metadata
base_model:
- google/flan-t5-small
license: apache-2.0
language:
- en
- fr
tags:
- classification
- emails
- 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)
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)
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