Model Card for chess-concept-probe
Model Details
Model Description
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).
- 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]
- Repository: https://github.com/pilipolio/chess-sandbox
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
Extract chess concepts from positions. See
https://github.com/pilipolio/chess-sandbox for usage examples.
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
Not suitable for non-chess domains or positions outside training distribution.
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
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.
Training Details
Training Data
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
Factors
[More Information Needed]
Metrics
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]
[More Information Needed]
Environmental Impact
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]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
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]
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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