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pandas-so-function-classifier

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pandas-so-function-classifier
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{
  "add_cross_attention": false,
  "architectures": [
    "RobertaForSequenceClassification"
  ],
  "attention_probs_dropout_prob": 0.1,
  "bos_token_id": 0,
  "classifier_dropout": null,
  "dtype": "float32",
  "eos_token_id": 2,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "id2label": {
    "0": "pandas.Categorical",
    "1": "pandas.Categorical.from_codes",
    "2": "pandas.CategoricalDtype",
    "3": "pandas.CategoricalIndex",
    "4": "pandas.CategoricalIndex.add_categories",
    "5": "pandas.CategoricalIndex.remove_unused_categories",
    "6": "pandas.DataFrame",
    "7": "pandas.DataFrame.T",
    "8": "pandas.DataFrame.abs",
    "9": "pandas.DataFrame.add",
    "10": "pandas.DataFrame.add_prefix",
    "11": "pandas.DataFrame.add_suffix",
    "12": "pandas.DataFrame.agg",
    "13": "pandas.DataFrame.aggregate",
    "14": "pandas.DataFrame.align",
    "15": "pandas.DataFrame.all",
    "16": "pandas.DataFrame.any",
    "17": "pandas.DataFrame.apply",
    "18": "pandas.DataFrame.asfreq",
    "19": "pandas.DataFrame.asof",
    "20": "pandas.DataFrame.assign",
    "21": "pandas.DataFrame.astype",
    "22": "pandas.DataFrame.at",
    "23": "pandas.DataFrame.at_time",
    "24": "pandas.DataFrame.attrs",
    "25": "pandas.DataFrame.between_time",
    "26": "pandas.DataFrame.bfill",
    "27": "pandas.DataFrame.boxplot",
    "28": "pandas.DataFrame.clip",
    "29": "pandas.DataFrame.columns",
    "30": "pandas.DataFrame.combine",
    "31": "pandas.DataFrame.combine_first",
    "32": "pandas.DataFrame.compare",
    "33": "pandas.DataFrame.convert_dtypes",
    "34": "pandas.DataFrame.copy",
    "35": "pandas.DataFrame.corr",
    "36": "pandas.DataFrame.corrwith",
    "37": "pandas.DataFrame.count",
    "38": "pandas.DataFrame.cov",
    "39": "pandas.DataFrame.cummax",
    "40": "pandas.DataFrame.cummin",
    "41": "pandas.DataFrame.cumprod",
    "42": "pandas.DataFrame.cumsum",
    "43": "pandas.DataFrame.describe",
    "44": "pandas.DataFrame.diff",
    "45": "pandas.DataFrame.div",
    "46": "pandas.DataFrame.divide",
    "47": "pandas.DataFrame.dot",
    "48": "pandas.DataFrame.drop",
    "49": "pandas.DataFrame.drop_duplicates",
    "50": "pandas.DataFrame.droplevel",
    "51": "pandas.DataFrame.dropna",
    "52": "pandas.DataFrame.dtypes",
    "53": "pandas.DataFrame.duplicated",
    "54": "pandas.DataFrame.empty",
    "55": "pandas.DataFrame.eq",
    "56": "pandas.DataFrame.equals",
    "57": "pandas.DataFrame.eval",
    "58": "pandas.DataFrame.ewm",
    "59": "pandas.DataFrame.expanding",
    "60": "pandas.DataFrame.explode",
    "61": "pandas.DataFrame.ffill",
    "62": "pandas.DataFrame.fillna",
    "63": "pandas.DataFrame.filter",
    "64": "pandas.DataFrame.first_valid_index",
    "65": "pandas.DataFrame.from_dict",
    "66": "pandas.DataFrame.from_records",
    "67": "pandas.DataFrame.ge",
    "68": "pandas.DataFrame.get",
    "69": "pandas.DataFrame.groupby",
    "70": "pandas.DataFrame.gt",
    "71": "pandas.DataFrame.head",
    "72": "pandas.DataFrame.hist",
    "73": "pandas.DataFrame.iat",
    "74": "pandas.DataFrame.idxmax",
    "75": "pandas.DataFrame.idxmin",
    "76": "pandas.DataFrame.iloc",
    "77": "pandas.DataFrame.index",
    "78": "pandas.DataFrame.infer_objects",
    "79": "pandas.DataFrame.info",
    "80": "pandas.DataFrame.insert",
    "81": "pandas.DataFrame.interpolate",
    "82": "pandas.DataFrame.isin",
    "83": "pandas.DataFrame.isna",
    "84": "pandas.DataFrame.isnull",
    "85": "pandas.DataFrame.items",
    "86": "pandas.DataFrame.iterrows",
    "87": "pandas.DataFrame.itertuples",
    "88": "pandas.DataFrame.join",
    "89": "pandas.DataFrame.kurtosis",
    "90": "pandas.DataFrame.last_valid_index",
    "91": "pandas.DataFrame.le",
    "92": "pandas.DataFrame.loc",
    "93": "pandas.DataFrame.lt",
    "94": "pandas.DataFrame.map",
    "95": "pandas.DataFrame.mask",
    "96": "pandas.DataFrame.max",
    "97": "pandas.DataFrame.mean",
    "98": "pandas.DataFrame.median",
    "99": "pandas.DataFrame.melt",
    "100": "pandas.DataFrame.memory_usage",
    "101": "pandas.DataFrame.merge",
    "102": "pandas.DataFrame.min",
    "103": "pandas.DataFrame.mode",
    "104": "pandas.DataFrame.mul",
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