Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- text-generation
+- causal
+- training
+- transformers
+- pytorch
+- jsonl
+- segmentation
+- validation
+size_categories:
+- 10M<n<100M
+---
+
+
+# 📚 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.
+
+---
+
+## 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**.
+
+---
+
+## Data Format
+
+Each record is stored as a JSON object:
+
+```json
+{
+  "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        |
+
+---
+
+##  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.
+
+---
+
+## How to Load the Dataset
+
+### Using Hugging Face Datasets
+
+```python
+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)
+```
+
+---
+
+### Reading JSONL Manually
+
+```python
+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:
+
+```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 properties:
+
+* **Average words per sample:** ~45
+* **Vocabulary:** Large natural English vocabulary
+* **Style:** Literary and narrative English
+* **Domain:** Fiction, non-fiction, historical texts
+
+---
+
+## 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.
+
+---
+
+## 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.
+
+---
+
+## Citation
+
+If you use this dataset in research or publications, please cite:
+
+```
+TinyWay-Gutenberg-Clean-40M
+NNEngine, 2026
+```
+
+---
+
+## 🧠 Maintainer
+
+Created and maintained by **Shivam Sharma**