| --- |
| library_name: transformers |
| tags: |
| - text-classification |
| - multi-label-classification |
| - toxicity |
| - content-moderation |
| - hinglish |
| - code-mixed |
| - indic-nlp |
| language: |
| - hi |
| - en |
| base_model: l3cube-pune/hing-roberta |
| license: mit |
| pipeline_tag: text-classification |
| metrics: |
| - f1 |
| - precision |
| - recall |
| - loss |
| --- |
| |
| # Hinglish Toxicity Classifier (`darelphilip/hinglish-toxicity-classifier`) |
|
|
| A fine-tuned multi-label text classification model engineered to detect toxicity, profanity, harassment, and identity-targeted hate speech in Romanized Hindi-English (Hinglish) code-mixed text. |
|
|
| > 🚀 **Live Interactive Demo:** Test this model in real-time on [Hugging Face Spaces](https://huggingface.co/spaces/darelphilip/hinglish_toxicity). |
|
|
| --- |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| This model is fine-tuned from [`l3cube-pune/hing-roberta`](https://huggingface.co/l3cube-pune/hing-roberta) across **116,000+ Romanized Hinglish conversational comments**. It is built for multi-label classification using a weighted Binary Cross-Entropy loss function (`pos_weight`) to counter class imbalance between high-frequency casual profanity and low-frequency targeted hate speech. |
|
|
| - **Developed by:** Darel Philip (`darelphilip`) |
| - **Contact / Author Email:** [enigmaticdarel@gmail.com](mailto:enigmaticdarel@gmail.com) |
| - **Model Type:** Transformer-based Multi-Label Sequence Classification (`XLMRobertaForSequenceClassification`) |
| - **Language(s) (NLP):** Hinglish (Romanized Hindi-English code-mixed), English (`en`), Hindi (`hi`) |
| - **License:** MIT |
| - **Finetuned from Model:** [`l3cube-pune/hing-roberta`](https://huggingface.co/l3cube-pune/hing-roberta) |
| - **Model Serialization:** Safetensors (`model.safetensors`, ~1.11 GB) |
|
|
| ### Model Sources & Links |
|
|
| - **Model Repository:** [`darelphilip/hinglish-toxicity-classifier`](https://huggingface.co/darelphilip/hinglish-toxicity-classifier) |
| - **Live Interactive Space:** [`darelphilip/hinglish_toxicity`](https://huggingface.co/spaces/darelphilip/hinglish_toxicity) |
| - **Base Architecture:** XLM-RoBERTa (Trained on L3Cube-HingCorpus) |
|
|
| --- |
|
|
| ## Uses |
|
|
| ### Direct Use |
|
|
| - **Automated Content Moderation:** Scanning forum posts, comment feeds, and social platforms for Romanized Hinglish abuse. |
| - **Toxicity Auditing:** Batch-processing historical comment archives to measure community health and identify moderation trends. |
| - **Community Bot Filters:** Serving as a backend decision engine for custom moderation bots and automated rule queues. |
|
|
| ### Downstream Use & Hybrid Pipelines |
|
|
| Because encoder models evaluate all tokens simultaneously, strong positive words (*mast*, *accha*, *pyaar*) can sometimes mask targeted slurs in sarcastic sentences. In production, this model is designed to work within a **two-stage hybrid architecture**: |
|
|
| 1. **Stage 1 (Deterministic Fast Path):** A strict Regex/keyword dictionary immediately catches zero-tolerance identity slurs. |
| 2. **Stage 2 (Statistical Context Path):** This transformer model classifies contextual toxicity, mild abuse, and implicit harassment. |
| 3. **Stage 3 (Escalation / Review):** Ambiguous borderline cases can be escalated to human moderators or routed to a larger reasoning LLM. |
|
|
| ### Out-of-Scope Use |
|
|
| - Autonomous execution of permanent account bans or legal enforcement without human review. |
| - Monolingual Devanagari Hindi or standard formal English (optimized specifically for Roman/Latin script code-mixed text). |
| - Complex multi-sentence narrative satire where toxicity relies exclusively on long-range external world context. |
|
|
| --- |
|
|
| ## Bias, Risks, and Limitations |
|
|
| - **Sarcasm & Positive Token Masking:** Sarcastic or passive-aggressive insults wrapped in affectionate language may produce lower probability scores. |
| - **Spelling & Phonetic Variations:** Romanized Hinglish lacks standardized spelling (e.g., *pyaar* vs. *pyar*, *bhai* vs. *bhaii*). Extreme misspellings can affect subword tokenization. |
| - **Class Frequency Skew:** Severe identity-targeted categories have significantly fewer positive training instances than casual slang, requiring custom per-class thresholding. |
|
|
| ### Recommended Threshold Strategy |
|
|
| Do not rely on a static `0.5` sigmoid threshold across all classes. For production moderation: |
| - **Casual Profanity / Insults:** `threshold = 0.50 - 0.60` (balances precision). |
| - **Severe Identity Slurs / Targeted Hate:** `threshold = 0.20 - 0.35` (maximizes recall). |
|
|
| --- |
|
|
| ## How to Get Started with the Model |
|
|
| ### Multi-Label Batch Inference (PyTorch) |
|
|
| ```python |
| import torch |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| |
| MODEL_ID = "darelphilip/hinglish-toxicity-classifier" |
| |
| # Load tokenizer and model |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
| model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID) |
| model.eval() |
| |
| # Move to GPU if available |
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| model.to(device) |
| |
| id2label = model.config.id2label |
| |
| # Input Hinglish texts |
| texts = [ |
| "Bhai tu pagal hai kya, yeh kya bakwas hai?", |
| "Have a wonderful day everyone!", |
| "Chup kar bilkul bakwaas mat kar yahan" |
| ] |
| |
| inputs = tokenizer( |
| texts, |
| padding=True, |
| truncation=True, |
| max_length=128, |
| return_tensors="pt" |
| ) |
| inputs = {k: v.to(device) for k, v in inputs.items()} |
| |
| with torch.no_grad(): |
| logits = model(**inputs).logits |
| probabilities = torch.sigmoid(logits).cpu() |
| |
| # Evaluate against a custom threshold |
| threshold = 0.50 |
| predictions = (probabilities > threshold).int() |
| |
| for text, probs, preds in zip(texts, probabilities, predictions): |
| print(f"\n📝 Sentence: \"{text}\"") |
| for idx, (prob, pred) in enumerate(zip(probs, preds)): |
| label = id2label[idx] |
| status = "🚨 FLAGGED" if pred == 1 else "✅ CLEAN" |
| print(f" {label:<25} -> {status} (prob: {prob:.4f})") |
| ``` |
|
|
| ### High-Level Pipeline Usage |
|
|
| ```python |
| from transformers import pipeline |
| |
| classifier = pipeline( |
| "text-classification", |
| model="darelphilip/hinglish-toxicity-classifier", |
| top_k=None |
| ) |
| |
| results = classifier("Bhai tu kitna bekaar insaan hai") |
| print(results) |
| ``` |
|
|
| --- |
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| - **Dataset Size:** 116,000+ labeled rows. |
| - **Domain:** Romanized Hinglish conversational comments, code-mixed social media posts, and forum discussions. |
| - **Labels:** Multi-label schema covering general toxicity, profanity, identity attack, and harassment. |
|
|
| ### Training Procedure |
|
|
| - **Loss Function:** `BCEWithLogitsLoss` using positive class weighting vectors (`pos_weight`) to penalize false negatives on rare, severe categories. |
| - **Early Stopping:** Triggered via `EarlyStoppingCallback(patience=1)` monitoring validation Macro F1 (`load_best_model_at_end=True`). Best weights automatically restored from Epoch 1. |
|
|
| #### Training Hyperparameters |
|
|
| - **Base Architecture:** `l3cube-pune/hing-roberta` (278M parameters) |
| - **Sequence Length:** 128 tokens |
| - **Optimizer:** AdamW |
| - **Epochs Completed:** 2 (Early stopped at best checkpoint: Epoch 1) |
| - **Batch Size:** Dynamic gradient accumulation configuration |
|
|
| --- |
|
|
| ## Evaluation |
|
|
| ### Validation Set Results |
|
|
| | Metric | Epoch 1 (Best Restored Checkpoint) | Epoch 2 | |
| | :--- | :--- | :--- | |
| | **Validation Loss** | **0.944581** | 0.769777 | |
| | **Macro F1** | **0.556591** | 0.538771 | |
| | **Micro F1** | **0.654647** | 0.637569 | |
| | **Precision** | **0.525735** | 0.440160 | |
| | **Recall** | **0.621775** | 0.739079 | |
|
|
| --- |
|
|
| ## Technical Specifications |
|
|
| ### Compute Infrastructure |
|
|
| - **Training Hardware:** NVIDIA Tesla T4 GPU (Google Colab) |
| - **System Memory:** ~6.9 GB / 12.7 GB |
| - **VRAM Utilization:** ~5.9 GB / 15.0 GB |
| - **Software Stack:** PyTorch, Hugging Face Transformers, Hugging Face Datasets, Accelerate, Gradio |
|
|
| --- |
|
|
| ## Contact & Community |
|
|
| - **Author / Maintainer:** Darel Philip |
| - **Email:** [enigmaticdarel@gmail.com](mailto:enigmaticdarel@gmail.com) |
| - **Hugging Face Profile:** [@darelphilip](https://huggingface.co/darelphilip) |
| - **Model Card Issues / PRs:** [darelphilip/hinglish-toxicity-classifier/discussions](https://huggingface.co/darelphilip/hinglish-toxicity-classifier/discussions) |
| - **Web App:** [darelphilip/hinglish_toxicity](https://huggingface.co/spaces/darelphilip/hinglish_toxicity) |
|
|
