Datasets:
Tasks:
Tabular Classification
Modalities:
Tabular
Languages:
No linguistic content
Size:
100K<n<1M
License:
Update README.md
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README.md
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---
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language:
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- zxx
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---
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language:
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license: cc-by-4.0
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tags:
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- chemistry
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task_categories:
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- tabular-classification
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configs:
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- config_name: csv
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data_files:
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- split: train
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dtype: float32
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- name: has_cl
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dtype: int8
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config_name:
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---
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# Dataset Summary
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- Splits: 80% train (618,272), 20% test (154,568).
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- Files: 968442_non_cl_filter_S10.rds, 386420_cl_data.rds, 80%_618272_train_binary.rds, 20%_154568_test_binary.rds.
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## Features
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- mz0: m/z of M
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- int2_o_int0: intensity ratio M+2/M
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```python
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from datasets import load_dataset
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#
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ds = load_dataset(
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)
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```
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year = {2024},
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doi = {10.1021/acs.analchem.3c05124},
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note = {PMID: 38294426},
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url = {https://doi.org/10.1021/acs.analchem.3c05124},
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---
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language:
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- zxx
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license: cc-by-4.0
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tags:
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- chemistry
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task_categories:
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- tabular-classification
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configs:
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- config_name: default
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data_files:
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- split: train
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path: 80%_618272_train_binary.rds
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- split: test
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path: 20%_154568_test_binary.rds
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- config_name: csv
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data_files:
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- split: train
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dtype: float32
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- name: has_cl
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dtype: int8
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config_name: default
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---
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# Dataset Summary
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- Splits: 80% train (618,272), 20% test (154,568).
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- Files: 968442_non_cl_filter_S10.rds, 386420_cl_data.rds, 80%_618272_train_binary.rds, 20%_154568_test_binary.rds.
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## How train.csv and test.csv were created
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- Source splits come from the RDS files above: 80%_618272_train_binary.rds (train) and 20%_154568_test_binary.rds (test).
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- Conversion was done locally using Python with pyreadr and pandas (see `data/converter.ipynb`).
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- Steps:
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1. Read each .rds table using pyreadr.read_r(...)
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2. Optionally cast numeric columns to float32/int32 for compact CSVs
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3. Save to CSV with index=False as `train.csv` and `test.csv`
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Example code used:
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```python
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import pyreadr, pandas as pd
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train_df = next(iter(pyreadr.read_r("80%_618272_train_binary.rds").values()))
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test_df = next(iter(pyreadr.read_r("20%_154568_test_binary.rds").values()))
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train_df.to_csv("train.csv", index=False)
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test_df.to_csv("test.csv", index=False)
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```
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## Features
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- mz0: m/z of M
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- int2_o_int0: intensity ratio M+2/M
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```python
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from datasets import load_dataset
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# Load CSVs from the Hub using the CSV builder
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ds = load_dataset(
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"csv",
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data_files={
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"train": "hf://datasets/chen1028/Cl-Containing-Compound/train.csv",
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"test": "hf://datasets/chen1028/Cl-Containing-Compound/test.csv",
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}
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)
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```
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year = {2024},
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doi = {10.1021/acs.analchem.3c05124},
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note = {PMID: 38294426},
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url = {https://doi.org/10.1021/acs.analchem.3c05124},
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```
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