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