Add dataset card

README.md

@@ -0,0 +1,171 @@
+---
+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.