NLP Email Classifier Development
Budget: $250 – $750 USD
I’m looking for an AI engineer who can take ownership of a Natural Language Processing project focused on classifying incoming emails. The end-goal is a robust model that can automatically tag or route messages based on their content so that our internal workflows become faster and more consistent.
Here’s what I need:
• Data pipeline – guidance on collecting, cleaning, and labeling a representative email dataset while keeping privacy top-of-mind.
• Model experimentation – work with modern text-classification approaches (traditional ML baselines through Transformer architectures like BERT or RoBERTa) and justify the final choice with clear metrics.
• Training & evaluation – deliver precision, recall, and F1 results on a held-out test set, plus error analysis that highlights improvement opportunities.
• Lightweight deployment – package the best model behind a simple REST or gRPC API so our dev team can call it from existing tools.
• Documentation – concise setup instructions, model assumptions, and a cheatsheet for retraining when new labeled emails arrive.
I’m open to your preferred frameworks (PyTorch, TensorFlow, Hugging Face, scikit-learn, etc.) as long as the solution is reproducible in a standard Python environment.
If you’ve built email or text classifiers before, especially in production settings, I’d love to see examples. Let’s discuss timelines and any questions you have about the data or project constraints.
Here’s what I need:
• Data pipeline – guidance on collecting, cleaning, and labeling a representative email dataset while keeping privacy top-of-mind.
• Model experimentation – work with modern text-classification approaches (traditional ML baselines through Transformer architectures like BERT or RoBERTa) and justify the final choice with clear metrics.
• Training & evaluation – deliver precision, recall, and F1 results on a held-out test set, plus error analysis that highlights improvement opportunities.
• Lightweight deployment – package the best model behind a simple REST or gRPC API so our dev team can call it from existing tools.
• Documentation – concise setup instructions, model assumptions, and a cheatsheet for retraining when new labeled emails arrive.
I’m open to your preferred frameworks (PyTorch, TensorFlow, Hugging Face, scikit-learn, etc.) as long as the solution is reproducible in a standard Python environment.
If you’ve built email or text classifiers before, especially in production settings, I’d love to see examples. Let’s discuss timelines and any questions you have about the data or project constraints.