Datasetpublic

napsternxg/kaggle_crowdflower_ecommerce_search_relevance

Crowdflower Search Results Relevance Original source: https://www.kaggle.com/c/crowdflower-search-relevance/overview More detailed version: https://data.world/crowdflower/ecommerce-search-relevance Citation @misc{crowdflower-search-relevance, author = {AaronZukoff, Anna Montoya, JustinTenuto, Wendy Kan}, title = {Crowdflower Search Results Relevance}, publisher = {Kaggle}, year = {2015}, url =… See the full description on the dataset page: https://huggingface.co/datasets/napsternxg/kaggle_crowdflower_ecommerce_search_relevance.

sourceHugging Faceupdated 2y agoView on Hugging Face

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Dataset Card

Crowdflower Search Results Relevance

—Original source: https://www.kaggle.com/c/crowdflower-search-relevance/overview
—More detailed version: https://data.world/crowdflower/ecommerce-search-relevance

Citation

@misc{crowdflower-search-relevance,
    author = {AaronZukoff, Anna Montoya, JustinTenuto, Wendy Kan},
    title = {Crowdflower Search Results Relevance},
    publisher = {Kaggle},
    year = {2015},
    url = {https://kaggle.com/competitions/crowdflower-search-relevance}
}

Code for generating data

python

# ! unzip train.csv.zip
# ! unzip test.csv.zip 


df_comp = pd.concat([
    pd.read_csv("./train.csv").assign(split="train"),
    pd.read_csv("./test.csv").assign(split="test"),
])

dataset = DatasetDict(
    train=Dataset.from_pandas(df_comp[df_comp["split"] == "train"].reset_index(drop=True)),
    test=Dataset.from_pandas(df_comp[df_comp["split"] == "test"].reset_index(drop=True)),
)

dataset.push_to_hub("napsternxg/kaggle_crowdflower_ecommerce_search_relevance")