README.md

---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- accessibility
- wcag
- text-classification
- distilbert
size_categories:
- n<1K
---

# WCAG Accessibility Issues

A small synthetic dataset for experimenting with machine learning classification of accessibility defects against WCAG success criteria.

This project was created as a learning exercise to explore the complete Hugging Face text-classification workflow, including dataset preparation, tokenisation, fine-tuning a pretrained Transformer model, evaluation and inference.

## Dataset

The dataset contains **100 synthetic accessibility defect descriptions** across five WCAG success criteria.

Each class contains 20 examples.

| Label | WCAG success criterion |
|---|---|
| 0 | 1.3.1 Info and Relationships |
| 1 | 2.1.1 Keyboard |
| 2 | 2.4.3 Focus Order |
| 3 | 2.4.7 Focus Visible |
| 4 | 4.1.2 Name, Role, Value |

Example:

```text
Custom radio control does not expose its selected state
```

Label:

```text
4.1.2 Name, Role, Value
```

All examples are synthetic and were created specifically for this project. The dataset does not contain real audit findings or customer data.

## Dataset Splits

The 100 examples are divided using a stratified split:

| Split | Examples | Examples per class |
|---|---:|---:|
| Training | 80 | 16 |
| Validation | 10 | 2 |
| Test | 10 | 2 |

The original complete dataset is available in `data.csv`.

`split_data.py` can be used to reproduce the train, validation and test datasets.

## Model Training

The project uses `distilbert-base-uncased` as the pretrained base model.

DistilBERT is fine-tuned for multiclass sequence classification with five output classes corresponding to the five WCAG success criteria.

The training configuration currently uses:

- 5 epochs
- Batch size of 8
- Learning rate of 2e-5
- Validation after each epoch
- Weighted F1 as the model-selection metric

The training process is implemented in `train_model.py`.

## Initial Results

The initial model achieved:

| Metric | Result |
|---|---:|
| Test accuracy | 80% |
| Weighted precision | 0.867 |
| Weighted recall | 0.800 |
| Weighted F1 | 0.787 |

These results are based on a test set containing only **10 examples** and should not be interpreted as evidence that the model will achieve 80% accuracy on real-world accessibility defects.

The results demonstrate that the end-to-end classification pipeline is working.

## Example Predictions

After fine-tuning, the model correctly classified previously unseen examples such as:

```text
no focus indicator
```

as:

```text
2.4.7 Focus Visible
```

and:

```text
can't open menu with keyboard
```

as:

```text
2.1.1 Keyboard
```

## Repository Files

- `data.csv` — complete synthetic dataset
- `train.csv` — training split
- `validation.csv` — validation split
- `test.csv` — held-out test split
- `split_data.py` — creates the stratified dataset splits
- `load_dataset.py` — loads the CSV files using Hugging Face Datasets
- `tokenize_dataset.py` — tokenises examples and encodes labels
- `load_model.py` — loads DistilBERT for sequence classification
- `train_model.py` — fine-tunes and evaluates the classifier
- `predict.py` — performs inference using the trained model

## Limitations

This is a small educational dataset rather than a production accessibility dataset.

In particular:

- Only five WCAG success criteria are represented.
- The examples are synthetic.
- There are only 20 examples per class.
- The test set contains only 10 examples.
- Real accessibility defects may be substantially more complex or ambiguous.
- Some accessibility defects can relate to more than one WCAG success criterion.

The classifier is a closed-set classifier. It must select one of its five known classes even when an issue actually belongs to a WCAG criterion that is not represented in the dataset.

For example, an issue concerning missing alternative text would normally relate to **1.1.1 Non-text Content**, but the current model has no 1.1.1 class available.

## Purpose

The purpose of this project is to learn and demonstrate an end-to-end Hugging Face machine learning workflow:

```text
Dataset
   ↓
Train / Validation / Test Split
   ↓
Tokenisation
   ↓
Pretrained DistilBERT
   ↓
Fine-tuning
   ↓
Evaluation
   ↓
Inference
```

The project can be extended by adding more examples, introducing additional WCAG success criteria and evaluating the resulting classifier against a larger and more representative test dataset.