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README.md

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+---
+license: mit
+task_categories:
+- tabular-classification
+- reinforcement-learning
+- time-series-forecasting
+language:
+- en
+tags:
+- chess
+- maia
+- human-behavior
+- lichess
+- game-ai
+pretty_name: Maia Chess 2025 (Processed 12-Bucket)
+size_categories:
+- 10M<n<100M
+---
+
+## Related Projects
+
+- 🧠 **Maia Chess / Cognitive Chess Coach**  
+  https://github.com/ygkali/zweig-chess-engine
+
+This dataset is designed to train human-aligned chess models such as Maia-style
+ELO-conditioned neural networks.
+
+# ♟️ Maia Chess 2025: Human-Aligned Chess Dataset
+
+> **"Predicting human moves, not engine moves."**
+
+This dataset contains **11.15 million processed chess games** from the Lichess Open Database (Year 2025), specifically curated and segmented to train **Human-Aligned AI models** (Maia Chess).
+
+Unlike traditional chess datasets that aim for objective optimality (Stockfish evaluation), this dataset captures the **subjective, error-prone, and stylistic nature of human play** across 12 distinct skill levels.
+
+## 📊 Dataset Structure & Distribution
+
+The data is partitioned into **12 Skill Buckets** based on the players' average ELO rating. This granular segmentation allows for **Isolated Training** or **Curriculum Learning** strategies, enabling the model to mimic specific skill levels from "Novice" to "Elite".
+
+| ID | Filename | ELO Range | Population % | Description | Skill Level |
+| :--- | :--- | :--- | :--- | :--- | :--- |
+| **01** | `train_01.pgn` | **400 - 1050** | 8.3% | Novice | 🌱 Beginner |
+| **02** | `train_02.pgn` | **1051 - 1200** | 9.6% | Beginner | 🌱 Beginner |
+| **03** | `train_03.pgn` | **1201 - 1325** | 11.6% | Casual Player | ♟️ Casual |
+| **04** | `train_04.pgn` | **1326 - 1425** | 11.4% | Lower Intermediate | ♟️ Casual |
+| **05** | `train_05.pgn` | **1426 - 1500** | 9.1% | Intermediate | ⚔️ Intermediate |
+| **06** | `train_06.pgn` | **1501 - 1575** | 9.1% | Upper Intermediate | ⚔️ Intermediate |
+| **07** | `train_07.pgn` | **1576 - 1650** | 8.5% | Advanced Intermediate | ⚔️ Intermediate |
+| **08** | `train_08.pgn` | **1651 - 1750** | 10.2% | Club Player | 🏅 Advanced |
+| **09** | `train_09.pgn` | **1751 - 1875** | 9.8% | Strong Club Player | 🏅 Advanced |
+| **10** | `train_10.pgn` | **1876 - 2100** | 8.8% | Expert | 🏆 Expert |
+| **11** | `train_11.pgn` | **2101 - 2400** | 3.3% | Master | 👑 Master |
+| **12** | `train_12.pgn` | **2401 - 3000** | **0.3%** | Elite / Super-Human | 🤖 Super-Human |
+
+> **⚠️ Data Scarcity Alert:** The Elite bucket (ID 12) represents only **0.3%** of the total population. Models trained on this bucket may require transfer learning from lower buckets to converge effectively.
+
+## 🚀 Usage
+
+You can load this dataset in **Streaming Mode** to avoid downloading the entire 10GB+ archive. This is ideal for Colab or low-disk environments.
+
+```python
+from datasets import load_dataset
+
+# Example: Load only the Grandmaster games (Bucket 12)
+dataset = load_dataset(
+    "ygkali/zweig-chess-engine-processed",
+    data_files="train_12.pgn",
+    streaming=True
+)
+
+print("Streaming games...")
+for i, game in enumerate(dataset['train']):
+    print(f"Game {i+1}: {game['text'][:50]}...")
+    if i == 5: break
+
+##
+
+ZWEIG Chess Engine Dataset is a general-purpose human chess dataset, while Maia Chess models are downstream consumers of this data.
+
+##
+
+🛠️ Methodology
+1. Source
+
+    Origin: Lichess Open Database (Jan 2025 - Dec 2025).
+
+    Time Controls: Blitz, Rapid, Classical (Bullet excluded to reduce noise).
+
+2. Preprocessing Pipeline
+
+    Parsing: python-chess library used for PGN parsing.
+
+    Filtering: Games with fewer than 10 moves were discarded.
+
+    Bucketing: Games were assigned to buckets based on the average ELO of White and Black players: (White_ELO + Black_ELO) / 2.
+
+⚖️ License & Citation
+
+This dataset is derived from Lichess (CC0) and is released under the MIT License.
+Kod snippet'i
+
+@dataset{maia_chess_2025,
+  author = {ygkali},
+  title = {Maia Chess 2025: Human-Aligned Chess Dataset},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {[https://huggingface.co/datasets/ygkla/zweig-chess-engine-processed](https://huggingface.co/datasets/ygkla/zweig-chess-engine-processed)}
+}
+
+
+***

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