sheepOp/docs/DATA_GUIDE.md

Data Collection Guide

This guide shows you how to get training data from the internet or create your own data.txt file.

Option 1: Use the Download Script

Quick Start

# Download Shakespeare text (recommended for testing)
python download_data.py --type shakespeare

# Create a sample data file
python download_data.py --type sample --output data/my_data.txt --samples 200

# Download Wikipedia article (requires: pip install wikipedia)
python download_data.py --type wikipedia --title "Artificial Intelligence" --output data/ai_article.txt

Available Options

Shakespeare Dataset:

python download_data.py --type shakespeare

Downloads classic Shakespeare text - great for testing!

Create Sample Data:

python download_data.py --type sample --output data/my_data.txt --samples 100

Creates a file with sample sentences about ML/AI.

Wikipedia Article:

python download_data.py --type wikipedia --title "Machine Learning" --output data/ml_article.txt

Downloads a Wikipedia article (requires pip install wikipedia).

Option 2: Manual Data Collection

Method A: Create Your Own data.txt

1. Create a text file:

nano data/my_data.txt
# or
vim data/my_data.txt

2. Add your text (one sentence per line):

This is my first training sample.
This is my second training sample.
Add as many lines as you want.

3. Save and use:

python train.py --data data/my_data.txt

Method B: Download from Public Datasets

Shakespeare Text:

curl -o data/shakespeare.txt https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt

Book Corpus Sample:

# Download Project Gutenberg books
curl -o data/book.txt https://www.gutenberg.org/files/1342/1342-0.txt  # Pride and Prejudice

News Articles:

# Download news text
curl -o data/news.txt https://raw.githubusercontent.com/sunnysai12345/News_Summary/master/news_summary_more.csv

Method C: Scrape Your Own Data

From Wikipedia (Python):

import wikipedia

page = wikipedia.page("Machine Learning")
with open("data/ml_article.txt", "w") as f:
    f.write(page.content)

From a Website:

import requests
from bs4 import BeautifulSoup

url = "https://example.com/article"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
text = soup.get_text()

with open("data/scraped.txt", "w") as f:
    f.write(text)

Option 3: Use Existing Datasets

Popular NLP Datasets

WikiText-2:

# Download WikiText-2
wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip
unzip wikitext-2-v1.zip
# Use: wikitext-2/wiki.train.tokens

OpenWebText Sample:

# Download sample
curl -o data/openwebtext_sample.txt https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt

BookCorpus:

# Various book sources available
# Check: https://github.com/soskek/bookcorpus

Data Format Requirements

Your data.txt file should:

• Have one text sample per line
• Use UTF-8 encoding
• Be plain text (no special formatting)

Example format:

This is the first training example.
This is the second training example.
Each line becomes one training sample.

Good:

Hello world!
This is a sentence.
Machine learning is cool.

Bad:

This is paragraph 1 with multiple sentences. This is sentence 2.
This is paragraph 2.

Preprocessing Tips

1. Clean your data:

import re

with open("raw_data.txt", "r") as f:
    text = f.read()

# Remove extra whitespace
text = re.sub(r'\s+', ' ', text)

# Split into sentences
sentences = text.split('.')

# Write one per line
with open("data/cleaned_data.txt", "w") as f:
    for sentence in sentences:
        if sentence.strip():
            f.write(sentence.strip() + '\n')

2. Split long texts:

# If you have long texts, split them into sentences
text = "Long paragraph here. Another sentence. More text."
sentences = text.split('.')
for sentence in sentences:
    if sentence.strip():
        print(sentence.strip())

Quick Test

1. Create a small test file:

cat > data/test.txt << EOF
Hello world!
This is a test.
Language models are cool.
EOF

2. Train with it:

python train.py --data data/test.txt --output ./checkpoints

Recommended Data Sources

• Small (for testing): Shakespeare text, sample_data.txt
• Medium (for training): Wikipedia articles, news articles
• Large (for serious training): WikiText-2, BookCorpus, OpenWebText

Next Steps

Once you have your data.txt file:

# Train your model
python train.py --data data/your_data.txt --output ./checkpoints

# Or use the sample data
python train.py --data data/sample_data.txt --output ./checkpoints

Happy training! 🚀