---
language: en
tags:
- disfluency-detection
- token-classification
- modernbert
- speech-pathology
- baseline
---

# ModernBERT-base Disfluency Detection — Real Data Baseline

Fine-tuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
using **only real data** from FluencyBank Timestamped (Romana et al., 2024).

## Purpose
This model serves as the **Experiment A baseline** in an ablation study comparing:
- This model: trained on real data only (2,744 train examples)
- Mixed model: trained on 80% synthetic + 20% real (13,713 train examples)

The comparison quantifies the contribution of the synthetic data augmentation pipeline.

## Dataset
FluencyBank Timestamped — 3,430 segments from 37 adults who stutter.
Split: 80/10/10 train/val/test (random_state=42).
No synthetic data used.

## Label Priority
FP > PW > RP > RV (corrected from original FP > RP > RV > PW)
This allows ~2,048 real PW tokens to be correctly labeled.

## Test Results
- Overall Accuracy : 0.9588
- Overall F1 (macro): 0.8312
- FP F1: 0.0000
- RP F1: 0.0000
- RV F1: 0.0000
- PW F1: 0.0000