MODEL_CARD.md

Model card - tox21_snn_classifier

Model details

• Model name: Self-Normalizing Neural Network Tox21 Baseline
• Developer: JKU Linz
• Paper URL: https://proceedings.neurips.cc/paper_files/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html
• Model type / architecture:
  • Self-Normalizing Neural Network implemented using PyTorch.
  • Hyperparameters: https://huggingface.co/spaces/ml-jku/tox21_snn_classifier/blob/main/config/config.json
  • A multitask network is trained for all Tox21 targets.
• Inference: Access via FastAPI endpoint. Upon receiving a Tox21 prediction request, the model generates and returns predictions for all Tox21 targets simultaneously.
• Model version: v0
• Model date: 14.10.2025
• Reproducibility: Code for full training is available and enables retraining from scratch.

Intended use

This model serves as a baseline benchmark for evaluating and comparing toxicity prediction methods across the 12 pathway assays of the Tox21 dataset. It is not intended for clinical decision-making without experimental validation.

Metric

Each Tox21 task is evaluated using the area under the receiver operating characteristic curve (AUC). Overall performance is reported as the mean AUC across all individual tasks.

Training data

Tox21 training and validation sets.

Evaluation data

Tox21 test set.