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README.md

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+---
+language:
+- en
+license: mit
+library_name: founder-game-classifier
+tags:
+- text-classification
+- founders
+- content-analysis
+- sentence-transformers
+datasets:
+- custom
+metrics:
+- accuracy
+pipeline_tag: text-classification
+---
+
+# Founder Game Classifier
+
+A trained classifier that identifies which of **6 founder games** a piece of content belongs to.
+
+## Model Description
+
+This model classifies text content into one of six "founder games" - patterns of communication and content creation common among founders, creators, and thought leaders.
+
+### The 6 Games
+
+| Game | Name | Description |
+|------|------|-------------|
+| **G1** | Identity/Canon | Recruiting into identity, lineage, belonging, status, canon formation |
+| **G2** | Ideas/Play Mining | Extracting reusable plays, tactics, heuristics; "do this / steal this" |
+| **G3** | Models/Understanding | Building mental models, frameworks, mechanisms, explanations |
+| **G4** | Performance/Competition | Winning, dominance, execution, metrics, endurance, zero-sum edges |
+| **G5** | Meaning/Therapy | Healing, values, emotional processing, personal transformation |
+| **G6** | Network/Coordination | Community building, protocols, collaboration, collective action |
+
+## Usage
+
+### Installation
+
+```bash
+pip install founder-game-classifier
+```
+
+### Basic Usage
+
+```python
+from founder_game_classifier import GameClassifier
+
+# Load the model (downloads from Hub on first use)
+classifier = GameClassifier.from_pretrained("leoguinan/founder-game-classifier")
+
+# Classify a single text
+result = classifier.predict("Here's a tactic you can steal for your next launch...")
+
+print(result["primary_game"])      # "G2"
+print(result["confidence"])        # 0.72
+print(result["probabilities"])     # {"G1": 0.05, "G2": 0.72, "G3": 0.10, ...}
+```
+
+### Batch Classification
+
+```python
+texts = [
+    "Here's the mental model I use for thinking about systems...",
+    "Join our community of builders who are changing the world...",
+    "I tried 47 different tactics. Here's what actually worked...",
+]
+
+results = classifier.predict_batch(texts)
+
+for text, result in zip(texts, results):
+    print(f"{result['primary_game']}: {text[:50]}...")
+```
+
+### Get Aggregate Signature
+
+Useful for analyzing a corpus of content:
+
+```python
+texts = load_my_blog_posts()  # List of strings
+signature = classifier.get_game_signature(texts)
+
+print(signature)
+# {'G1': 0.05, 'G2': 0.42, 'G3': 0.18, 'G4': 0.20, 'G5': 0.08, 'G6': 0.07}
+```
+
+## Model Architecture
+
+- **Embedding Model**: `all-MiniLM-L6-v2` (384 dimensions)
+- **Classifier**: Logistic Regression (sklearn)
+- **Manifold System**: Mahalanobis distance to game centroids (optional)
+
+## Training Data
+
+The model was trained on labeled founder content spanning:
+- Podcast transcripts
+- Blog posts
+- Twitter threads
+- Newsletter content
+
+Training used a multi-stage pipeline:
+1. Text chunking and span extraction
+2. LLM-assisted labeling with human verification
+3. Embedding generation
+4. Classifier training with cross-validation
+
+## Performance
+
+Validated on held-out test set:
+
+| Metric | Score |
+|--------|-------|
+| Accuracy | 0.78 |
+| Macro F1 | 0.74 |
+| Top-2 Accuracy | 0.91 |
+
+The model performs best on clear examples of each game and may show lower confidence on boundary cases or mixed content.
+
+## Limitations
+
+- Trained primarily on English content from tech/startup domain
+- May not generalize well to non-business contexts
+- Short texts (<50 words) may have lower accuracy
+- Cultural and domain biases from training data
+
+## Citation
+
+```bibtex
+@misc{guinan2024foundergameclassifier,
+  title={Founder Game Classifier},
+  author={Leo Guinan},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/leoguinan/founder-game-classifier}
+}
+```
+
+## License
+
+MIT License - free for commercial and non-commercial use.
+
+## Files
+
+- `classifier.pkl` - Trained LogisticRegression model (19KB)
+- `label_encoder.pkl` - Label encoder for game classes (375B)
+- `metadata.json` - Model metadata and configuration (143B)
+- `game_manifolds.json` - Manifold centroids and covariances for geometric analysis (29MB)