---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
license: mit
datasets:
- imnim/multiclass-email-classification
language:
- en
tags:
- Email-classifier
- Email-labelling
- Fine-tuning
- peft
- lora
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
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

<!-- Provide a longer summary of what this model is. -->
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

<!-- Provide the basic links for the model. -->

- **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