The Critical Role of Datasets in Training Language Models

A Good Dataset for Training a Language Model

Training language models requires high-quality datasets, as evidenced by Common Crawl’s 9.5 petabytes of web content—though it demands rigorous cleaning to address biases and noise. The WikiText-2 dataset, with 2 million curated words, exemplifies the balance between quality and practicality.

Why This Matters

Ideal language models would learn from flawless, unbiased data, but real-world datasets like Common Crawl contain duplicates, offensive material, and formatting errors. Cleaning costs can exceed training costs, as seen in studies where 30% of computational resources were spent on data preprocessing. Wikipedia, while well-curated, risks overfitting due to its encyclopedic structure, highlighting the trade-off between quality and diversity.

Key Insights

• “Common Crawl contains 9.5 PB of web content but requires filtering (MachineLearningMastery.com, 2025)”
• “WikiText-2 offers 2 million words from curated Wikipedia articles (MachineLearningMastery.com, 2025)”
• “Hugging Face datasets used by researchers for standardized access (MachineLearningMastery.com, 2025)“

Working Example

import random
from datasets import load_dataset

dataset = load_dataset("wikitext", "wikitext-2-raw-v1")
print(f"Size of the dataset: {len(dataset)}")

n = 5
while n > 0:
    idx = random.randint(0, len(dataset)-1)
    text = dataset[idx]["text"].strip()
    if text and not text.startswith("="):
        print(f"{idx}: {text}")
    n -= 1

Practical Applications

• Use Case: “WikiText-2 for training models on structured knowledge”
• Pitfall: “Over-reliance on Wikipedia may cause models to overfit to encyclopedic style”

References:

• https://machinelearningmastery.com/datasets-for-training-a-language-model/
• https://huggingface.co/datasets
• https://commoncrawl.org/
• https://the-pile.readthedocs.io/

---