performance & Ablation Findings
+During our extensive ablation studies, this Transformer model was evaluated against traditional Machine Learning algorithms (like Linear SVMs). 
+
+* **Digital/Clean Text:** This LaBSE model achieves state-of-the-art semantic understanding on clean, typed digital text.
+* **OCR Noisy Text:** When exposed to high Character Error Rates (CER) from Tesseract OCR (e.g., extracting text from blurry mobile screenshots), this model's F1-score drops to ~49.70% because Transformers are highly sensitive to sub-word tokenization destruction. 
+* **Architecture Decision:** Because of this sensitivity, the Swift backend dynamically routes **OCR-extracted text** to a robust Linear SVM, while routing **clean digital text** to this LaBSE model.
+
+## How to use in Python
+
+You can easily use this model via the `transformers` pipeline:
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="Swift-Support/labse-intent-1.0")
+
+result = classifier("I lost my credit card yesterday, please help me cancel it")
+print(result)
+# Output: [{'label': 'Card payment declined', 'score': 0.98}]