Structuring a Real NLP Project
Data, code, and model layout.
Structuring a Real NLP Project is a free NLP Academy lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the NLP Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Structure Matters
A messy folder is the fastest way to lose a model. Good project structure keeps your data, code, and results easy to find and trust. 📁
Separate Data and Code
Keep your raw data in its own folder, far from your scripts. You never want a stray edit to overwrite the dataset you trained on.
A Simple Folder Layout
A clean layout makes a project readable at a glance. Here is a layout that scales from tiny demos to real apps.
project/
data/ raw text and labels
src/ your python code
models/ saved models
notebooks/ experimentsNever Touch Raw Data
Treat raw data as read-only. Write any cleaned version to a new file, so you can always reproduce results from the original source.
Split Train and Test Early
Decide your train/test split before you experiment. Testing on data the model already saw gives you a score that is far too optimistic.
from sklearn.model_selection import train_test_split
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2)One Job Per Module
Give each file one clear job. A module for loading data, one for features, one for training keeps changes small and safe.
Pin Your Dependencies
List exact library versions in a requirements file. A model that worked last month can break when a library quietly updates.
scikit-learn==1.4.0
pandas==2.2.0
joblib==1.3.2Write a README
A short README explains how to run your project. Future you, six months from now, will be very grateful for those few lines.
Keep Secrets Out of Code
Never hardcode API keys or passwords. Store secrets in environment variables and load them at runtime instead.
import os
api_key = os.environ["OPENAI_API_KEY"]Set a Random Seed
Set a random seed so splits and shuffles repeat the same way. Reproducible runs make your numbers something you can actually compare. 🎲
import numpy as np
np.random.seed(42)Version Control Everything
Track your code with git from day one. Commits give you a safety net and a clear history of every change you made.
Quick Check
Think about how you should treat your original dataset.
Recap
You learned to separate data and code, keep raw data read-only, pin versions, seed randomness, and track everything in git. A solid base. ✅
Frequently asked questions
Is the “Structuring a Real NLP Project” lesson free?
Yes — the full text of “Structuring a Real NLP Project” is free to read here on the web, and the NLP Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the NLP Academy course, upgrade to CoddyKit PRO.
What will I learn in “Structuring a Real NLP Project”?
Data, code, and model layout. You practise NLP Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start NLP Academy?
No prior experience is required. NLP Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Structuring a Real NLP Project” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this NLP Academy lesson?
Yes. Every NLP Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Structuring a Real NLP Project
- scikit-learn Pipelines End to End
- Saving and Loading Your Model
- Predicting on Brand-New Text