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@@ -13,7 +13,12 @@ short_description: XGBoost baseline classifier for Tox21
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  This repository hosts a Hugging Face Space that provides an examplary API for submitting models to the [Tox21 Leaderboard](https://huggingface.co/spaces/tschouis/tox21_leaderboard).
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- In this example, we train a XGBoost classifier on the Tox21 targets and save the trained model in the `assets/` folder.
 
 
 
 
 
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  **Important:** For leaderboard submission, your Space does not need to include training code. It only needs to implement inference in the `predict()` function inside `predict.py`. The `predict()` function must keep the provided skeleton: it should take a list of SMILES strings as input and return a prediction dictionary as output, with SMILES and targets as keys. Therefore, any preprocessing of SMILES strings must be executed on-the-fly during inference.
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  This repository hosts a Hugging Face Space that provides an examplary API for submitting models to the [Tox21 Leaderboard](https://huggingface.co/spaces/tschouis/tox21_leaderboard).
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+ This repository hosts a Hugging Face Space that provides an API for submitting models to the [Tox21 Leaderboard](https://huggingface.co/spaces/ml-jku/tox21_leaderboard).
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+ Here **XGBoost** models are trained on the Tox21 dataset, and the trained models are provided for
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+ inference. Model input is a SMILES string of the small molecule, and the output are 12 numeric values for
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+ each of the toxic effects of the Tox21 dataset.
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  **Important:** For leaderboard submission, your Space does not need to include training code. It only needs to implement inference in the `predict()` function inside `predict.py`. The `predict()` function must keep the provided skeleton: it should take a list of SMILES strings as input and return a prediction dictionary as output, with SMILES and targets as keys. Therefore, any preprocessing of SMILES strings must be executed on-the-fly during inference.
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