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README.md

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+---
+language: en
+license: mit
+library_name: transformers
+tags:
+- text-classification
+- character-analysis
+- plot-arc
+- narrative-analysis
+- deberta-v3
+- binary-classification
+datasets:
+- custom
+metrics:
+- accuracy
+- f1
+model-index:
+- name: plot-arc-classifier
+  results:
+  - task:
+      type: text-classification
+      name: Character Plot Arc Classification
+    dataset:
+      type: custom
+      name: Character Arc Dataset
+    metrics:
+    - type: accuracy
+      value: 0.796
+      name: Accuracy
+    - type: f1
+      value: 0.796
+      name: F1 Score (Strong Class)
+    - type: precision
+      value: 0.777
+      name: Precision (Strong Class)
+    - type: recall
+      value: 0.816
+      name: Recall (Strong Class)
+base_model: microsoft/deberta-v3-xsmall
+---
+
+# Plot Arc Character Classifier
+
+A DeBERTa-v3-XSmall model fine-tuned to classify fictional characters based on their plot arc potential.
+
+## Model Description
+
+This model classifies character descriptions into two categories:
+- **STRONG** (label 1): Characters with both internal conflict and external responsibilities/events
+- **WEAK** (label 0): Characters with no plot arc, pure internal conflict only, or pure external events only
+
+The model fixes critical bias issues where simple background characters (shopkeepers, guards) were incorrectly classified as plot-significant.
+
+## Training Data
+
+- **Dataset Size**: 11,888 balanced examples (50/50 split)
+- **Training Examples**: 9,510
+- **Validation Examples**: 2,378
+- **Source**: Custom 4-way classified character descriptions from literature
+
+### Label Mapping
+- **STRONG (1)**: Characters classified as "BOTH" (internal conflict + external events)
+- **WEAK (0)**: Characters classified as "NONE", "INTERNAL", or "EXTERNAL"
+
+## Training Details
+
+- **Base Model**: microsoft/deberta-v3-xsmall (22M parameters)
+- **Training Time**: ~15 minutes
+- **Batch Size**: 8 (with gradient accumulation = 2)
+- **Max Sequence Length**: 384 tokens
+- **Learning Rate**: 5e-5 with warmup
+- **Early Stopping**: Yes (stopped at 3.7/5 epochs)
+
+## Performance
+
+### Validation Metrics
+| Metric | Score |
+|--------|-------|
+| Accuracy | 79.6% |
+| F1 (Strong) | 79.6% |
+| Precision (Strong) | 77.7% |
+| Recall (Strong) | 81.6% |
+
+### Synthetic Test Results
+**100% accuracy** on diverse test cases including previously problematic examples:
+
+| Character Type | Example | Prediction | Confidence |
+|----------------|---------|------------|------------|
+| Background (NONE) | Baker, Guard | WEAK ✅ | 98.9%, 98.5% |
+| Pure Internal | Haunted Artist | WEAK ✅ | 93.9% |
+| Pure External | Military Commander | WEAK ✅ | 94.5% |
+| Both (Internal+External) | Conflicted King | STRONG ✅ | 95.1% |
+| Both (Trauma+Mission) | PTSD Captain | STRONG ✅ | 95.5% |
+| Both (Doubt+Quest) | Uncertain Prophet | STRONG ✅ | 96.0% |
+
+**Key Achievement**: Fixed critical bias where simple background characters were incorrectly classified as plot-significant.
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("plot-arc-classifier")
+model = AutoModelForSequenceClassification.from_pretrained("plot-arc-classifier")
+
+# Example usage
+def classify_character(description):
+    inputs = tokenizer(description, return_tensors="pt", truncation=True, max_length=384)
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        predicted_class = torch.argmax(probabilities, dim=-1).item()
+    
+    labels = {0: "WEAK", 1: "STRONG"}
+    confidence = probabilities[0][predicted_class].item()
+    
+    return labels[predicted_class], confidence
+
+# Test examples
+examples = [
+    "A baker who makes fresh bread daily and serves customers with a smile.",
+    "A warrior haunted by past failures who must lead a desperate battle to save his homeland while confronting his inner demons.",
+]
+
+for desc in examples:
+    label, conf = classify_character(desc)
+    print(f"'{desc[:50]}...': {label} ({conf:.3f})")
+```
+
+## Model Improvements
+
+This model addresses critical issues from previous versions:
+
+1. **Fixed Bias**: No longer classifies simple background characters as STRONG
+2. **Proper Discrimination**: Requires both internal and external elements for STRONG classification  
+3. **Balanced Training**: 50/50 split prevents class imbalance issues
+4. **Clean Taxonomy**: Based on proper 4-way character analysis
+
+## Limitations
+
+- Trained on English literary character descriptions
+- May not generalize well to other domains (screenwriting, gaming, etc.)
+- Performance may degrade on very short or very long descriptions
+- Cultural bias toward Western narrative structures
+
+## Ethical Considerations
+
+This model is designed for narrative analysis and creative writing assistance. It should not be used to make judgments about real people or for any discriminatory purposes.
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{plot-arc-classifier-2024,
+  title={Plot Arc Character Classifier},
+  author={Generated with Claude Code},
+  year={2024},
+  url={https://huggingface.co/plot-arc-classifier}
+}
+```
+
+## Training Infrastructure
+
+- **Framework**: 🤗 Transformers
+- **Hardware**: Apple Silicon (MPS)
+- **Optimization**: Memory-optimized for MPS training
+- **Early Stopping**: Enabled to prevent overfitting
+
+---
+
+🤖 Generated with [Claude Code](https://claude.ai/code)
+
+Co-Authored-By: Claude <[email protected]>

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config.json

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tokenizer_config.json

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