That’s a *great* pivot, and you’re thinking like a real applied machine learning engineer now. If libraries aren’t a barrier anymore, and you’re ready to start working with **real-world text**, then using **Project Gutenberg** for **NLP** is a smart move—especially because you’re already strong in Python and have a background in applied math.
Let’s walk through how to **use Project Gutenberg to build an NLP model** — from dataset collection to model training.
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## 🧠What You Can Do with Project Gutenberg Data
Project Gutenberg is a goldmine of free eBooks in the public domain. You can use it for many NLP projects like:
| Task | Description | Model Type |
|—————————–|————————————————-|———————-|
| 📖 Text Generation | Generate Shakespeare-like or Dickens-like text | Language Modeling |
| 🧾 Text Classification | Classify books by author or genre | Classification |
| 🧹 Summarization | Summarize chapters or whole books | Sequence-to-sequence |
| 👥 Named Entity Recognition | Extract people, places, events | Sequence tagging |
| 🧠Sentiment Analysis | Apply polarity scoring on sentences | Classification |
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## 📦 Step-by-Step: Use Project Gutenberg for NLP
### **Step 1: Install gutenberg or use requests for raw text**
bash
pip install gutenberg
But the gutenberg package has limitations. I suggest using the **raw text** from [https://www.gutenberg.org](https://www.gutenberg.org) instead.
Here’s how to fetch a book:
python
import requests
url = “https://www.gutenberg.org/files/1342/1342-0.txt” # Pride and Prejudice
response = requests.get(url)
text = response.text
print(text[:1000]) # Preview first 1000 characters
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### **Step 2: Clean the Text**
Books come with headers/footers. Clean them like this:
python
def clean_gutenberg_text(text):
start = text.find("*** START OF THIS PROJECT GUTENBERG EBOOK")
end = text.find("*** END OF THIS PROJECT GUTENBERG EBOOK")
return text[start:end]
cleaned_text = clean_gutenberg_text(text)
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### **Step 3: Tokenize and Preprocess**
Use nltk or spaCy:
bash
pip install nltk
python
import nltk
from nltk.tokenize import word_tokenize
nltk.download('punkt')
tokens = word_tokenize(cleaned_text.lower())
print(tokens[:20])
You can also remove stopwords, punctuation, etc.
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### **Step 4: Choose a Project Idea**
Here are 3 practical beginner-friendly projects with Gutenberg data:
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#### ✅ **1. Word Prediction Model**
Use n-grams to predict the next word.
python
from nltk import bigrams, FreqDist
import random
bi_grams = list(bigrams(tokens))
freq = FreqDist(bi_grams)
def predict_next_word(word):
candidates = [(a, b) for (a, b) in freq if a == word]
if not candidates:
return None
return max(candidates, key=lambda x: freq[x])[1]
print(predict_next_word(“elizabeth”))
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#### ✅ **2. Text Generation (Character-Level)**
Use an LSTM in Keras for a character-based language model (like GPT-mini!).
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#### ✅ **3. Author Classification**
Download 3-4 books each from 3 authors. Train a classifier (Naive Bayes or TF-IDF + SVM) to predict the author of a text excerpt.
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## 🗃 Where to Get More Books
Use a script to download multiple books from Gutenberg:
python
book_ids = [1342, 1661, 2701] # Add more IDs
books =
for book_id in book_ids:
url = f”https://www.gutenberg.org/files/book_id/book_id-0.txt”
text = requests.get(url).text
books[book_id] = clean_gutenberg_text(text)
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## 🚀 Want to Train a Language Model?
If you want to go further and train a **Transformer (like GPT-2)** on Gutenberg data, we can walk through that using Hugging Face’s transformers library and prepare your dataset accordingly.
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## 📘 Final Tip
Once you’ve built your first NLP project, even something small:
– Push it to GitHub
– Include a README explaining the model and the dataset
– Show some visualizations or outputs
That *is* your portfolio.
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