docs: add README for ML training pipeline

ml/README.md

@@ -0,0 +1,40 @@
+# ML Pipeline
+
+This directory contains the machine learning pipeline for training the complexity classifier.
+
+## Structure
+
+```
+ml/
+├── data/              # Dataset loading and preprocessing
+│   └── load_dataset.py
+├── training/          # Model training and evaluation
+│   ├── train.py       # DistilBERT fine-tuning
+│   └── evaluate.py    # Model evaluation
+├── export/            # Model export
+│   └── convert_to_onnx.py
+└── artifacts/         # Saved models and metrics
+    ├── model.onnx
+    └── metrics.json
+```
+
+## Training
+
+```bash
+# Train the complexity classifier
+python -m ml.training.train --dataset arc --epochs 5
+
+# Evaluate the model
+python -m ml.training.evaluate --model-dir ml/artifacts/complexity-classifier
+
+# Export to ONNX
+python -m ml.export.convert_to_onnx --model-dir ml/artifacts/complexity-classifier
+```
+
+## Dataset
+
+The classifier is trained on the ARC dataset (AI2 Reasoning Challenge) which provides:
+- **Easy examples**: Simple questions that can be handled by smaller models
+- **Challenge examples**: Complex questions requiring more capable models
+
+Alternatively, Easy2Hard-Bench can be used for continuous difficulty scores.