literary-tone-classifier-v0.1

Literary Tone Classifier v0.1

A DeBERTa-based classifier for detecting emotional tone in literary fiction passages. Designed for commercial fiction filtering and training data curation.

Model Description

• Base Model: microsoft/deberta-v3-small (86M parameters)
• Context Window: 2048 tokens (~1500 words, 3-5 pages)
• Training Data: 4,052 literary passages from various fiction genres
• Task: Multi-class text classification (5 tones)

Quick Start

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "Mitchins/literary-tone-classifier-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = """Your literary passage here..."""

inputs = tokenizer(text, max_length=2048, truncation=True,
                   padding='max_length', return_tensors='pt')
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=1)[0]

tones = ['dark', 'humorous', 'lyrical', 'melancholic', 'warm']
for tone, score in zip(tones, probs):
    print(f"{tone}: {score:.2%}")

Classes

The model predicts 5 emotional tone categories:

Performance

Validation Set (406 passages)

• Overall Accuracy: 91.38%
• F1-Macro: 91.12%
• Precision-Macro: 90.72%

Real-World Testing (Published Books)

• Dark detection: 80% accuracy (tested on Abercrombie, Jemisin, Sanderson, King, Lawrence)
• Humorous detection: 100% accuracy (tested on Pratchett, Weir)
• Overall: 87.5% accuracy on 1500-word passages from published fiction

Synthetic Test Set

• Accuracy: 93.75% (15/16 correct)
• Better generalization than validation (-2.4pp gap)

Practical Usage: Commercial Fiction Filtering

The model works best when interpreted as a commercial fiction filter:

# Recommended interpretation
if probs[tones.index('humorous')] >= 0.60:
    category = "Comedy/Satire"  # 98% precision - very reliable

elif probs[tones.index('dark')] >= 0.60:
    category = "Grim/Horror"  # 88% precision - reliable

elif probs[tones.index('warm')] >= 0.70 or probs[tones.index('melancholic')] >= 0.70:
    category = "Heartfelt/Emotional"  # Combine for relationship-focused fiction

else:
    category = "Neutral"  # Likely action/thriller/plot-driven

What Works Well

✅ Dark/Grim (grimdark fantasy, noir, horror)

• High precision (88.5%)
• Works on Abercrombie, Jemisin, King
• Good for filtering dark content

✅ Humorous (comedy, satire)

• Very high precision (98.2%)
• Perfect accuracy on Pratchett, Weir
• Rarely produces false positives

✅ Warm (romance, cozy)

• Best validation performance (94.2% F1)
• High recall (97.6%)
• Not yet tested on published romance

✅ Melancholic (drama, loss)

• Good recall (95.9%)
• Catches emotional passages well
• Not yet tested on published literary fiction

Limitations

⚠️ Lyrical class has narrow application

• Detects poetic/metaphorical prose, not epic scale
• Rarely triggers on genre fiction (fantasy/sci-fi)
• Only triggered on 1/7 published books tested
• More useful as "literary style" metadata than tone category

⚠️ Cannot detect pacing

• No "action-packed" vs "contemplative" detection
• "Tense/Propulsive" is not included (considered book-level, not passage-level)

⚠️ Limited testing on some categories

• Warm and melancholic not yet validated on published romance/literary fiction
• Most testing focused on dark and humorous

⚠️ Edge cases

• Struggled with Stephen King's conversational horror style
• Can confuse lyrical with melancholic (both reflective)
• May classify Pratchett's dark humor scenes inconsistently

Training Details

• Optimizer: AdamW
• Learning Rate: 2e-5
• Batch Size: 2 per device, gradient accumulation 16 (effective: 32)
• Epochs: 5
• Loss Function: Focal Loss (γ=2.0) with class weights
• Early Stopping: Patience 3 on validation F1-macro
• Hardware: Single GPU, ~1.5 hours training time

Data

• Total samples: 4,052 literary passages
• Train/Val split: 90/10 stratified
• Source: Consolidated from 12-tone taxonomy
• Class distribution:
  • humorous: 1,264 (31.2%)
  • dark: 980 (24.2%)
  • warm: 828 (20.4%)
  • lyrical: 490 (12.1%)
  • melancholic: 490 (12.1%)

Recommended Thresholds

Based on validation precision:

Higher thresholds = fewer false positives, lower recall.

Example Use Cases

1. Filter Grimdark Passages

dark_passages = []
for passage in corpus:
    inputs = tokenizer(passage, max_length=2048, truncation=True,
                      padding='max_length', return_tensors='pt')
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=1)[0]

    if probs[0] >= 0.65:  # dark threshold
        dark_passages.append((passage, probs[0].item()))

# Expected precision: ~85%

2. Filter Comedy

# Very high precision due to conservative model
if probs[1] >= 0.60:  # humorous threshold
    comedy_passages.append(passage)
# Expected precision: ~95%+

3. Identify Literary Prose Style

# Use lyrical score as prose density indicator
if probs[2] >= 0.20:  # lyrical threshold
    literary_style = "poetic/metaphorical"
else:
    literary_style = "commercial/invisible"

When NOT to Use This Model

❌ Don't use for:

• Detecting pacing (fast vs slow)
• Epic/wondrous atmosphere (model's "lyrical" doesn't capture this)
• Fine-grained romance subgenres
• Non-English text
• Very short passages (<200 words)
• Poetry or non-prose text

❌ Known failure modes:

• Conversational horror (e.g., Stephen King) may not register as dark
• Dry satirical passages may not register as humorous
• Epic fantasy worldbuilding won't trigger lyrical
• Action scenes default to "neutral" (no action category)

Comparison to Baseline

Previous 12-tone model:

• Validation: 64.76%
• Synthetic test: 45.83%
• Published books: 66.67%

This 5-tone model v0.1:

• Validation: 91.38% (+26.6pp)
• Synthetic test: 93.75% (+47.9pp)
• Published books: 87.5% (+20.8pp)

Model Version

• Version: 0.1
• Date: 2025-12-15
• Status: Production-ready for dark and humorous filtering; warm/melancholic need more testing

Future Improvements (v0.2)

Planned improvements:

• Replace "lyrical" with "wondrous/atmospheric" for epic scale detection
• Validate warm/melancholic on published romance and literary fiction
• Potentially add pacing classifier as separate model
• Test on more diverse edge cases

Citation

@misc{literary-tone-v0.1,
  title={Literary Tone Classifier v0.1},
  author={Your Name},
  year={2025},
  howpublished={HuggingFace Model Hub}
}

License

Model weights: [Your chosen license] Base model (DeBERTa): MIT License

Contact

For issues or questions about this model, please open an issue on the model repository.

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Note: This is v0.1 - a working prototype optimized for dark and humorous detection. Some categories (warm, melancholic, lyrical) need additional validation on diverse published fiction before production use in all scenarios.