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Datasets:
maomlab
/
AttentiveSkin

Tasks:
Tabular Classification
Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
1K - 10K
Tags:
chemistry
toxicology
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files
xet
Community
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haneulpark commited on
Commit
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Update README.md

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  1. README.md +67 -34
README.md CHANGED
@@ -23,63 +23,96 @@ citation: >-
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  size_categories:
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  - 1K<n<10K
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  config_names:
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- - AttentiveSkin
 
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  configs:
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- - config_name: AttentiveSkin
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  data_files:
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- - split: test
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- path: AttentiveSkin/test.csv
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- - split: train
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- path: AttentiveSkin/train.csv
 
 
 
 
 
 
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  dataset_info:
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- - config_name: AttentiveSkin
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  features:
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  - name: "Name"
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- dtype: string
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  - name: "Synonym"
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- dtype: string
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  - name: "CAS RN"
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- dtype: string
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  - name: "GHS"
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- dtype:
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- class_label:
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- names:
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- 1: "Cat 1"
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- 2: "Cat 2"
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- 0: "NC"
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  - name: "Detailed Page"
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- dtype: string
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  - name: "Evidence"
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- dtype: string
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  - name: "OECD TG 404"
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- dtype: string
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  - name: "Data Source"
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- dtype: string
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  - name: "Frequency"
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  dtype: int64
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  - name: "SMILES"
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- dtype: string
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  - name: "SMILES URL"
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- dtype: string
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  - name: "SMILES Source"
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- dtype: string
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  - name: "Canonical SMILES"
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- dtype: string
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  - name: "Split"
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- dtype: string
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- - name: "ClusterNo"
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- dtype: int64
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- - name: "MolCount"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  dtype: int64
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- - name: "group"
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- dtype: string
 
 
 
 
 
 
 
 
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  splits:
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  - name: train
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- num_bytes: 328704
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- num_examples: 2416
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  - name: test
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- num_bytes: 109336
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- num_examples: 803
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  ---
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  # Attentive Skin
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  To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods
 
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  size_categories:
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  - 1K<n<10K
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  config_names:
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+ - Cog_Neg
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+ - Irrit_Neg
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  configs:
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+ - config_name: Cog_Neg
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  data_files:
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+ - split: test
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+ path: Cog_Neg/test.csv
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+ - split: train
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+ path: Cog_Neg/train.csv
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+ - config_name: Irrit_Neg
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+ data_files:
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+ - split: test
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+ path: Irrit_Neg/test.csv
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+ - split: train
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+ path: Irrit_Neg/train.csv
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  dataset_info:
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+ - config_name: Irrit_Neg
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  features:
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  - name: "Name"
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+ dtype: object
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  - name: "Synonym"
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+ dtype: float64
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  - name: "CAS RN"
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+ dtype: object
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  - name: "GHS"
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+ dtype: object
 
 
 
 
 
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  - name: "Detailed Page"
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+ dtype: object
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  - name: "Evidence"
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+ dtype: object
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  - name: "OECD TG 404"
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+ dtype: object
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  - name: "Data Source"
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+ dtype: object
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  - name: "Frequency"
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  dtype: int64
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  - name: "SMILES"
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+ dtype: object
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  - name: "SMILES URL"
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+ dtype: object
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  - name: "SMILES Source"
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+ dtype: object
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  - name: "Canonical SMILES"
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+ dtype: object
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  - name: "Split"
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+ dtype: object
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+ splits:
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+ - name: train
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+ num_bytes: 196688
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+ num_examples: 1755
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+ - name: test
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+ num_bytes: 20400
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+ num_examples: 181
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+ - config_name: Irrit_Neg
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+ features:
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+ - name: "Name"
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+ dtype: object
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+ - name: "Synonym"
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+ dtype: float64
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+ - name: "CAS RN"
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+ dtype: object
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+ - name: "GHS"
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+ dtype: object
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+ - name: "Detailed Page"
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+ dtype: object
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+ - name: "Evidence"
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+ dtype: object
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+ - name: "OECD TG 404"
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+ dtype: object
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+ - name: "Data Source"
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+ dtype: object
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+ - name: "Frequency"
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  dtype: int64
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+ - name: "SMILES"
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+ dtype: object
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+ - name: "SMILES URL"
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+ dtype: object
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+ - name: "SMILES Source"
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+ dtype: object
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+ - name: "Canonical SMILES"
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+ dtype: object
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+ - name: "Split"
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+ dtype: object
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  splits:
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  - name: train
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+ num_bytes: 249776
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+ num_examples: 2229
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  - name: test
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+ num_bytes: 29136
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+ num_examples: 259
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  ---
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  # Attentive Skin
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  To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods