rakshak-severity-v2

🛡️ rakshak-severity-v2

Severity classifier for Nepali social media content, fine-tuned from XLM-RoBERTa Large using LoRA/PEFT. Part of the RakshakAI content moderation system.

📄 Paper: RakshakAI: Multi-Label Toxicity Detection for Low-Resource Nepali Social Media Content

What it does

Given a piece of Nepali text along with detected toxicity labels from rakshak-xlmr-large-v2, this model predicts one of three severity classes:

How it differs from a standard classifier

Rather than predicting severity from text alone, the input is enriched by appending detected toxicity labels from the multi-label classifier to the original text — for example: yo jaat ko manchhe haru lai nepal bata nikala [DETECTED: hate_speech, casteism]

This allows the model to leverage category information when estimating severity, making predictions more accurate and context-aware.

Performance

The model performs strongly on normal and severe classes but struggles with moderate severity due to class imbalance — only 9.3% of training samples carry a moderate label. This is a known limitation we plan to address with more data collection.

How to use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "biraj-bhusal/rakshak-severity-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

id2label = {0: "normal", 1: "moderate", 2: "severe"}

# Append detected labels from rakshak-xlmr-large-v2 to the text
text = "your nepali text here [DETECTED: hate_speech, casteism]"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)

with torch.no_grad():
    logits = model(**inputs).logits
    prediction = torch.argmax(logits, dim=-1).item()

print(id2label[prediction])

Training Details

• Base model: xlm-roberta-large
• Fine-tuning method: LoRA (PEFT) — rank r=16, α=32, dropout=0.1
• Training data: 4,716 augmented samples
• Loss: Class-weighted cross-entropy to address class imbalance
• Precision: fp16 mixed precision
• Hardware: Dual NVIDIA T4 GPUs on Kaggle

Training Data

• Augmented Training Dataset (4,716 samples)
• Curated Dataset (1,574 samples)

Limitations

• Moderate severity class performs poorly (F1=0.26) due to underrepresentation in training data
• Depends on output from rakshak-xlmr-large-v2 for best results
• Single annotator labeling may introduce individual bias
• May not generalize well to content outside Facebook and YouTube domains

Related Models

• rakshak-xlmr-large-v2 — multi-label toxicity classifier whose output feeds into this model

Citation

@misc{bhusal2025rakshak,
  author = {Bhusal, Biraj},
  title = {RakshakAI: Multi-Label Toxicity Detection for Low-Resource Nepali Social Media Content},
  year = {2025},
  publisher = {Zenodo},
  url = {https://zenodo.org/records/20850923}
}

Content Warning

This model is trained on toxic social media content. The author does not endorse any views expressed in the training data.

Developed by: Biraj Bhusal