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license: mit
tags:
- text-classification
- modernbert
- orality
- linguistics
- rhetorical-analysis
language:
- en
metrics:
- f1
- accuracy
base_model:
- answerdotai/ModernBERT-base
pipeline_tag: text-classification
library_name: transformers
datasets:
- custom
model-index:
- name: bert-marker-subtype
results:
- task:
type: text-classification
name: Marker Subtype Classification
metrics:
- type: f1
value: 0.493
name: F1 (macro)
- type: accuracy
value: 0.500
name: Accuracy
---
# Havelock Marker Subtype Classifier
ModernBERT-based classifier for **71 fine-grained rhetorical marker subtypes** on the oral–literate spectrum, grounded in Walter Ong's *Orality and Literacy* (1982).
This is the finest level of the Havelock span classification hierarchy. Given a text span identified as a rhetorical marker, the model classifies it into one of 71 specific rhetorical devices (e.g., `anaphora`, `epistemic_hedge`, `vocative`, `nested_clauses`).
## Model Details
| Property | Value |
|----------|-------|
| Base model | `answerdotai/ModernBERT-base` |
| Architecture | `ModernBertForSequenceClassification` |
| Task | Multi-class classification (71 classes) |
| Max sequence length | 128 tokens |
| Test F1 (macro) | **0.493** |
| Test Accuracy | **0.500** |
| Missing labels (test) | 1/71 (`proverb`) |
| Parameters | ~149M |
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "HavelockAI/bert-marker-subtype"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
span = "it seems likely that this would, in principle, be feasible"
inputs = tokenizer(span, return_tensors="pt", truncation=True, max_length=128)
with torch.no_grad():
logits = model(**inputs).logits
pred = torch.argmax(logits, dim=1).item()
print(f"Marker subtype: {model.config.id2label[pred]}")
```
## Label Taxonomy (71 subtypes)
### Oral Subtypes (36)
| Category | Subtypes |
|----------|----------|
| **Repetition & Pattern** | `anaphora`, `epistrophe`, `parallelism`, `tricolon`, `lexical_repetition`, `refrain` |
| **Sound & Rhythm** | `alliteration`, `assonance`, `rhyme`, `rhythm` |
| **Address & Interaction** | `vocative`, `imperative`, `second_person`, `inclusive_we`, `rhetorical_question`, `audience_response`, `phatic_check`, `phatic_filler` |
| **Conjunction** | `polysyndeton`, `asyndeton`, `simple_conjunction` |
| **Formulas** | `discourse_formula`, `proverb`, `religious_formula`, `epithet` |
| **Narrative** | `named_individual`, `specific_place`, `temporal_anchor`, `sensory_detail`, `embodied_action`, `everyday_example` |
| **Performance** | `dramatic_pause`, `self_correction`, `conflict_frame`, `us_them`, `intensifier_doubling`, `antithesis` |
### Literate Subtypes (35)
| Category | Subtypes |
|----------|----------|
| **Abstraction** | `nominalization`, `abstract_noun`, `conceptual_metaphor`, `categorical_statement` |
| **Syntax** | `nested_clauses`, `relative_chain`, `conditional`, `concessive`, `temporal_embedding`, `causal_chain` |
| **Hedging** | `epistemic_hedge`, `probability`, `evidential`, `qualified_assertion`, `concessive_connector` |
| **Impersonality** | `agentless_passive`, `agent_demoted`, `institutional_subject`, `objectifying_stance`, `third_person_reference` |
| **Scholarly Apparatus** | `citation`, `footnote_reference`, `cross_reference`, `metadiscourse`, `methodological_framing` |
| **Technical** | `technical_term`, `technical_abbreviation`, `enumeration`, `list_structure`, `definitional_move` |
| **Connectives** | `contrastive`, `causal_explicit`, `additive_formal`, `aside` |
## Training
### Data
22,367 span-level annotations from the Havelock corpus with marker types normalized against a canonical taxonomy at build time. Each span carries a `marker_subtype` field. Only subtypes with ≥10 examples are included. A stratified 80/10/10 train/val/test split was used with swap-based optimization to balance label distributions across splits. The test set contains 2,357 spans.
### Hyperparameters
| Parameter | Value |
|-----------|-------|
| Epochs | 20 |
| Batch size | 16 |
| Learning rate | 3e-5 |
| Optimizer | AdamW (weight decay 0.01) |
| LR schedule | Cosine with 10% warmup |
| Gradient clipping | 1.0 |
| Loss | Focal loss (γ=2.0) + class weights |
| Mixout | 0.1 |
| Mixed precision | FP16 |
| Min examples per class | 10 |
### Training Metrics
Best checkpoint selected at epoch 15 by missing-label-primary, F1-tiebreaker (0 missing, F1 0.486).
### Test Set Classification Report
<details><summary>Click to expand per-class precision/recall/F1/support</summary>
```
precision recall f1-score support
abstract_noun 0.408 0.330 0.365 88
additive_formal 0.286 0.167 0.211 12
agent_demoted 0.667 1.000 0.800 10
agentless_passive 0.583 0.491 0.533 57
alliteration 0.500 0.200 0.286 10
anaphora 0.500 0.537 0.518 41
antithesis 0.947 0.818 0.878 22
aside 0.615 0.216 0.320 37
assonance 1.000 0.960 0.980 25
asyndeton 0.636 0.500 0.560 14
audience_response 1.000 0.800 0.889 10
categorical_statement 0.103 0.200 0.136 20
causal_chain 0.442 0.452 0.447 42
causal_explicit 0.400 0.468 0.431 47
citation 0.743 0.565 0.642 46
conceptual_metaphor 0.065 0.051 0.057 39
concessive 0.595 0.556 0.575 45
concessive_connector 0.882 0.833 0.857 18
conditional 0.596 0.609 0.602 87
conflict_frame 0.733 0.733 0.733 15
contrastive 0.533 0.525 0.529 61
cross_reference 0.733 0.458 0.564 24
definitional_move 0.286 0.200 0.235 10
discourse_formula 0.405 0.508 0.451 118
dramatic_pause 0.833 0.500 0.625 10
embodied_action 0.375 0.214 0.273 42
enumeration 0.510 0.605 0.553 43
epistemic_hedge 0.102 0.357 0.159 14
epistrophe 0.824 0.875 0.848 16
epithet 0.333 0.250 0.286 12
everyday_example 0.312 0.179 0.227 28
evidential 0.667 0.432 0.525 37
footnote_reference 0.417 0.500 0.455 10
imperative 0.645 0.600 0.622 100
inclusive_we 0.630 0.576 0.602 59
institutional_subject 0.938 0.714 0.811 21
intensifier_doubling 0.944 0.773 0.850 22
lexical_repetition 0.417 0.556 0.476 45
list_structure 0.267 0.174 0.211 23
metadiscourse 0.085 0.182 0.116 22
methodological_framing 0.500 0.190 0.276 21
named_individual 0.500 0.300 0.375 30
nested_clauses 0.500 0.348 0.410 46
nominalization 0.288 0.304 0.296 56
objectifying_stance 0.267 0.400 0.320 10
parallelism 0.350 0.259 0.298 27
phatic_check 0.500 0.364 0.421 11
phatic_filler 0.333 0.800 0.471 10
polysyndeton 1.000 0.792 0.884 24
probability 0.500 0.455 0.476 22
proverb 0.000 0.000 0.000 10
qualified_assertion 0.250 0.241 0.246 29
refrain 0.944 0.708 0.810 24
relative_chain 0.350 0.509 0.415 55
religious_formula 0.857 0.750 0.800 16
rhetorical_question 0.688 0.762 0.723 84
rhyme 0.231 0.300 0.261 10
rhythm 0.909 0.625 0.741 16
second_person 0.571 0.586 0.579 116
self_correction 0.821 0.575 0.676 40
sensory_detail 0.364 0.200 0.258 20
simple_conjunction 0.167 0.300 0.214 10
specific_place 0.400 0.222 0.286 18
technical_abbreviation 0.900 0.321 0.474 28
technical_term 0.426 0.703 0.531 74
temporal_anchor 0.396 0.618 0.483 34
temporal_embedding 0.500 0.562 0.529 48
third_person_reference 0.700 0.700 0.700 10
tricolon 0.611 0.611 0.611 18
us_them 0.733 0.611 0.667 18
vocative 0.462 0.600 0.522 20
accuracy 0.500 2357
macro avg 0.535 0.484 0.493 2357
weighted avg 0.532 0.500 0.503 2357
```
</details>
**Top performing subtypes (F1 ≥ 0.75):** `assonance` (0.980), `polysyndeton` (0.884), `antithesis` (0.878), `concessive_connector` (0.857), `intensifier_doubling` (0.850), `epistrophe` (0.848), `audience_response` (0.889), `institutional_subject` (0.811), `refrain` (0.810), `agent_demoted` (0.800), `religious_formula` (0.800), `conflict_frame` (0.733), `rhythm` (0.741), `rhetorical_question` (0.723).
**Weakest subtypes (F1 < 0.20):** `proverb` (0.000), `conceptual_metaphor` (0.057), `metadiscourse` (0.116), `categorical_statement` (0.136), `epistemic_hedge` (0.159). These tend to be semantically diffuse classes that overlap heavily with neighbouring subtypes or have very low test support.
## Class Distribution
The training set exhibits significant imbalance across 71 classes:
| Support Range | Example Classes | Count |
|---------------|-----------------|-------|
| >1000 | `discourse_formula`, `second_person` | 2 |
| 500–1000 | `conditional`, `rhetorical_question`, `technical_term`, `imperative` | 8 |
| 200–500 | `abstract_noun`, `contrastive`, `inclusive_we`, `nominalization` | 27 |
| 100–200 | `alliteration`, `antithesis`, `asyndeton`, `epistrophe`, `refrain` | 30 |
| <100 | `footnote_reference`, `phatic_check`, `technical_abbreviation` | 4 |
## Limitations
- **71-way classification on ~22k spans**: The data budget per class is thin, particularly for classes near the minimum. More data or class consolidation would help.
- **Semantic overlap**: Some subtypes are difficult to distinguish from surface text alone (e.g., `parallelism` vs `anaphora` vs `tricolon`; `epistemic_hedge` vs `qualified_assertion` vs `probability`). The model may benefit from hierarchical classification that conditions on type-level predictions.
- **Recall-precision tradeoff on rare classes**: Many rare classes show high precision but lower recall (e.g., `self_correction`: P=0.821, R=0.575; `technical_abbreviation`: P=0.900, R=0.321), suggesting the model learns narrow prototypes but misses variation.
- **Span-level only**: Requires pre-extracted spans. Does not detect boundaries.
- **128-token context window**: Longer spans are truncated.
## Theoretical Background
The 71 subtypes represent the full granularity of the Havelock taxonomy, operationalizing Ong's oral–literate framework into specific, annotatable rhetorical devices. Oral subtypes capture the textural signatures of spoken and performative discourse: repetitive structures (`anaphora`, `epistrophe`, `tricolon`), sound patterning (`alliteration`, `assonance`, `rhythm`), direct audience engagement (`vocative`, `imperative`, `rhetorical_question`), and formulas (`proverb`, `epithet`, `discourse_formula`). Literate subtypes capture the apparatus of analytic prose: complex syntax (`nested_clauses`, `relative_chain`, `conditional`), epistemic positioning (`epistemic_hedge`, `evidential`, `probability`), impersonal voice (`agentless_passive`, `institutional_subject`), and scholarly machinery (`citation`, `footnote_reference`, `metadiscourse`).
## Related Models
| Model | Task | Classes | F1 |
|-------|------|---------|-----|
| [`HavelockAI/bert-marker-category`](https://huggingface.co/HavelockAI/bert-marker-category) | Binary (oral/literate) | 2 | 0.875 |
| [`HavelockAI/bert-marker-type`](https://huggingface.co/HavelockAI/bert-marker-type) | Functional type | 18 | 0.583 |
| **This model** | Fine-grained subtype | 71 | 0.493 |
| [`HavelockAI/bert-orality-regressor`](https://huggingface.co/HavelockAI/bert-orality-regressor) | Document-level score | Regression | MAE 0.079 |
| [`HavelockAI/bert-token-classifier`](https://huggingface.co/HavelockAI/bert-token-classifier) | Span detection (BIO) | 145 | 0.500 |
## Citation
```bibtex
@misc{havelock2026subtype,
title={Havelock Marker Subtype Classifier},
author={Havelock AI},
year={2026},
url={https://huggingface.co/HavelockAI/bert-marker-subtype}
}
```
## References
- Ong, Walter J. *Orality and Literacy: The Technologizing of the Word*. Routledge, 1982.
- Lee, C. et al. "Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models." ICLR 2020.
- Warner, A. et al. "Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference." 2024.
---
*Trained: February 2026* |