Create README.md

README.md

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+---
+license: apache-2.0
+base_model:
+- distilbert/distilbert-base-uncased
+tags:
+- Safety
+- Content Moderation
+- Hate Speech Detection
+- Toxicity Detection
+language:
+- en
+library_name: transformers
+datasets:
+- Paul/hatecheck
+- dvruette/toxic-completions
+- nvidia/Aegis-AI-Content-Safety-Dataset-2.0
+pipeline_tag: text-classification
+---
+
+# 🐾 PurrBERT-v1.1
+
+**PurrBERT-v1.1** is a lightweight content-safety classifier built on top of [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased).  
+It’s designed to flag harmful or unsafe user prompts before they reach an AI assistant.  
+
+This model is trained on a combination of:
+- [HateCheck](https://huggingface.co/datasets/Paul/hatecheck)
+- [Toxic Completions](https://huggingface.co/datasets/dvruette/toxic-completions)
+- [Aegis AI Content Safety Dataset 2.0](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0)
+
+---
+
+## 📝 Model Description
+
+- **Architecture**: DistilBERT with a classification head (2 labels: `SAFE` vs. `FLAGGED`)  
+- **Purpose**: Detect hate speech, toxic content, and unsafe prompts in English text.  
+- **Input**: A single string (prompt text).  
+- **Output**: A binary prediction:  
+  - `0` → SAFE  
+  - `1` → FLAGGED  
+
+---
+
+## 🧠 Training Details
+
+- **Base model**: `distilbert-base-uncased`
+- **Epochs**: 2 (initial run)  
+- **Optimizer**: AdamW  
+- **Batch size**: 16  
+- **Learning rate**: 2e-5  
+- **Weight decay**: 0.01  
+
+Loss dropped steadily during training, and metrics were evaluated on a held-out test set.
+
+---
+
+## 📊 Evaluation Results
+
+On an Aegis test slice:
+
+| Metric     | Score  | v1     | v2     |
+|------------|--------|--------|--------|
+| Accuracy   |        | 0.8050 | 0.8200 |
+| Precision  |        | 0.7731 | 0.8091 |
+| Recall     |        | 0.8846 | 0.8558 |
+| F1 Score   |        | 0.8251 | 0.8318 |
+
+Latency per prompt on GPU: **~0.0230 sec**
+
+---
+
+## 🚀 Usage
+
+```python
+from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification
+import torch
+
+# Load trained model and tokenizer
+model = DistilBertForSequenceClassification.from_pretrained("purrgpt-community/purrbert-v1.1")
+tokenizer = DistilBertTokenizerFast.from_pretrained("purrgpt-community/purrbert-v1.1")
+model.eval()
+
+def classify_prompt(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        pred = torch.argmax(outputs.logits, dim=-1).item()
+    return "SAFE" if pred == 0 else "FLAGGED"
+
+print(classify_prompt("You are worthless and nobody likes you!"))
+# → FLAGGED
+````
+
+---
+
+## ⚠️ Limitations & Bias
+
+* The model is trained primarily on English datasets.
+* It may produce false positives on edgy but non-harmful speech, or false negatives on subtle harms.
+* It reflects biases present in its training datasets.
+
+---
+
+## 🐾 Intended Use
+
+PurrBERT is intended for **moderating prompts** before they’re passed to AI models or for content-safety tasks.
+It is **not** a replacement for professional moderation in high-risk settings.