sourabh5500/hate-speech-muril

SafeShield AI — Multilingual Hate Speech Detection

Model Description

SafeShield AI is a fine-tuned MuRIL (Multilingual Representations for Indian Languages) model for detecting hate speech and offensive content in Hinglish (Hindi-English code-mixed) text.

Most hate speech classifiers are trained on monolingual English, but a significant volume of online abuse in India occurs in Hinglish — which standard tokenizers and models handle poorly. This model addresses that gap.

Model Architecture

Input Text (Hinglish/English)

↓

MuRIL Tokenizer (vocab: 197k tokens, covers Devanagari + Latin)

↓

MuRIL-base-cased (12 layers, 768 hidden, 12 heads, 237M params)

↓

[CLS] token embedding (768-dim)

↓

Dropout (0.1) + Linear(768 → 2)

↓

Softmax → [P(NOT), P(HOF)]

Labels

Label	ID	Meaning
NOT	0	Non-hate, non-offensive content
HOF	1	Hate speech or Offensive content

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Training Details

Dataset

• Source: Code-Mixed Hinglish Hate Speech Detection Dataset
• Size: 29,533 samples (after cleaning)
• Split: 70% train / 10% val / 20% test
• Class balance: NOT=53.7% / HOF=46.3% (nearly balanced, 1.16x ratio)
• Languages: Hinglish, English, mixed

Preprocessing Pipeline

• URL removal
• @mention removal
• Hashtag normalization (#BJP → BJP)
• Emoji demojization (😂 → :face_with_tears_of_joy:)
• Repeated character normalization (sooo → soo)
• Repeated punctuation normalization (!!! → !)
• Lowercasing

Hyperparameters

Parameter	Value
Base model	google/muril-base-cased
Max sequence length	128
Batch size	32
Learning rate	2e-5
Epochs	4 (early stop at 3)
Warmup ratio	0.1
Weight decay	0.01
Optimizer	AdamW
Scheduler	Linear warmup + decay
Loss	CrossEntropyLoss (balanced weights)

Training Infrastructure

• Platform: Kaggle (free tier)
• GPU: Tesla T4 x2
• Training time: ~25 minutes

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Evaluation Results

Test Set Performance (5,907 samples)

Metric	NOT	HOF	Macro Avg
Precision	0.7351	0.7444	0.7397
Recall	0.8015	0.6668	0.7342
F1-Score	0.7669	0.6974	0.7352
Support	3,164	2,743	5,907

Overall Accuracy: 73.90%

Comparison with Baseline

Model	Macro F1	Notes
TF-IDF + Logistic Regression	0.7138	Baseline
MuRIL Fine-tuned (ours)	0.7352	+3.0% relative gain

Confusion Matrix

Predicted NOT HOF Actual NOT [ 2513 651 ] HOF [ 926 1817 ]

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Error Analysis & Known Limitations

Error Breakdown

Type	Count	Rate	Description
True Positive	1,817	-	Correctly caught hate
True Negative	2,513	-	Correctly passed clean
False Negative	926	33.76%	Missed hate speech ← most dangerous
False Positive	651	20.58%	Over-flagged clean content

Known Failure Modes

1. Implicit/Subtle Hate Speech The model struggles with hate speech that doesn't use explicit slurs or offensive vocabulary — sarcasm, dog whistles, and context-dependent toxicity are frequently missed.

2. Very Short Texts Texts under 50 characters have a 41.8% false positive rate. With minimal context, the model over-flags based on surface patterns.

3. Non-Hinglish Languages Croatian, Serbian, and other non-Hinglish texts are sometimes misclassified as hate speech despite MuRIL's multilingual training.

4. Medium-Length Hate Speech (101-150 chars) The worst false negative rate (48.7%) occurs in the 101-150 character range — nearly half of hate speech in this length is missed.

5. Confidence Calibration

• Below 0.60 confidence: only 56-66% accurate (borderline territory)
• Above 0.65 confidence: 87-96% accurate (reliable zone)

Recommended Production Thresholds

if confidence >= 0.65:
    # Auto-classify — reliable (87-96% accurate)
elif 0.60 <= confidence < 0.65:
    # Auto-classify with logging
else:  # confidence < 0.60
    # Route to human review queue

---

How to Use

Direct Inference

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model
tokenizer = AutoTokenizer.from_pretrained("sourabh5500/hate-speech-muril")
model = AutoModelForSequenceClassification.from_pretrained(
    "sourabh5500/hate-speech-muril"
)
model.eval()

def predict(text: str) -> dict:
    inputs = tokenizer(
        text,
        return_tensors="pt",
        max_length=128,
        truncation=True,
        padding=True
    )
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.softmax(outputs.logits, dim=1)[0]

    label_id = probs.argmax().item()
    return {
        "label": model.config.id2label[label_id],
        "confidence": probs[label_id].item(),
        "scores": {
            "NOT": probs[0].item(),
            "HOF": probs[1].item()
        }
    }

# Test
print(predict("yaar tu bahut bura insaan hai"))
print(predict("aaj ka din bahut acha tha"))

Using the API

curl -X POST "https://your-api-url/predict" \
     -H "Content-Type: application/json" \
     -d '{"text": "yaar tu bahut bura insaan hai"}'

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Bias & Fairness Considerations

1. Language bias: Trained primarily on Hinglish/English. Performance degrades on pure Hindi (Devanagari script) or other Indian languages.
2. Topic bias: Dataset sourced from Twitter/social media — may not generalize to other platforms (WhatsApp, YouTube comments).
3. Annotator bias: Human annotations inherently reflect annotator demographics and perspectives. Hate speech labeling is subjective.
4. Recency bias: Training data has a cutoff date. New slang and emerging hate speech patterns won't be captured.
5. Class bias: Despite near-balanced dataset (1.16x), HOF recall (0.667) is lower than NOT recall (0.802).

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Intended Use

✅ Appropriate uses:

• Content moderation assistance (human-in-the-loop)
• Research on Hinglish hate speech
• First-pass filtering with human review for borderline cases
• Academic study of multilingual toxicity detection

❌ Inappropriate uses:

• Fully automated moderation without human oversight
• Legal or punitive decisions based solely on model output
• Languages other than Hinglish/English
• Real-time high-stakes moderation without threshold tuning

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Project Structure

SafeShield AI/

├── src/

│ ├── data/

│ │ ├── preprocessor.py

│ │ └── loader.py

│ └── models/

│ └── baseline.py

├── notebooks/

│ ├── 01_eda.ipynb

│ ├── 02_preprocessing.ipynb

│ ├── 03_baseline.ipynb

│ ├── 04_muril_finetune.ipynb

│ ├── 05_muril_improved.ipynb

│ └── 06_error_analysis.ipynb

├── api/

├── app/

├── results/

└── MODEL_CARD.md

Citation

@misc{saxena2025safeshield,
  author = {Sourabh Saxena},
  title = {SafeShield AI: Multilingual Hate Speech Detection for Hinglish Text},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/sourabh5500/hate-speech-muril}
}

---

Author

Sourabh Saxena B.Tech CSE (AI/ML) — IMS Engineering College GitHub: sourabh-550 HuggingFace: sourabh5500

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This model is intended as a research tool. Always use human oversight for content moderation decisions. The author is not responsible for misuse of this model.