| # X-ROBUST-009 interface |
|
|
| ## Entry point: `model.py` |
|
|
| ```python |
| def load(checkpoint_dir: str, models_dir: str, device: str): |
| """Return whatever predict() needs; roberta-base is under models_dir/roberta-base.""" |
| |
| def predict(state, texts: list[str]): |
| """Return (labels, confidence, abstain), each of length len(texts): |
| labels int 0..149, your best in-scope intent (always given, also when you abstain) |
| confidence float in [0, 1], your probability that `labels` is the right answer |
| abstain bool, True if the assistant should not answer""" |
| ``` |
|
|
| - Intent indices follow `$ABENCH_DATA_DIR/intents.json`; label −1 in the public data marks out-of-scope queries. |
| - `train.py --data "$ABENCH_DATA_DIR" --out checkpoint` must reproduce the checkpoint. |
|
|
| ## Evaluator code you can read |
|
|
| `stages.py` (batching, timing), `metrics.py` (AURC, ECE, AUROC, decision accuracy), `textpack.py`, |
| `abench_metrics.py`, `abench_stage.py`, `selfcheck.py`, and the reference submission's code under `baseline/`. |
|
|
| ## Available software |
|
|
| Python 3, PyTorch (CUDA), transformers, NumPy, SciPy, scikit-learn. Nothing can be installed. |
|
|
