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:

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

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

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