OmarioVIC/customer-email-classifier
OmarioVIC/customer-email-classifier is a 1 billion parameter Gemma 3 1B IT model fine-tuned for classifying customer email responses into five specific categories. Developed by OmarioVIC, this model generates structured JSON output and is optimized for low-latency deployment using vLLM. It excels at quickly categorizing short email replies for business outreach, providing a deterministic classification.
Loading preview...
OmarioVIC/customer-email-classifier: Email Response Classification
This model is a fine-tuned Gemma 3 1B IT (google/gemma-3-1b-it) specifically designed for classifying customer email responses. It processes email text and outputs a structured JSON object containing one of five predefined categories, making it ideal for automating email triage and response workflows.
Key Capabilities & Features
- Generative Classification: Accurately categorizes email replies into
automated_reply,interested,not_interested,out_of_office, orunrelated. - Structured Output: Always returns a JSON object in the format
{"classification": "<label>"}. - Optimized for Production: Fine-tuned with QLoRA (4-bit) via Unsloth for efficient training and designed for low-latency inference with vLLM.
- Deterministic Output: Configured for greedy decoding (
do_sample=False,temperature=0) to ensure consistent classification results.
Use Cases & Limitations
This model is best suited for:
- Automating the classification of short customer email replies, particularly in business outreach scenarios.
- Integrating into systems requiring fast, programmatic categorization of email intent.
Limitations:
- Primarily designed for short email replies (max 320 tokens including prompt).
- Trained on a specific business outreach dataset; performance may vary on different email domains.
Training Details
The model was trained on approximately 3,500 samples using Unsloth's SFTTrainer with completion-only masking, focusing loss computation solely on the assistant's response. This approach ensures the model learns to generate the correct JSON output efficiently.