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Model is finetuned for the task of email labelling. It labels the given email into one or more than one categories based on email subject and email body.
Model Details
Model Description
The model classifies emails into the following 10 categories: "Business", "Personal", "Promotions", "Customer Support", "Job Application", "Finance & Bills", "Events & Invitations", "Travel & Bookings", "Reminders", "Newsletters"
I have prepared a synthetic but realistic dataset of 2,105 labeled emails. Each email includes a subject, body, and one or more categories.
- Developed by: imnim
- Model type: text-to-text
- Language(s) (NLP): English
- Finetuned from model: Llama-3.1-8B-Instruct
Model Sources
- Repository: https://github.com/contributerMe/multi-label-email-classifier
- Demo: https://huggingface.co/spaces/imnim/Multi-labelEmailClassifier
Technical Specifications
Model Architecture and Objective
Auto-regressive language model that uses an optimized transformer architecture.
Compute Infrastructure
Kaggle Notebook
Hardware
Trained on Kaggle's P100 GPU
Framework versions
- PEFT 0.15.2
- Downloads last month
- 56
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Model tree for imnim/multi-label-email-classifier
Base model
meta-llama/Llama-3.1-8B
Finetuned
meta-llama/Llama-3.1-8B-Instruct