Update README.md

README.md

@@ -11,4 +11,104 @@ tags:
 - text2text-generation
 - onnx
 - mobile
----
+---
+
+# Gmail Email Classifier (FLAN-T5 ONNX)
+
+A fine-tuned FLAN-T5-small model for email classification, optimized for on-device inference in mobile apps using ONNX Runtime.
+
+## Model Description
+
+This model classifies emails into 5 categories and determines if action is required:
+
+| Category | Description |
+|----------|-------------|
+| **PERSONAL** | 1:1 human communication, social messages |
+| **NEWSLETTER** | Marketing, promotions, subscribed content |
+| **TRANSACTION** | Orders, receipts, payments, confirmations |
+| **ALERT** | Security notices, important notifications |
+| **SOCIAL** | Social network notifications, community updates |
+
+### Output Format
+
+```
+CATEGORY | ACTION/NO_ACTION | Brief summary
+```
+
+**Example:**
+
+```
+Input: "Subject: Your order has shipped\n\nBody: Your order #12345 is on its way..."
+Output: "TRANSACTION | NO_ACTION | Order shipment confirmation for #12345"
+```
+
+## Intended Use
+
+- **Primary:** On-device email triage in mobile apps (iOS/Android)
+- **Runtime:** ONNX Runtime React Native
+- **Use case:** Prioritizing inbox, filtering noise, surfacing actionable emails
+
+## Model Details
+
+| Attribute | Value |
+|-----------|-------|
+| Base Model | `google/flan-t5-small` |
+| Parameters | ~80M |
+| Architecture | T5 Encoder-Decoder |
+| ONNX Size | 357 MB (encoder: 141 MB, decoder: 232 MB) |
+| Latency | ~79ms (iPhone, CPU) |
+| Max Sequence | 512 tokens |
+
+## Training Data
+
+- **Size:** 2,043 training / 256 validation / 255 test examples
+- **Source:** Personal Gmail inboxes (anonymized)
+- **Languages:** English, French
+- **Labeling:** Human-annotated with category + action flag
+
+## How to Use
+
+### ONNX Runtime (React Native)
+
+```typescript
+import { InferenceSession } from 'onnxruntime-react-native';
+
+const encoder = await InferenceSession.create('encoder_model.onnx');
+const decoder = await InferenceSession.create('decoder_model.onnx');
+
+// Tokenize input, run encoder, greedy decode
+```
+
+### Python (Transformers)
+
+```python
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+model = T5ForConditionalGeneration.from_pretrained("ippoboi/gmail-classifier")
+tokenizer = T5Tokenizer.from_pretrained("ippoboi/gmail-classifier")
+
+input_text = "Classify this email: Subject: Meeting tomorrow\n\nBody: Can we reschedule?"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+# Output: "PERSONAL | ACTION | Request to reschedule meeting"
+```
+
+## Files
+
+| File | Size | Description |
+|------|------|-------------|
+| `encoder_model.onnx` | 141 MB | ONNX encoder |
+| `decoder_model.onnx` | 232 MB | ONNX decoder |
+| `tokenizer.json` | 2.4 MB | SentencePiece tokenizer |
+| `config.json` | 2 KB | Model configuration |
+
+## Limitations
+
+- Trained primarily on English/French emails
+- May not generalize well to enterprise/corporate email patterns
+- Classification accuracy depends on email content quality (plain text preferred over HTML-heavy)
+
+## License
+
+Apache 2.0