| | --- |
| | 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 |
| | ``` |
