This model is a fine-tuned version of
microsoft/Phi-3-mini-4k-instruct for email classification tasks. It uses LoRA (Low-Rank Adaptation) for efficient fine-tuning on Apple Silicon using the MLX framework.
This model classifies emails into predefined categories to help with inbox organization, email filtering, and workflow automation.
1from mlx_lm import load, generate
2
3# Load the model
4model, tokenizer = load("jake-watkins/email-classifier")
5
6# Classify an email
7email_content = """
8Your subscription to Premium Service will renew on January 1st, 2026.
9To cancel or modify your subscription, visit your account settings.
10"""
11
12prompt = f"Classify this email:\n\n{email_content}\n\nCategory:"
13
14response = generate(model, tokenizer, prompt=prompt, max_tokens=50, verbose=False)
15print(response)
The model was trained on a private dataset of email examples across 20 categories:
Fine-tuned using MLX-LM on Apple Silicon with LoRA adapters for parameter-efficient training.
The model was validated on a held-out test set with stratified sampling to maintain category distribution across training, validation, and test splits (80/10/10).
This model is intended for email organization and automation purposes. Users should:
1@article{abdin2024phi,
2 title={Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone},
3 author={Abdin, Marah and others},
4 journal={arXiv preprint arXiv:2404.14219},
5 year={2024}
6}
For questions or feedback about this model, please open an issue on the model repository.
