OmarioVICC/email-classifier
TEXT GENERATIONConcurrency Cost:1Model Size:1BQuant:BF16Ctx Length:32kPublished:Apr 6, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
OmarioVICC/email-classifier is a 1 billion parameter Gemma 3-1B-IT-based causal language model developed by OmarioVICC. This model is specifically fine-tuned for email classification tasks, leveraging Unsloth for accelerated training. It offers efficient performance for categorizing email content, making it suitable for applications requiring quick and accurate email organization.
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OmarioVICC/email-classifier: A Specialized Email Classification Model
This model, developed by OmarioVICC, is a 1 billion parameter language model fine-tuned from the unsloth/gemma-3-1b-it-unsloth-bnb-4bit base. It is specifically designed and optimized for email classification tasks.
Key Capabilities
- Efficient Email Categorization: Specialized in understanding and classifying email content.
- Accelerated Training: Utilizes Unsloth and Huggingface's TRL library, enabling 2x faster training compared to standard methods.
- Gemma 3-1B-IT Base: Built upon a robust instruction-tuned Gemma architecture, providing a strong foundation for language understanding.
Good For
- Automated Email Sorting: Ideal for systems that need to automatically sort incoming emails into predefined categories.
- Spam Detection: Can be adapted for identifying and filtering unwanted emails.
- Customer Support Triage: Useful for routing customer inquiries to the appropriate department based on email content.
This model is released under the Apache-2.0 license, offering flexibility for various applications.