Objectives:

1. Select appropriate pre-trained NLP models.
2. Adapt and fine-tune these models for email categorization and priority tagging.

Steps and Tasks:

1. Select Pre-trained Models:

Research and Selection:

• Objective: Choose the best pre-trained models for email categorization and priority tagging.
• Tasks:
  • Research the latest pre-trained NLP models in libraries such as Hugging Face Transformers.
  • Evaluate models like BERT (Bidirectional Encoder Representations from Transformers) and GPT-3 (Generative Pre-trained Transformer 3) for text classification tasks.
  • Consider the strengths and weaknesses of each model in relation to the specific requirements of email processing.
• Tools/Resources:
  • Hugging Face Model Hub
  • Research papers and documentation on BERT, GPT-3, and other relevant models

2. Set Up Environment:

Objective: Prepare the development environment for model adaptation and fine-tuning.

• Tasks:
  • Set up Google Colab or AWS SageMaker for training and fine-tuning models.
  • Ensure that necessary libraries and dependencies (such as Transformers, PyTorch, TensorFlow) are installed.
• Tools/Resources:
  • Google Colab
  • AWS SageMaker
  • Python packages: transformers, torch, tensorflow, etc.

3. Train/Adapt Models:

Dataset Preparation:

• Objective: Prepare the dataset for fine-tuning the selected pre-trained models.
• Tasks:
  • Collect and preprocess datasets related to email content and categorization. This might include public email datasets or proprietary data.
  • Ensure that the data is labeled correctly for the tasks of categorization and priority tagging.
• Tools/Resources:
  • Public datasets (e.g., Enron email dataset)
  • Custom email datasets

Fine-tuning:

• Objective: Fine-tune the selected pre-trained models using the prepared datasets.
• Tasks:
  • Load the pre-trained models using Hugging Face Transformers.
  • Set up training scripts to fine-tune the models on the email datasets.
  • Monitor the training process, adjusting hyperparameters as needed to improve performance.
  • Save the fine-tuned models for deployment.
• Tools/Resources:
  • Hugging Face Transformers
  • Python scripts for training and evaluation

4. Model Evaluation:

Objective: Evaluate the performance of the fine-tuned models.

• Tasks:
  • Use metrics like accuracy, precision, recall, and F1-score to assess the performance of the models.
  • Compare the results with baseline models to ensure improvements.
  • Perform cross-validation if necessary to validate the model's performance.
• Tools/Resources:
  • Evaluation scripts
  • Libraries for metrics calculation (e.g., sklearn)

5. Documentation and Reporting:

Objective: Document the process and results of model selection, adaptation, and evaluation.

• Tasks:
  • Write detailed documentation on the model selection process, including reasons for choosing specific models.
  • Document the training process, including hyperparameters, datasets used, and any challenges faced.
  • Prepare a report on the evaluation results, highlighting key metrics and any insights gained.
• Tools/Resources:
  • Documentation tools (e.g., Markdown, Jupyter Notebooks)
  • Reporting templates

Deliverables:

• A fine-tuned model for email categorization and priority tagging.
• Detailed documentation of the model selection, training, and evaluation process.
• Evaluation report with performance metrics and insights.

Tech Stack Setup:

• Frontend:

  • Set up React with libraries like Apollo Client for interacting with GraphQL.

• Backend:

  • Set up Node.js with Express and integrate GraphQL.

Backend Setup:

1. Express Setup:

   • Initialize a new Node.js project with npm init.
   • Install Express with npm install express.
   • Set up basic Express server (server.js or index.js).

2. GraphQL Setup:

   • Install GraphQL and Apollo Server with npm install graphql apollo-server-express.
   • Create a GraphQL schema defining types, queries, and mutations (e.g., email queries, user mutations).
   • Implement resolvers to handle data fetching and manipulation.

Email Integration:

1. Email APIs Integration:
   • Gmail API:
     • Set up OAuth 2.0 for authentication.
     • Use Google’s API client library to fetch and manage emails.