Datasets:
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README.md
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@@ -141,11 +141,10 @@ then, from within python load the datasets library
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and load one of the `AttentiveSkin` datasets, e.g.,
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>>> Corr_Neg = datasets.load_dataset("maomlab/AttentiveSkin", name = 'Corr_Neg')
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Downloading readme: 100%|██████████|
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Downloading data: 100%|██████████|
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Generating
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Generating train split: 100%|██████████| 2416/2416 [00:00<00:00, 10.8Mexamples/s]
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and inspecting the loaded dataset
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### Use a dataset to train a model
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One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia.
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First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support
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and load one of the `AttentiveSkin` datasets, e.g.,
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>>> Corr_Neg = datasets.load_dataset("maomlab/AttentiveSkin", name = 'Corr_Neg')
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Downloading readme: 100%|██████████| 64.0k/64.0k [00:00<00:00, 11.7kkB/s]
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Downloading data: 100%|██████████| 1.02M/1.02M [00:00<00:00, 4.88MkB/s]
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Generating test split: 100%|██████████| 181/181 [00:00<00:00, 3189.72examples/s]
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Generating train split: 100%|██████████| 1755/1755 [00:00<00:00, 19806.87examples/s]
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and inspecting the loaded dataset
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})
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### Use a dataset to train a model
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One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia.
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First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support
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