README.md

---
language: en
license: mit
tags:
  - text-classification
  - roberta
  - sludge
  - administrative-burden
  - consumer-complaints
base_model: roberta-base
---

# sludge-informational-roberta

Fine-tuned [RoBERTa-base](https://huggingface.co/roberta-base) classifier for detecting **informational sludge** (barriers to knowing — inadequate or obscured information) in consumer financial complaint narratives.

Developed and validated in (manuscript in preparation):

> Chesterfield, A., Gillespie, A., Goddard, A. and Krpan, D. (2026). *Feeling the Friction: Developing and validating text classifiers for sludge in consumer complaints.*

Code and data: [GitHub](https://github.com/AlexChesterfield/sludge-classifiers) | Companion model: [sludge-process-roberta](https://huggingface.co/AlexChesterfield/sludge-process-roberta)

## What this model detects

**Informational sludge** = barriers to knowing: inadequate, obscured, or confusing information provision, including organisational opacity where companies cannot or will not explain their decisions. Binary classification: 1 = informational sludge present, 0 = absent.

## Training details

| Parameter | Value |
|-----------|-------|
| Base model | roberta-base |
| Training samples | 825 (85% stratified split of n=971) |
| Test samples | 146 (15% held-out, evaluated once) |
| Epochs | 4 |
| Learning rate | 2e-5 |
| Batch size | 8 |
| Warmup ratio | 0.1 |
| Class weighting | Balanced |
| Random seed | 42 |

## Performance (held-out test set, n=146)

| Metric | Score |
|--------|-------|
| MCC | 0.621 |
| F1 | 0.667 |
| Precision | 0.778 |
| Recall | 0.583 |
| Accuracy | 0.904 |

## How to use

```python
from transformers import pipeline

classifier = pipeline("text-classification", model="AlexChesterfield/sludge-informational-roberta")
complaint = "No one could explain why my claim was denied or what I needed to do next."
result = classifier(complaint)
# LABEL_1 = sludge present, LABEL_0 = absent
```

## Citation

```bibtex
@article{chesterfield2026sludge,
  title={Feeling the Friction: Developing and validating text classifiers for sludge in consumer complaints},
  author={Chesterfield, Alexandra and Gillespie, Alex and Goddard, Alex and Krpan, Dario},
  year={2026}
}
```