meetfranz/franz-models

Franz Email Classifier

Multi-label email classification model used by Franz to automatically prioritize emails.

Fine-tuned from microsoft/Multilingual-MiniLM-L12-H384 and exported as ONNX INT8 for fast CPU inference in Electron.

Labels

The model predicts 8 binary labels per email:

Label	Meaning
IS_URGENT	Needs attention today
NEEDS_REPLY	Direct question or action request to the user
HAS_DEADLINE	Explicit or relative deadline mentioned
IS_ACTIONABLE	Any action required (broader than NEEDS_REPLY)
IS_INFORMATIONAL	FYI / status update, no action needed
IS_AUTOMATED	Machine-generated (CI/CD, monitoring, alerts)
IS_NEWSLETTER	Content marketing / newsletter
IS_TRANSACTIONAL	Receipt, invoice, order confirmation

Priority Mapping

Labels are combined into priority tiers in the Franz app:

Condition	Priority
IS_URGENT + NEEDS_REPLY	urgent
IS_URGENT	important
NEEDS_REPLY + IS_ACTIONABLE	important
IS_NEWSLETTER or IS_TRANSACTIONAL	noise
IS_AUTOMATED (not urgent)	noise
IS_INFORMATIONAL (not urgent/reply)	low
Everything else	normal

Usage

With @huggingface/transformers (Node.js / Electron)

import { pipeline, env } from '@huggingface/transformers'

env.allowLocalModels = true
env.localModelPath = '/path/to/models' // parent dir

const classifier = await pipeline(
  'text-classification',
  'email-classifier', // subdirectory name
  { dtype: 'int8', device: 'cpu', multi_label: true }
)

const result = await classifier('Re: Urgent: Invoice #4521 due Friday')
// [
//   { label: 'IS_URGENT', score: 0.94 },
//   { label: 'NEEDS_REPLY', score: 0.12 },
//   { label: 'HAS_DEADLINE', score: 0.91 },
//   ...
// ]

With transformers (Python)

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="meetfranz/franz-models",
    top_k=None
)

result = classifier("Re: Urgent: Invoice #4521 due Friday")

Model Details

Property	Value
Architecture	BertForSequenceClassification
Base model	microsoft/Multilingual-MiniLM-L12-H384
Hidden size	384
Layers	12
Attention heads	12
Max sequence length	512
Vocab size	250,037
Tokenizer	XLMRobertaTokenizer (SentencePiece BPE)
Problem type	Multi-label classification
Quantization	ONNX INT8
Model size	~113 MB (quantized)

Training

Trained on LLM-generated synthetic email data. No real user emails or personal data were used in training. Labels were bootstrapped via LLM annotation and human-reviewed for quality.

Fine-tuned with multi-label BCE loss, then exported to ONNX with INT8 dynamic quantization.

How Franz Uses This Model

This model is Stage 2 in Franz's three-stage email classification funnel:

1. Stage 1 — Heuristics: Fast rules-based classification for obvious cases
2. Stage 2 — ML (this model): ONNX inference for ambiguous emails (confidence threshold: 0.75)
3. Stage 3 — LLM: Local or cloud LLM for emails below the ML confidence threshold

The model is downloaded on demand when a user first adds an email account to Franz. If unavailable, the app gracefully falls through to Stage 3.

License

MIT