Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,68 @@
+---
+license: apache-2.0
+tags:
+- question-answering
+- complexity-classification
+- distilbert
+datasets:
+- wesley7137/question_complexity_classification
+---
+
+# question-complexity-classifier
+
+🤖 Fine-tuned DistilBERT model for classifying question complexity (Simple vs Complex)
+
+## Model Details
+
+### Model Description
+- **Architecture:** DistilBERT base uncased
+- **Fine-tuned on:** Question Complexity Classification Dataset
+- **Language:** English
+- **License:** Apache 2.0
+- **Max Sequence Length:** 128 tokens
+
+## Uses
+
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "text-classification",
+    model="grahamaco/question-complexity-classifier",
+    tokenizer="grahamaco/question-complexity-classifier",
+    truncation=True,
+    max_length=128  # Matches training config
+)
+
+result = classifier("Explain quantum computing in simple terms")
+# Output example: {'label': 'COMPLEX', 'score': 0.97}
+```
+
+## Training Details
+
+- **Epochs:** 5
+- **Batch Size:** 32 (global)
+- **Learning Rate:** 2e-5
+- **Train/Val/Test Split:** 80/10/10 (stratified)
+- **Early Stopping:** Patience of 2 epochs
+
+## Evaluation Results
+
+| Metric | Value |
+|--------|-------|
+| Accuracy | 0.92 |
+| F1 Score | 0.91 |
+
+## Performance
+
+| Metric | Value |
+|--------|-------|
+| Inference Latency | 15.2ms (CPU) |
+| Throughput | 68.4 samples/sec (GPU) |
+
+## Ethical Considerations
+This model is intended for educational content classification only. Developers should:
+- Regularly audit performance across different question types
+- Monitor for unintended bias in complexity assessments
+- Provide human-review mechanisms for high-stakes classifications
+- Validate classifications against original context when used with RAG systems