NLP Capstone Project in AI & Artificial Intelligence

Welcome to the NLP Capstone. Over the previous modules, you've learned the deep theory behind language models—from raw tokens to the mathematical beauty of the Transformer architecture.

Now, it's time to act like a Senior Engineer. We are going to build two professional-grade NLP applications: an instant Sentiment Analyzer for processing business feedback, and an interactive, stateful Chatbot.

For our first app, we need to rapidly classify incoming user feedback. Instead of manually loading models and tokenizers, we will use the Hugging Face pipeline abstraction.

The pipeline handles everything under the hood. You pass it raw text, and it instantly runs the tokenizer, passes the tensors through a pre-trained model (like DistilBERT), and returns human-readable labels like 'POSITIVE' along with a strict confidence score.

Building a Chatbot is fundamentally different from a simple classifier. We will use a Causal Language Model designed for conversation, such as Microsoft's DialoGPT.

The massive challenge here is state management. Transformer APIs are inherently stateless—they don't remember the last thing you said. To make a chatbot work, you must manually capture the user's input, encode it, and append it to a constantly growing 'history tensor' representing the entire conversation.

Once we have concatenated the user's new message onto our history tensor, we feed that massive tensor into the model's .generate() method.

This is where the magic happens. The model looks at the entire history, calculates the probabilities for the next word, and begins generating a response token by token until it reaches the End-Of-Sequence (eos) token. We then decode that response and display it to the user.

You've done it. You successfully deployed a production-ready sentiment classifier and engineered the complex tensor management required for a stateful conversational AI.

You now possess the core skills to manipulate state-of-the-art language models in Python. The NLP track is complete, leaving you prepared to tackle the final frontier of AI development: Ethics, Bias, and Safety in production environments.

Welcome to the NLP Capstone! You are going to build a professional-grade Sentiment Analyzer and an interactive Chatbot using the skills you've learned.

Phase 1: Sentiment Analysis. We use the Hugging Face pipeline to instantly deploy a pre-trained model for classifying user feedback.

Phase 2: The Chatbot. We use a Causal LM like DialoGPT. We must manage the 'Chat History' manually because the API is stateless.

Checkpoint: In our Chatbot implementation, why do we need to store and update the 'chat_history_ids'?

• →To make the model faster
• →To provide the model with conversational context

To generate a response, we encode the user input, append it to the history, and use the .generate() method with specific parameters like max_length.

Capstone complete! You've successfully built and deployed two core NLP applications. You are now a certified NLP practitioner.

Checkpoint: Which method is used in the Transformers library to produce the actual text response from a Causal Language Model?

• →.predict()
• →.generate()

Congratulations! You've reached the end of the NLP track. One final module remains: AI Ethics and Safety.

Classify Real Review Sentiment. Finish classifying a review as positive or negative using keyword matching.