base_model: lczerolens/maia-1500
datasets:
- pilipolio/chess-positions-concepts
language: en
library_name: scikit-learn
license: mit
pipeline_tag: tabular-classification
tags:
- chess
- concept-detection
- interpretability
- lc0
- multi-label-classification
model-index:
- name: Chess Concept Probe
results:
- task:
type: tabular-classification
dataset:
name: Chess Positions with Concepts
type: pilipolio/chess-positions-concepts
revision: main
metrics:
- type: exact_match
value: 0.05298268670361694
- type: micro_precision
value: 0.028681196366378225
- type: micro_recall
value: 0.5357478202548626
- type: macro_precision
value: 0.018620338127770992
- type: macro_recall
value: 0.3286780957080038
- type: micro_auc
value: 0.785794468578521
- type: macro_auc
value: 0.6581696637373479
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).
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]
- 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.
See https://github.com/pilipolio/chess-sandbox README for complete examples.
Training Details
Training Data
Dataset: pilipolio/chess-positions-concepts
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
Same as training data. See pilipolio/chess-positions-concepts
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
[More Information Needed]
Software
[More Information Needed]
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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