chess-positions-extractor

Model Card for chess-concept-probe

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Model Details

Model Description

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Trained multi-label classifier for detecting 12 chess concepts (discovered_attack, exposed_king, fork, initiative, mating_threat, outpost, passed_pawn, pin, sacrifice, skewer, weak_square, zugzwang) from LC0 layer activations (block3/conv2/relu).

Trained from pilipolio/chess-sandbox@df742cf.

• Developed by: chess-sandbox
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: Multi-label tabular classifier (scikit-learn LogisticRegression)
• Language(s) (NLP): en
• License: mit
• Finetuned from model [optional]: lczerolens/maia-1500

Model Sources [optional]

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• Repository: https://github.com/pilipolio/chess-sandbox
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

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Direct Use

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Extract chess concepts from positions. See https://github.com/pilipolio/chess-sandbox for usage examples.

Downstream Use [optional]

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[More Information Needed]

Out-of-Scope Use

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Not suitable for non-chess domains or positions outside training distribution.

Bias, Risks, and Limitations

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[More Information Needed]

Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

See https://github.com/pilipolio/chess-sandbox README for complete examples.

Training Details

Training Data

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Dataset: pilipolio/chess-positions-concepts

Training Procedure

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Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

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[More Information Needed]

Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Same as training data. See pilipolio/chess-positions-concepts

Factors

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[More Information Needed]

Metrics

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Multi-label metrics: precision, recall, AUC, subset accuracy. See evaluation results above.

Results

See evaluation results in model card metadata above and metadata.json for detailed per-concept breakdown.

Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
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Technical Specifications [optional]

Model Architecture and Objective

sklearn LogisticRegression (C=1.0) with OneVsRestClassifier wrapper for multi-label classification.

Compute Infrastructure

CPU-based training (8 cores typical)

Hardware

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Software

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Citation [optional]

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BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

https://github.com/pilipolio/chess-sandbox/issues