Put your knowledge into practice. In this final project, you will combine everything from tokenization to transformers to build working AI apps.
1The Final Build
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.
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NLP Capstone Project
Phase 1: Sentiment Analysis Pipeline
Phase 2: Stateful Conversational AI
"""2Phase 1: Sentiment Pipeline
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.
from transformers import pipeline
analyzer = pipeline('sentiment-analysis')
result = analyzer('This tutorial is amazing!')
# Output: [{'label': 'POSITIVE', 'score': 0.99}]3Phase 2: The Stateful Bot
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.
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained('microsoft/DialoGPT-small')
model = AutoModelForCausalLM.from_pretrained('microsoft/DialoGPT-small')4The Generation Engine
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.
# Simplified Chat Loop
user_input = tok.encode('Hello!' + tok.eos_token, return_tensors='pt')
history = torch.cat([history, user_input], dim=-1)
# Generate the response
output = model.generate(history, max_length=1000)5Deployment Complete
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.
# Capstone completed.
# AI applications successfully built.
print("NLP Track Mastered.")