| --- |
| language: |
| - en |
| license: mit |
| tags: |
| - hate-speech-detection |
| - severity-prediction |
| - text-classification |
| - bert |
| - explainable-ai |
| datasets: |
| - Hate-speech-CNERG/hatexplain |
| metrics: |
| - accuracy |
| - f1 |
| --- |
| |
| # Hate Speech Severity Predictor — BERT |
|
|
| ## Model Description |
|
|
| This is a fine-tuned BERT model (bert-base-uncased) for hate speech severity prediction, |
| developed as part of an MSc research project at the University of Moratuwa, Sri Lanka. |
|
|
| The model predicts hate speech severity across three levels: |
| - Level 0 — Non-hate Speech |
| - Level 1 — Mild / Offensive |
| - Level 2 — Severe Hate Speech |
|
|
| It also produces a continuous severity score S in [0,1]: |
| S = 0.0 x P(Level 0) + 0.5 x P(Level 1) + 1.0 x P(Level 2) |
|
|
| ## Model Details |
|
|
| - Developed by: J.A.U.S. Jayakody (239817M), University of Moratuwa |
| - Supervised by: Dr. Supunmali Ahangama |
| - Base model: bert-base-uncased |
| - Language: English |
| - License: MIT |
|
|
| ## Dataset |
|
|
| Fine-tuned on HateXplain (Mathew et al., 2021): |
| - 20,148 posts from Twitter and Gab |
| - Stratified 70-15-15 train-validation-test split |
|
|
| ## Training Details |
|
|
| - Epochs: 3 (best checkpoint: Epoch 2) |
| - Batch size: 16 |
| - Learning rate: 2e-5 |
| - Max sequence length: 128 tokens |
| - Class weighting: Balanced |
| - Hardware: Tesla T4 GPU |
|
|
| ## Evaluation Results |
|
|
| | Metric | SVM | BERT | |
| |--------|-----|------| |
| | Accuracy | 0.629 | 0.684 | |
| | Macro F1 | 0.615 | 0.679 | |
|
|
| Severity Prediction Metrics: |
| - Spearman Correlation: 0.714 |
| - Pearson Correlation: 0.720 |
| - Mean Absolute Error: 0.212 |
| - RMSE: 0.292 |
|
|
| ## How to Use |
|
|
| ```python |
| from transformers import BertForSequenceClassification, BertTokenizer |
| import torch |
| import torch.nn.functional as F |
| import numpy as np |
| |
| tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') |
| model = BertForSequenceClassification.from_pretrained('UdaniSJ/hate-speech-severity-bert') |
| model.eval() |
| |
| def predict_severity(text): |
| inputs = tokenizer(text, return_tensors='pt', |
| truncation=True, max_length=128) |
| with torch.no_grad(): |
| outputs = model(**inputs) |
| probs = F.softmax(outputs.logits, dim=1).numpy()[0] |
| score = 0.0*probs[0] + 0.5*probs[1] + 1.0*probs[2] |
| level = int(np.argmax(probs)) |
| names = {0:'Non-hate', 1:'Mild', 2:'Severe'} |
| return {'level': names[level], 'score': round(float(score),3)} |
| |
| print(predict_severity("I love all people regardless of background")) |
| ``` |
|
|
| ## Live Demo |
|
|
| https://huggingface.co/spaces/UdaniSJ/hate-speech-severity-predictor |
|
|
| ## Limitations |
|
|
| - Trained on English social media content only |
| - May exhibit lexical over-reliance on identity terms |
| - Context-aware adjustment partially mitigates reclaimed language misclassification |
|
|
| ## References |
|
|
| - Mathew et al. (2021). HateXplain. AAAI 2021. |
| - Devlin et al. (2019). BERT. NAACL 2019. |
| - Ribeiro et al. (2016). LIME. KDD 2016. |
| - Lundberg and Lee (2017). SHAP. NeurIPS 2017. |
|
|
