README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: CUMCM-2025C.csv
    encoding: GBK

Download Dataset

from datasets import load_dataset

# 1. Specify the dataset's "repository ID"
# Replace "your-username/your-dataset-name" with the actual ID of the dataset you want to download
repo_id = "sxj1024/cumcm_test"

# 2. Call load_dataset()
# This will automatically download the data from the Hub (if not cached locally),
# then load it into memory (or in streaming mode)
dataset = load_dataset(repo_id)

# 3. View and use the dataset
print(dataset)
  
# You can access different splits like a dictionary
# print(dataset["train"])
   
# View the first training data entry
# print(dataset["train"][0])

Execution Output

(base) PS D:\Desktop\tmp\huggingFace_datasets> python3 .\getDateSet.py
README.md: 100%|██████████████████████████████████████████████████████████████████████| 749/749 [00:00<00:00, 4.21MB/s]
C:\Users\GeJie\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.13_qbz5n2kfra8p0\LocalCache\local-packages\Python313\site-packages\huggingface_hub\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\Users\GeJie\.cache\huggingface\hub\datasets--sxj1024--cumcm_test. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.
To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development
warnings.warn(message)
CUMCM-2025C.csv: 461kB [00:02, 161kB/s]
Generating train split: 100%|████████████████████████████████████████████| 1687/1687 [00:00<00:00, 22949.28 examples/s]
DatasetDict({
train: Dataset({
   features: ['Proportion of Filtered Reads', 'Pregnant Woman Code', 'Age', 'Height', 'Weight', 'Last Menstrual Period', 'IVF Pregnancy (IVF: In Vitro Fertilization)', 'Detection Date', 'Number of Detection Blood Draws', 'Gestational Week at Detection', " Pregnant Woman's BMI (BMI: Body Mass Index)", 'Number of Raw Reads', 'Proportion Aligned to Reference Genome', 'Proportion of Duplicate Reads', ' Number of Uniquely Aligned Reads', ' GC Content', 'Z-Score of Chromosome 13', 'Z-Score of Chromosome 18', 'Z-Score of Chromosome 21', ' Z-Score of Chromosome X', 'Z-Score of Chromosome Y', 'Concentration of Chromosome Y', 'Concentration of Chromosome X', 'GC Content of Chromosome 13', ' GC Content of Chromosome 18', ' GC Content of Chromosome 21', 'Proportion of Filtered Reads.1', 'Chromosomal Aneuploidy (e.g., T13, T18, T21 represent trisomy 13, 18, 21 respectively)', ' Number of Pregnancies', 'Number of Deliveries', ' Fetal Health Status (Yes/No)', ' Fetal Type (e.g., Male Fetus)'],
   num_rows: 1687
})
})