| | ---
|
| | license: mit
|
| | task_categories:
|
| | - tabular-classification
|
| | - reinforcement-learning
|
| | - time-series-forecasting
|
| | language:
|
| | - en
|
| | tags:
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| | - chess
|
| | - maia
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| | - human-behavior
|
| | - lichess
|
| | - game-ai
|
| | pretty_name: Maia Chess 2025 (Processed 12-Bucket)
|
| | size_categories:
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| | - 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
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| |
|
| | > **"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)}
|
| | }
|
| |
|
| |
|
| | ***
|
| | |
