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--- |
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license: mit |
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task_categories: |
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- text-classification |
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- question-answering |
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- text-generation |
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language: |
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- en |
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size_categories: |
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- 10M<n<100M |
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--- |
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# 📚 TinyWay-Gutenberg-Clean (Compressed Shards) |
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A large-scale, high-quality English text dataset derived from Project Gutenberg. |
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The corpus has been cleaned, normalized, deduplicated, segmented into fixed-length samples, and stored as compressed JSONL shards for efficient large-scale language model training. |
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This dataset is intended for pretraining and experimentation with small and medium language models such as **TinyWay**, tokenizer training, and large-scale NLP research. |
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--- |
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## 📦 Dataset Overview |
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* **Name:** TinyWay-Gutenberg-Clean |
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* **Current Release:** ~19 compressed shards (`.jsonl.gz`) |
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* **Estimated Samples:** Tens of millions of text segments |
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* **Language:** English |
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* **Format:** Gzip-compressed JSON Lines (`.jsonl.gz`) |
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* **Source:** Project Gutenberg (public domain books) |
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* **License:** Public Domain |
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* **Maintainer:** Shivam (NNEngine / ITM AIR Lab) |
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Each record contains a clean text segment between **30 and 60 words**. |
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Future releases will scale this dataset further (e.g., 100M+ samples). |
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--- |
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## Data Format |
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Each line is a JSON object: |
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```json |
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{ |
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"id": "twg_000000012345", |
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"text": "Cleaned natural English text segment between thirty and sixty words.", |
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"word_count": 42, |
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"source": "gutenberg" |
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} |
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``` |
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### Fields |
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| Field | Description | |
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| ------------ | ------------------------------ | |
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| `id` | Unique sample identifier | |
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| `text` | Clean English text segment | |
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| `word_count` | Number of words in the segment | |
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| `source` | Data source identifier | |
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--- |
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## Data Processing Pipeline |
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The dataset was generated using a fully streaming pipeline to ensure scalability and low memory usage. |
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### Processing Steps |
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1. **Streaming Input** |
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* Text streamed from a Project Gutenberg mirror on Hugging Face. |
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2. **Text Cleaning** |
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* Removed Gutenberg headers and footers. |
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* Removed chapter titles, page numbers, and boilerplate text. |
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* Normalized whitespace and line breaks. |
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* Removed non-ASCII and control characters. |
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* Filtered malformed or extremely short segments. |
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3. **Segmentation** |
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* Text segmented into chunks of **30–60 words**. |
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4. **Validation** |
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* Enforced word count limits. |
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* Filtered invalid or noisy segments. |
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5. **Deduplication** |
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* Exact hash-based deduplication applied during generation. |
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6. **Compression & Sharding** |
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* Data stored as `.jsonl.gz` shards for efficient disk usage and streaming. |
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--- |
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## How to Load the Dataset |
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### Using Hugging Face Datasets (Streaming) |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset( |
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"NNEngine/TinyWay-Gutenberg-Clean", |
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split="train", |
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streaming=True |
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) |
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for i, sample in enumerate(dataset): |
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print(sample) |
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if i == 3: |
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break |
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``` |
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--- |
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### Reading a Shard Manually |
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```python |
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import gzip |
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import json |
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with gzip.open("train-00000.jsonl.gz", "rt", encoding="utf-8") as f: |
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for _ in range(3): |
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print(json.loads(next(f))) |
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``` |
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--- |
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## Dataset Characteristics (Approximate) |
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* **Average words per sample:** ~45 |
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* **Style:** Literary and narrative English |
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* **Domain:** Fiction, non-fiction, historical texts |
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* **Vocabulary:** Large natural English vocabulary |
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* **Compression:** ~60–70% size reduction vs raw JSONL |
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Exact statistics may vary per shard and will be expanded in future releases. |
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--- |
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## Limitations |
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* Primarily literary and historical language. |
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* No conversational chat data. |
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* No code or structured technical documentation. |
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* Some archaic vocabulary and sentence structures may appear. |
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* Deduplication is hash-based (near-duplicates may remain). |
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For conversational or web-style language modeling, this dataset should be mixed with complementary corpora. |
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--- |
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## License |
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All source texts originate from Project Gutenberg and are in the **public domain**. |
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This processed dataset is released for unrestricted research and commercial use. |
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--- |
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## Versioning & Roadmap |
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Planned future updates: |
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- Larger releases (target: 100M+ samples) |
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- Improved deduplication (near-duplicate filtering) |
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- Dataset statistics and analytics |
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- Additional language normalization |
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Each major release will be versioned clearly. |
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--- |
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## Citation |
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If you use this dataset in research or publications, please cite: |
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``` |
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TinyWay-Gutenberg-Clean |
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Shivam (NNEngine), 2026 |
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``` |
