title	Email Classifier
emoji	📚
colorFrom	gray
colorTo	purple
sdk	docker
pinned	false
short_description	Classifier for email using BERT

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Email Classifier

This project is an email classification application built using a BERT-based model. It classifies emails into predefined categories, enabling efficient email management and automation.

---

Approach

The email classifier leverages a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model fine-tuned for text classification tasks. BERT is a state-of-the-art natural language processing model that understands the context of words in a sentence, making it highly effective for tasks like email classification.

Key Features of the Approach:

1. Pre-trained BERT Model: Utilizes a pre-trained BERT model from Hugging Face for transfer learning.
2. Fine-tuning: The model is fine-tuned on a labeled dataset of emails to adapt it to the specific classification task.
3. Tokenization: Emails are tokenized using the BERT tokenizer to convert text into input IDs and attention masks.
4. Multi-class Classification: The model predicts the probability of each email belonging to one of the predefined categories.

---

Methodology

1. Data Preprocessing:

   • Emails are cleaned to remove unnecessary characters, HTML tags, and stopwords.
   • Tokenization is performed using the BERT tokenizer to prepare the input for the model.

2. Model Training:

   • A pre-trained BERT model is fine-tuned on the labeled dataset.
   • The training process optimizes the model's weights using a cross-entropy loss function.

3. Evaluation:

   • The model is evaluated on a validation dataset using metrics like accuracy, precision, recall, and F1-score.
   • Hyperparameters such as learning rate and batch size are tuned for optimal performance.

4. Deployment:

   • The trained model is integrated into a FastAPI application for serving predictions.
   • Docker is used to containerize the application for easy deployment.

---

Implementation Details

Project Structure

• app.py: The main FastAPI application that serves the email classification API.
• bert-email-classifier/: Contains the BERT model configuration, tokenizer, and vocabulary files.
• Dockerfile: Configuration for building the Docker image.
• requirements.txt: List of Python dependencies required for the project.
• data/: Directory for storing training and validation datasets (not included in the repository).

Running the Application

Prerequisites

• Docker installed on your system
• Python 3.9 or later (if running locally without Docker)
• pip for managing Python dependencies

Using Docker

1. Build the Docker image:

   docker build -t email-classifier .