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----
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+---
+language: en
+license: apache-2.0
+tags:
+- toxicity-detection
+- text-classification
+- dendritic-optimization
+- perforated-backpropagation
+- bert-tiny
+- efficient-ml
+datasets:
+- jigsaw-toxic-comment-classification-challenge
+metrics:
+- f1
+- accuracy
+- precision
+- recall
+model-index:
+- name: dendritic-bert-tiny-toxicity
+  results:
+  - task:
+      type: text-classification
+      name: Toxicity Detection
+    dataset:
+      name: Civil Comments
+      type: jigsaw_toxicity_pred
+    metrics:
+    - type: f1
+      value: 0.358
+      name: F1 Score (Toxic Class)
+    - type: accuracy
+      value: 0.918
+      name: Accuracy
+    - type: inference_time
+      value: 2.25
+      name: Inference Latency (ms)
+---
+
+# Dendritic BERT-Tiny for Toxicity Detection
+
+## Model Description
+
+This model applies **Perforated Backpropagation with Dendritic Optimization** to enhance a compact BERT-Tiny model (4.8M parameters) for toxicity classification. It achieves performance comparable to BERT-Base (109M parameters) while maintaining **17.8x faster inference speed**.
+
+**Key Features:**
+-  **22.8x smaller** than BERT-Base (4.8M vs 109M parameters)
+-  **17.8x faster** inference (2.25ms vs 40ms)
+-  **Superior toxic detection** (F1=0.358 vs BERT-Base F1=0.05)
+-  **Dendritic optimization** using PerforatedAI
+-  **Edge-ready** for real-time deployment
+
+## Model Details
+
+- **Model Type:** BERT-Tiny with Dendritic Optimization
+- **Language:** English
+- **Task:** Binary Text Classification (Toxic/Non-Toxic)
+- **Base Model:** [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny)
+- **Framework:** PyTorch 2.9.1 + PerforatedAI 3.0.7
+- **Parameters:** 4,798,468
+- **Model Size:** 19.3 MB
+
+## Performance Metrics
+
+| Metric | Dendritic BERT-Tiny | BERT-Base | Improvement |
+|--------|---------------------|-----------|-------------|
+| **Parameters** | 4.8M | 109M | 22.8x smaller |
+| **F1 Score (Toxic)** | 0.358 | 0.050 | 7.16x better |
+| **Accuracy** | 91.8% | 91.0% | +0.8% |
+| **Inference Time** | 2.25ms | 40.1ms | 17.8x faster |
+| **Throughput** | 444 samples/s | 25 samples/s | 17.8x higher |
+
+## Intended Use
+
+### Primary Use Cases
+- Real-time content moderation in online forums and social media
+- Edge device deployment for resource-constrained environments
+- High-throughput toxicity screening systems
+- Research in efficient NLP and dendritic optimization
+
+### Limitations
+- Trained on Civil Comments dataset (English only)
+- May not generalize to all forms of toxicity or cultural contexts
+- Class imbalance handling (94% non-toxic samples)
+- Best suited for binary toxicity detection
+
+## How to Use
+
+### Installation
+
+```bash
+pip install transformers torch perforatedai
+```
+
+### Quick Start
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_name = "your-username/dendritic-bert-tiny-toxicity"
+tokenizer = AutoTokenizer.from_pretrained("prajjwal1/bert-tiny")
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Prepare input
+text = "This is a sample comment to classify"
+inputs = tokenizer(text, return_tensors="pt", max_length=128, truncation=True, padding=True)
+
+# Inference
+model.eval()
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    prediction = torch.argmax(logits, dim=-1)
+    
+# Get result
+label = "toxic" if prediction.item() == 1 else "non-toxic"
+confidence = torch.softmax(logits, dim=-1)[0][prediction].item()
+print(f"Prediction: {label} (confidence: {confidence:.2%})")
+```
+
+### Batch Processing for High Throughput
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+model_name = "your-username/dendritic-bert-tiny-toxicity"
+tokenizer = AutoTokenizer.from_pretrained("prajjwal1/bert-tiny")
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example batch of comments
+comments = [
+    "This is a great discussion!",
+    "You are absolutely terrible",
+    "I disagree but respect your opinion",
+]
+
+# Tokenize batch
+inputs = tokenizer(comments, return_tensors="pt", max_length=128, 
+                   truncation=True, padding=True)
+
+# Batch inference
+model.eval()
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.argmax(outputs.logits, dim=-1)
+    probabilities = torch.softmax(outputs.logits, dim=-1)
+
+# Display results
+for comment, pred, probs in zip(comments, predictions, probabilities):
+    label = "toxic" if pred.item() == 1 else "non-toxic"
+    confidence = probs[pred].item()
+    print(f"'{comment}' -> {label} ({confidence:.2%})")
+```
+
+### Edge Deployment with Quantization
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification
+
+model = AutoModelForSequenceClassification.from_pretrained(
+    "your-username/dendritic-bert-tiny-toxicity"
+)
+
+# Dynamic quantization for edge devices
+quantized_model = torch.quantization.quantize_dynamic(
+    model, {torch.nn.Linear}, dtype=torch.qint8
+)
+
+# Model is now ~75% smaller and faster on CPU
+```
+
+## Training Details
+
+### Training Dataset
+- **Source:** Civil Comments / Jigsaw Toxicity Dataset
+- **Train samples:** 5,000 (4.54% toxic)
+- **Validation samples:** 1,000 (6.60% toxic)
+- **Test samples:** 1,000 (9.00% toxic)
+
+### Training Procedure
+- **Optimizer:** AdamW (lr=2e-5, weight_decay=0.01)
+- **Epochs:** 10
+- **Batch size:** 32
+- **Max sequence length:** 128 tokens
+- **Loss function:** Cross-entropy with class weights (1.0, 21.0)
+- **Warmup steps:** 500
+- **Early stopping:** Patience 3 epochs
+
+### Dendritic Optimization
+This model uses **Perforated Backpropagation** with dendritic nodes to enhance learning capacity:
+- Correlation threshold: 0.95
+- Perforated AI version: 3.0.7
+- Configuration: 3D tensor output dimensions for transformer layers
+
+## Technical Architecture
+
+```
+Input Text (max 128 tokens)
+    ↓
+BERT-Tiny Tokenizer
+    ↓
+Embedding Layer (vocab_size: 30522, hidden: 128)
+    ↓
+Transformer Layer 1 (2 attention heads) + Dendritic Nodes
+    ↓
+Transformer Layer 2 (2 attention heads) + Dendritic Nodes
+    ↓
+Pooler Layer (CLS token)
+    ↓
+Classification Head (128 → 2)
+    ↓
+Output: [non-toxic_logit, toxic_logit]
+```
+
+## Ethical Considerations
+
+### Bias and Fairness
+- Model may inherit biases from the Civil Comments dataset
+- Performance may vary across demographic groups and cultural contexts
+- Should not be the sole decision-maker in content moderation
+
+### Recommended Practices
+- Use as part of a human-in-the-loop moderation system
+- Regularly evaluate for fairness across user demographics
+- Combine with other signals for critical decisions
+- Provide appeal mechanisms for users
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{dendritic-bert-tiny-2026,
+  title={Giant-Killer NLP: Dendritic Optimization for Toxicity Classification},
+  author={PROJECT-Z Team},
+  year={2026},
+  publisher={HuggingFace Hub},
+  howpublished={\url{https://huggingface.co/your-username/dendritic-bert-tiny-toxicity}},
+  note={PyTorch Dendritic Optimization Hackathon}
+}
+```
+
+## Acknowledgments
+
+- **Base Model:** BERT-Tiny by [prajjwal1](https://huggingface.co/prajjwal1)
+- **Optimization Framework:** [PerforatedAI](https://perforatedai.com/)
+- **Dataset:** Jigsaw/Google Civil Comments
+- **Hackathon:** PyTorch Dendritic Optimization Challenge
+
+## License
+
+Apache 2.0
+
+## Contact
+
+For questions, issues, or collaboration opportunities, please open an issue on the [GitHub repository](https://github.com/your-username/dendritic-bert-tiny-toxicity).
+
+---
+
+**Developed for the PyTorch Dendritic Optimization Hackathon - January 2026**