def load_pandas_df(local_cache_path=None, file_split=Split.TRAIN, file_type="txt", nrows=None):
    """
    Loads the SNLI dataset as pd.DataFrame
    Download the dataset from "https://nlp.stanford.edu/projects/snli/snli_1.0.zip", unzip, and load

    Args:
        local_cache_path (str): Path (directory or a zip file) to cache the downloaded zip file.
            If None, all the intermediate files will be stored in a temporary directory and removed
            after use.
        file_split (str): File split to load, defaults to "train"
        file_type (str): File type to load, defaults to "txt"
        nrows (int): Number of rows to load, defaults to None (in which all rows will be returned)

    Returns:
        pd.DataFrame: SNLI dataset.
    """
    with download_path(local_cache_path) as path:
        filepath = os.path.join(path, "snli_1.0.zip")
        snlipath = _maybe_download_and_extract(filepath, file_split, file_type)

        if file_type == "txt":
            snli_df = pd.read_csv(snlipath, sep="\t", nrows=nrows)
        else:
            snli_df = pd.read_json(snlipath, lines=True)
            if nrows:
                snli_df = snli_df[:nrows]

    return snli_df

    Loads the SNLI dataset as pd.DataFrame
    Download the dataset from "https://nlp.stanford.edu/projects/snli/snli_1.0.zip", unzip, and load

    Args:
        local_cache_path (str): Path (directory or a zip file) to cache the downloaded zip file.
            If None, all the intermediate files will be stored in a temporary directory and removed
            after use.
        file_split (str): File split to load, defaults to "train"
        file_type (str): File type to load, defaults to "txt"
        nrows (int): Number of rows to load, defaults to None (in which all rows will be returned)

    Returns:
        pd.DataFrame: SNLI dataset.
    
utils_nlp/dataset/snli.py