How do I build a professional NLP project step by step for my portfolio?

All answers to this question.

To stand out in 2024, avoid basic sentiment analysis on movie reviews. Instead, try a "Niche News Summarizer" or a "Legal Contract Risk Analyzer." The workflow is: 1. Scrape data using BeautifulSoup or use a Kaggle dataset. 2. Clean text (remove noise, normalize). 3. Perform EDA to find trends. 4. Use a Hugging Face model for the actual NLP task. 5. Build a UI using Streamlit—it's much faster than Flask for data apps. Finally, deploy it on Hugging Face Spaces or Vercel. Showing you can deploy a model is what really impresses hiring managers.

   Answered 2024-02-04 by Gloria Mendez

Gloria, do you think including a "RAG" (Retrieval-Augmented Generation) component is necessary now for an NLP portfolio to be considered "modern"?

   Answered 2024-02-06 by Curtis Lane

• Curtis, absolutely. RAG is the hottest topic in NLP right now. If you can show that your project doesn't just "chat," but actually retrieves information from a specific PDF or database to answer questions, you'll be ahead of 90% of other applicants. It shows you understand vector databases like Pinecone or Milvus, which are high-demand skills in the current job market.

     Commented 2024-02-08 by Gloria Mendez

Documentation is just as important as the code. Make sure your GitHub README explains the "Why" behind your choice of model and tokenization.

   Answered 2024-02-10 by Ralph Burgess

• Ralph is right. I’ve had interviews where they only asked about my README. It proves you can communicate technical decisions to non-tech stakeholders.

     Commented 2024-02-12 by Austin Powers