📚 TinyWay-Gutenberg-Clean-40M

A large-scale, high-quality English text dataset derived from Project Gutenberg, cleaned, normalized, deduplicated, and segmented into fixed-length samples for efficient language model pretraining.

This dataset is designed to support training small and medium language models such as TinyWay, tokenizer training, embedding models, and large-scale NLP experimentation.

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Dataset Overview

• Name: TinyWay-Gutenberg-Clean-40M
• Samples: ~40,000,000
• Language: English
• Format: JSONL (optionally gzip-compressed)
• Source: Project Gutenberg (public domain books)
• License: Public Domain
• Intended Use: Language model pretraining, tokenizer training, representation learning

Each line in the dataset contains a clean text segment between 30 and 60 words.

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Data Format

Each record is stored as a JSON object:

{
  "id": "twg_000000000123",
  "text": "Cleaned text segment of natural English language between thirty and sixty words.",
  "word_count": 42,
  "source": "gutenberg"
}

Fields

Field	Description
id	Unique sample identifier
text	Clean English text segment
word_count	Number of words in the sample
source	Data source identifier

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Data Processing Pipeline

The dataset was generated using a fully streaming pipeline to ensure scalability and low memory usage.

Steps

1. Streaming Input

   • Data loaded from a Project Gutenberg mirror using Hugging Face streaming APIs.

2. Text Cleaning

   • Removed Gutenberg headers and footers
   • Removed chapter titles and page numbers
   • Normalized whitespace and line breaks
   • Removed non-ASCII and control characters
   • Removed URLs and artifacts

3. Segmentation

   • Text split into fixed segments of 30–60 words.

4. Validation

   • Enforced word count constraints
   • Filtered short or malformed segments

5. Deduplication

   • Exact hash-based deduplication applied during generation.

6. Output

   • Stored as JSONL files (optionally gzip-compressed).
   • Sharded for easier distribution and loading.

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How to Load the Dataset

Using Hugging Face Datasets

from datasets import load_dataset

dataset = load_dataset(
    "NNEngine/TinyWay-Gutenberg-Clean-40M",
    split="train",
    streaming=True
)

for sample in dataset.take(3):
    print(sample)

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Reading JSONL Manually

import json

with open("data/train-00000.jsonl", "r", encoding="utf-8") as f:
    for _ in range(3):
        print(json.loads(next(f)))

If files are compressed:

import gzip
import json

with gzip.open("train-00000.jsonl.gz", "rt", encoding="utf-8") as f:
    for _ in range(3):
        print(json.loads(next(f)))

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Dataset Characteristics

Approximate properties:

• Average words per sample: ~45
• Vocabulary: Large natural English vocabulary
• Style: Literary and narrative English
• Domain: Fiction, non-fiction, historical texts

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Limitations

• Content is primarily literary and historical in nature.
• No conversational, chat, or code data.
• Some archaic vocabulary and sentence structure may appear.
• Deduplication is hash-based (near-duplicates may remain).

For conversational or modern web text, additional datasets should be mixed.

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License

All source texts originate from Project Gutenberg and are in the public domain. This processed dataset is released for unrestricted research and commercial use.

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Citation

If you use this dataset in research or publications, please cite:

TinyWay-Gutenberg-Clean-40M
NNEngine, 2026

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🧠 Maintainer

Created and maintained by Shivam Sharma