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.gitignore

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+*.py
+*.jsonl
+.claude
+__pycache__/
+*.json
+*.txt
+Stacksystem/*
+v2_changes.md
+version_1_README.md

README.md

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+---
+license: apache-2.0
+task_categories:
+- text-generation
+- sentence-similarity
+language:
+- en
+tags:
+- code
+pretty_name: NPset-2-Python-Edu
+size_categories:
+- 1M<n<10M
+---
+
+![Axiomic Banner](AxiomicBanner.png)
+
+# NPset-2 (Python-Edu)
+
+A normalized semi-synthetic Python dataset for training small language models on code logic without the overhead of raw code syntax.
+
+![Tokenizer chart](tokenizer_chart.png)
+
+## Why NPset-2?
+
+Small language models develop latent logical representations but struggle with syntactic overhead. NPset-2 addresses this through the **TinyDSL v2** specification, which strips syntactic noise and provides explicit logical anchors. 
+
+## The v2 Specification
+
+
+NPset-2 introduces significant improvements over v1, designed to minimize "token tax" and maximize the model's ability to track long-range logical dependencies:
+
+1.  **Explicit Block Scoping**: All indented blocks (if, for, while, try, with) now use numbered/named anchors: `begin if 1` ... `end if 1`. This provides unambiguous attention anchors for small models.
+2.  **Natural Language Phrasing**:
+    *   **Functions**: `function find_max with input numbers`
+    *   **Calls**: `call fibonacci with n - 1`
+    *   **Loops**: `exit loop` and `next loop` instead of `break` and `continue`.
+3.  **Slicing**: Replaced symbol-heavy `[0:10]` with `starting from index 0 to 10`.
+4.  **Semantic Normalization**: 
+    *   `isinstance(x, int)` -> `type of x is int`
+    *   `lambda x: x+1` -> `function taking x returning x + 1`
+    *   `async for` -> `async for` (removing forced underscores).
+5.  **Strict English Filtering**: Documents with >0.5% Chinese characters are dropped, and all remaining text is scrubbed of non-ASCII characters to maintain a clean, English-only training distribution.
+
+## Performance (Context Capacity)
+
+When tested against standard tokenizers, TinyDSL v2 significantly expands the effective context window for logic-heavy training:
+
+| Tokenizer | Reduction (Tokens) | Context Capacity (2048 window) |
+| :--- | :--- | :--- |
+| **GPTX (Custom 32k)** | **+13.7%** | **7.1 -> 8.3 examples (+15.9%)** |
+| **GPT-2** | **+16.6%** | **7.4 -> 8.9 examples (+19.9%)** |
+| Qwen 2.5 | -8.1% | 10.1 -> 9.3 examples (-7.5%) |
+| Llama 3 | -2.2% | 8.3 -> 8.1 examples (-2.2%) |
+
+*Note: While raw character counts increase by ~17%, the "Token Tax" for logical constructs is drastically reduced for models not pre-specialized for code syntax.*
+
+## Format
+
+Parquet format with the following schema:
+
+| Field | Type | Description |
+|---|---|---|
+| `code` | string | Normalized TinyDSL v2 pseudocode |
+| `original_code` | string | Original Python source |
+| `original_language` | string | Always `python` |
+| `score` | float | Quality/Difficulty score (if available from source) |
+
+## Sources
+
+The dataset is compiled from high-quality educational and stack-overflow style Python sources, including:
+- `dbands/pythonMath`
+- `nomic-ai/cornstack-python-v1`
+- `zaydzuhri/stack-edu-python`
+- `jtatman/python-code-dataset-500k`
+- And other curated sources.

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