kugu/email_classification
The kugu/email_classification model is a 4 billion parameter instruction-tuned causal language model, finetuned by kugu from unsloth/Qwen3-4B-Instruct-2507. Optimized for email classification tasks, this model was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. It features a 32768 token context length, making it suitable for processing longer email content.
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Overview
The kugu/email_classification model is a 4 billion parameter language model specifically fine-tuned for email classification. Developed by kugu, it leverages the Qwen3 architecture and was fine-tuned from unsloth/Qwen3-4B-Instruct-2507.
Key Capabilities
- Specialized Email Classification: This model is optimized for accurately categorizing and understanding the intent of email content.
- Efficient Fine-tuning: The model was fine-tuned using Unsloth and Huggingface's TRL library, indicating an efficient training process.
- Extended Context Window: With a context length of 32768 tokens, it can process and classify longer and more complex email messages effectively.
Good For
- Automated Email Triage: Ideal for systems requiring automatic sorting and routing of incoming emails.
- Customer Support Automation: Can be used to classify customer inquiries, directing them to the appropriate department or generating relevant responses.
- Spam/Phishing Detection: While not explicitly stated, its classification capabilities could be adapted for identifying unwanted or malicious emails.
This model differentiates itself by its specific optimization for email classification, offering a targeted solution for developers working with email-centric applications.