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Mitchins/
literary-tone-classifier-v0.1

Name: literary-tone-classifier-v0.1
Rating: 2.5 (5 reviews)

safetensors

deberta-v2

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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

python

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:

Class	What It Detects	Validation F1	Typical Use
dark	Grim, violent, oppressive, noir	91.1%	Filter grimdark fantasy, horror, noir
humorous	Satirical, witty, comedic	91.1%	Filter comedy, satire, lighthearted fiction
warm	Comforting, affectionate, uplifting	94.2%	Filter romance, cozy mysteries
melancholic	Sad, loss-focused, bittersweet	88.7%	Filter emotional drama, literary fiction
lyrical	Poetic prose, metaphorical	90.5%	Identify literary/poetic writing style

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:

python

# 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:

Tone	Conservative	Balanced	Aggressive
dark	≥0.75	≥0.65	≥0.55
humorous	≥0.70	≥0.60	≥0.50
warm/melancholic	≥0.80	≥0.70	≥0.60

Higher thresholds = fewer false positives, lower recall.

Example Use Cases

1. Filter Grimdark Passages

python

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

python

# 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

python

# 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

bibtex

@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.

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.