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mopatik/
PuoBERTa-offensive-detection-v1

Name: PuoBERTa-offensive-detection-v1
Availability: InStock

mopatik/setswana-offensive-977

dsfsi/PuoBERTa

finetune

apache-2.0

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Model Card for PuoBERTa Offensive Language Detection (Setswana)

Model Summary

A fine-tuned transformer model based on PuoBERTa for binary classification of Setswana text into:

Offensive (1)
Non-offensive (0)

The model is intended for digital forensic investigations and cybercrime analysis involving Setswana-language social media text.

Intended Use

Digital forensics
Cyberbullying detection
Hate speech detection
Research on low-resource African languages

Not intended for:

Fully automated decision-making
Legal action without human review

Training Data

977 entries total
- 477 Offensive
- 500 Non-offensive
Sourced from public Facebook posts and comments
Annotated with semantic trigger tags (removed at test-time)

Training Procedure

Model: roberta-base architecture (PuoBERTa variant)
Epochs: 10 (final model)
Batch size: 16 (train), 64 (eval)
Learning rate: 1e-5
Weight decay: 0.01
Loss: class-weighted cross-entropy [1.0, 2.0]

Evaluation Results

The model was evaluated on a held-out 20% test set that was kept completely unseen during training and cross-validation.
The following metrics were computed:

Test Set Performance

Metric	Value
Accuracy	0.8673
Macro F1-score	0.8662
Recall (Offensive = 1)	0.8444
Matthews Correlation Coefficient (MCC)	0.7326
ROC-AUC	0.9288
Loss	0.3381
Runtime (seconds)	0.5897
Samples per second	332.398
Steps per second	6.784

Interpretation

The ROC-AUC of 0.9288 indicates strong separation ability between offensive and non-offensive classes.
The MCC of 0.7326 shows robust classification performance even with slight class imbalance.
A recall of 0.8444 on the offensive class means the model is effective at capturing harmful or abusive language—important for forensic use cases.
The macro F1-score of 0.8662 confirms balanced performance across both classes.

Overall, the model demonstrates high discriminative power and reliable generalisation for Setswana offensive-language detection.

Limitations

Dataset is relatively small
Sensitive to spelling variations
May misclassify subtle forms of metaphorical or humorous abuse

Ethical Considerations

Contains sensitive language
Use in investigations must comply with local laws (Botswana Cybercrime Act, DPA)

How to Use

python

from transformers import RobertaTokenizer, RobertaForSequenceClassification

tokenizer = RobertaTokenizer.from_pretrained("mopatik/PuoBERTa-offensive-detection-v1")
model = RobertaForSequenceClassification.from_pretrained("mopatik/PuoBERTa-offensive-detection-v1")

# Ensure model is in evaluation mode
model.eval()

# Sample text (replace with your actual text)
#sample_text = "o seso tota"  # Example Setswana text
sample_text = "modimo a le segofatse"  # Example Setswana text

# Tokenize and prepare input
inputs = tokenizer(
    sample_text,
    padding='max_length',
    truncation=True,
    max_length=128,
    return_tensors="pt"
)

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=1)
    predicted_class = torch.argmax(probs).item()

# Get class label and confidence
class_names = ["Non-offensive", "Offensive"]
confidence = probs[0][predicted_class].item()

print(f"Text: {sample_text}")
print(f"Predicted class: {class_names[predicted_class]} (confidence: {confidence:.2%})")
print(f"Class probabilities: {dict(zip(class_names, [f'{p:.2%}' for p in probs[0].tolist()]))}")