Upload model
Browse files- config.json +1414 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +17 -0
config.json
ADDED
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
| 1 |
+
{
|
| 2 |
+
"add_cross_attention": false,
|
| 3 |
+
"architectures": [
|
| 4 |
+
"RobertaForSequenceClassification"
|
| 5 |
+
],
|
| 6 |
+
"attention_probs_dropout_prob": 0.1,
|
| 7 |
+
"bos_token_id": 0,
|
| 8 |
+
"classifier_dropout": null,
|
| 9 |
+
"dtype": "float32",
|
| 10 |
+
"eos_token_id": 2,
|
| 11 |
+
"hidden_act": "gelu",
|
| 12 |
+
"hidden_dropout_prob": 0.1,
|
| 13 |
+
"hidden_size": 768,
|
| 14 |
+
"id2label": {
|
| 15 |
+
"0": "pandas.Categorical",
|
| 16 |
+
"1": "pandas.Categorical.from_codes",
|
| 17 |
+
"2": "pandas.CategoricalDtype",
|
| 18 |
+
"3": "pandas.CategoricalIndex",
|
| 19 |
+
"4": "pandas.CategoricalIndex.add_categories",
|
| 20 |
+
"5": "pandas.CategoricalIndex.remove_unused_categories",
|
| 21 |
+
"6": "pandas.DataFrame",
|
| 22 |
+
"7": "pandas.DataFrame.T",
|
| 23 |
+
"8": "pandas.DataFrame.abs",
|
| 24 |
+
"9": "pandas.DataFrame.add",
|
| 25 |
+
"10": "pandas.DataFrame.add_prefix",
|
| 26 |
+
"11": "pandas.DataFrame.add_suffix",
|
| 27 |
+
"12": "pandas.DataFrame.agg",
|
| 28 |
+
"13": "pandas.DataFrame.aggregate",
|
| 29 |
+
"14": "pandas.DataFrame.align",
|
| 30 |
+
"15": "pandas.DataFrame.all",
|
| 31 |
+
"16": "pandas.DataFrame.any",
|
| 32 |
+
"17": "pandas.DataFrame.apply",
|
| 33 |
+
"18": "pandas.DataFrame.asfreq",
|
| 34 |
+
"19": "pandas.DataFrame.asof",
|
| 35 |
+
"20": "pandas.DataFrame.assign",
|
| 36 |
+
"21": "pandas.DataFrame.astype",
|
| 37 |
+
"22": "pandas.DataFrame.at",
|
| 38 |
+
"23": "pandas.DataFrame.at_time",
|
| 39 |
+
"24": "pandas.DataFrame.attrs",
|
| 40 |
+
"25": "pandas.DataFrame.between_time",
|
| 41 |
+
"26": "pandas.DataFrame.bfill",
|
| 42 |
+
"27": "pandas.DataFrame.boxplot",
|
| 43 |
+
"28": "pandas.DataFrame.clip",
|
| 44 |
+
"29": "pandas.DataFrame.columns",
|
| 45 |
+
"30": "pandas.DataFrame.combine",
|
| 46 |
+
"31": "pandas.DataFrame.combine_first",
|
| 47 |
+
"32": "pandas.DataFrame.compare",
|
| 48 |
+
"33": "pandas.DataFrame.convert_dtypes",
|
| 49 |
+
"34": "pandas.DataFrame.copy",
|
| 50 |
+
"35": "pandas.DataFrame.corr",
|
| 51 |
+
"36": "pandas.DataFrame.corrwith",
|
| 52 |
+
"37": "pandas.DataFrame.count",
|
| 53 |
+
"38": "pandas.DataFrame.cov",
|
| 54 |
+
"39": "pandas.DataFrame.cummax",
|
| 55 |
+
"40": "pandas.DataFrame.cummin",
|
| 56 |
+
"41": "pandas.DataFrame.cumprod",
|
| 57 |
+
"42": "pandas.DataFrame.cumsum",
|
| 58 |
+
"43": "pandas.DataFrame.describe",
|
| 59 |
+
"44": "pandas.DataFrame.diff",
|
| 60 |
+
"45": "pandas.DataFrame.div",
|
| 61 |
+
"46": "pandas.DataFrame.divide",
|
| 62 |
+
"47": "pandas.DataFrame.dot",
|
| 63 |
+
"48": "pandas.DataFrame.drop",
|
| 64 |
+
"49": "pandas.DataFrame.drop_duplicates",
|
| 65 |
+
"50": "pandas.DataFrame.droplevel",
|
| 66 |
+
"51": "pandas.DataFrame.dropna",
|
| 67 |
+
"52": "pandas.DataFrame.dtypes",
|
| 68 |
+
"53": "pandas.DataFrame.duplicated",
|
| 69 |
+
"54": "pandas.DataFrame.empty",
|
| 70 |
+
"55": "pandas.DataFrame.eq",
|
| 71 |
+
"56": "pandas.DataFrame.equals",
|
| 72 |
+
"57": "pandas.DataFrame.eval",
|
| 73 |
+
"58": "pandas.DataFrame.ewm",
|
| 74 |
+
"59": "pandas.DataFrame.expanding",
|
| 75 |
+
"60": "pandas.DataFrame.explode",
|
| 76 |
+
"61": "pandas.DataFrame.ffill",
|
| 77 |
+
"62": "pandas.DataFrame.fillna",
|
| 78 |
+
"63": "pandas.DataFrame.filter",
|
| 79 |
+
"64": "pandas.DataFrame.first_valid_index",
|
| 80 |
+
"65": "pandas.DataFrame.from_dict",
|
| 81 |
+
"66": "pandas.DataFrame.from_records",
|
| 82 |
+
"67": "pandas.DataFrame.ge",
|
| 83 |
+
"68": "pandas.DataFrame.get",
|
| 84 |
+
"69": "pandas.DataFrame.groupby",
|
| 85 |
+
"70": "pandas.DataFrame.gt",
|
| 86 |
+
"71": "pandas.DataFrame.head",
|
| 87 |
+
"72": "pandas.DataFrame.hist",
|
| 88 |
+
"73": "pandas.DataFrame.iat",
|
| 89 |
+
"74": "pandas.DataFrame.idxmax",
|
| 90 |
+
"75": "pandas.DataFrame.idxmin",
|
| 91 |
+
"76": "pandas.DataFrame.iloc",
|
| 92 |
+
"77": "pandas.DataFrame.index",
|
| 93 |
+
"78": "pandas.DataFrame.infer_objects",
|
| 94 |
+
"79": "pandas.DataFrame.info",
|
| 95 |
+
"80": "pandas.DataFrame.insert",
|
| 96 |
+
"81": "pandas.DataFrame.interpolate",
|
| 97 |
+
"82": "pandas.DataFrame.isin",
|
| 98 |
+
"83": "pandas.DataFrame.isna",
|
| 99 |
+
"84": "pandas.DataFrame.isnull",
|
| 100 |
+
"85": "pandas.DataFrame.items",
|
| 101 |
+
"86": "pandas.DataFrame.iterrows",
|
| 102 |
+
"87": "pandas.DataFrame.itertuples",
|
| 103 |
+
"88": "pandas.DataFrame.join",
|
| 104 |
+
"89": "pandas.DataFrame.kurtosis",
|
| 105 |
+
"90": "pandas.DataFrame.last_valid_index",
|
| 106 |
+
"91": "pandas.DataFrame.le",
|
| 107 |
+
"92": "pandas.DataFrame.loc",
|
| 108 |
+
"93": "pandas.DataFrame.lt",
|
| 109 |
+
"94": "pandas.DataFrame.map",
|
| 110 |
+
"95": "pandas.DataFrame.mask",
|
| 111 |
+
"96": "pandas.DataFrame.max",
|
| 112 |
+
"97": "pandas.DataFrame.mean",
|
| 113 |
+
"98": "pandas.DataFrame.median",
|
| 114 |
+
"99": "pandas.DataFrame.melt",
|
| 115 |
+
"100": "pandas.DataFrame.memory_usage",
|
| 116 |
+
"101": "pandas.DataFrame.merge",
|
| 117 |
+
"102": "pandas.DataFrame.min",
|
| 118 |
+
"103": "pandas.DataFrame.mode",
|
| 119 |
+
"104": "pandas.DataFrame.mul",
|
| 120 |
+
"105": "pandas.DataFrame.multiply",
|
| 121 |
+
"106": "pandas.DataFrame.ne",
|
| 122 |
+
"107": "pandas.DataFrame.nlargest",
|
| 123 |
+
"108": "pandas.DataFrame.notna",
|
| 124 |
+
"109": "pandas.DataFrame.notnull",
|
| 125 |
+
"110": "pandas.DataFrame.nsmallest",
|
| 126 |
+
"111": "pandas.DataFrame.nunique",
|
| 127 |
+
"112": "pandas.DataFrame.pct_change",
|
| 128 |
+
"113": "pandas.DataFrame.pipe",
|
| 129 |
+
"114": "pandas.DataFrame.pivot",
|
| 130 |
+
"115": "pandas.DataFrame.pivot_table",
|
| 131 |
+
"116": "pandas.DataFrame.plot",
|
| 132 |
+
"117": "pandas.DataFrame.plot.area",
|
| 133 |
+
"118": "pandas.DataFrame.plot.bar",
|
| 134 |
+
"119": "pandas.DataFrame.plot.barh",
|
| 135 |
+
"120": "pandas.DataFrame.plot.box",
|
| 136 |
+
"121": "pandas.DataFrame.plot.density",
|
| 137 |
+
"122": "pandas.DataFrame.plot.hist",
|
| 138 |
+
"123": "pandas.DataFrame.plot.kde",
|
| 139 |
+
"124": "pandas.DataFrame.plot.line",
|
| 140 |
+
"125": "pandas.DataFrame.plot.pie",
|
| 141 |
+
"126": "pandas.DataFrame.plot.scatter",
|
| 142 |
+
"127": "pandas.DataFrame.pop",
|
| 143 |
+
"128": "pandas.DataFrame.pow",
|
| 144 |
+
"129": "pandas.DataFrame.prod",
|
| 145 |
+
"130": "pandas.DataFrame.product",
|
| 146 |
+
"131": "pandas.DataFrame.quantile",
|
| 147 |
+
"132": "pandas.DataFrame.query",
|
| 148 |
+
"133": "pandas.DataFrame.radd",
|
| 149 |
+
"134": "pandas.DataFrame.rank",
|
| 150 |
+
"135": "pandas.DataFrame.rdiv",
|
| 151 |
+
"136": "pandas.DataFrame.reindex",
|
| 152 |
+
"137": "pandas.DataFrame.reindex_like",
|
| 153 |
+
"138": "pandas.DataFrame.rename",
|
| 154 |
+
"139": "pandas.DataFrame.rename_axis",
|
| 155 |
+
"140": "pandas.DataFrame.reorder_levels",
|
| 156 |
+
"141": "pandas.DataFrame.replace",
|
| 157 |
+
"142": "pandas.DataFrame.resample",
|
| 158 |
+
"143": "pandas.DataFrame.reset_index",
|
| 159 |
+
"144": "pandas.DataFrame.rolling",
|
| 160 |
+
"145": "pandas.DataFrame.round",
|
| 161 |
+
"146": "pandas.DataFrame.sample",
|
| 162 |
+
"147": "pandas.DataFrame.select_dtypes",
|
| 163 |
+
"148": "pandas.DataFrame.set_axis",
|
| 164 |
+
"149": "pandas.DataFrame.set_index",
|
| 165 |
+
"150": "pandas.DataFrame.shape",
|
| 166 |
+
"151": "pandas.DataFrame.shift",
|
| 167 |
+
"152": "pandas.DataFrame.size",
|
| 168 |
+
"153": "pandas.DataFrame.sort_index",
|
| 169 |
+
"154": "pandas.DataFrame.sort_values",
|
| 170 |
+
"155": "pandas.DataFrame.squeeze",
|
| 171 |
+
"156": "pandas.DataFrame.stack",
|
| 172 |
+
"157": "pandas.DataFrame.std",
|
| 173 |
+
"158": "pandas.DataFrame.style",
|
| 174 |
+
"159": "pandas.DataFrame.sub",
|
| 175 |
+
"160": "pandas.DataFrame.subtract",
|
| 176 |
+
"161": "pandas.DataFrame.sum",
|
| 177 |
+
"162": "pandas.DataFrame.swaplevel",
|
| 178 |
+
"163": "pandas.DataFrame.tail",
|
| 179 |
+
"164": "pandas.DataFrame.take",
|
| 180 |
+
"165": "pandas.DataFrame.to_clipboard",
|
| 181 |
+
"166": "pandas.DataFrame.to_csv",
|
| 182 |
+
"167": "pandas.DataFrame.to_dict",
|
| 183 |
+
"168": "pandas.DataFrame.to_excel",
|
| 184 |
+
"169": "pandas.DataFrame.to_hdf",
|
| 185 |
+
"170": "pandas.DataFrame.to_html",
|
| 186 |
+
"171": "pandas.DataFrame.to_json",
|
| 187 |
+
"172": "pandas.DataFrame.to_latex",
|
| 188 |
+
"173": "pandas.DataFrame.to_markdown",
|
| 189 |
+
"174": "pandas.DataFrame.to_numpy",
|
| 190 |
+
"175": "pandas.DataFrame.to_parquet",
|
| 191 |
+
"176": "pandas.DataFrame.to_period",
|
| 192 |
+
"177": "pandas.DataFrame.to_pickle",
|
| 193 |
+
"178": "pandas.DataFrame.to_records",
|
| 194 |
+
"179": "pandas.DataFrame.to_sql",
|
| 195 |
+
"180": "pandas.DataFrame.to_string",
|
| 196 |
+
"181": "pandas.DataFrame.to_timestamp",
|
| 197 |
+
"182": "pandas.DataFrame.to_xarray",
|
| 198 |
+
"183": "pandas.DataFrame.to_xml",
|
| 199 |
+
"184": "pandas.DataFrame.transform",
|
| 200 |
+
"185": "pandas.DataFrame.transpose",
|
| 201 |
+
"186": "pandas.DataFrame.truncate",
|
| 202 |
+
"187": "pandas.DataFrame.tz_convert",
|
| 203 |
+
"188": "pandas.DataFrame.tz_localize",
|
| 204 |
+
"189": "pandas.DataFrame.unstack",
|
| 205 |
+
"190": "pandas.DataFrame.update",
|
| 206 |
+
"191": "pandas.DataFrame.value_counts",
|
| 207 |
+
"192": "pandas.DataFrame.values",
|
| 208 |
+
"193": "pandas.DataFrame.var",
|
| 209 |
+
"194": "pandas.DataFrame.where",
|
| 210 |
+
"195": "pandas.DataFrame.xs",
|
| 211 |
+
"196": "pandas.DatetimeIndex",
|
| 212 |
+
"197": "pandas.DatetimeIndex.date",
|
| 213 |
+
"198": "pandas.DatetimeIndex.day",
|
| 214 |
+
"199": "pandas.DatetimeIndex.day_name",
|
| 215 |
+
"200": "pandas.DatetimeIndex.dayofweek",
|
| 216 |
+
"201": "pandas.DatetimeIndex.difference",
|
| 217 |
+
"202": "pandas.DatetimeIndex.floor",
|
| 218 |
+
"203": "pandas.DatetimeIndex.freqstr",
|
| 219 |
+
"204": "pandas.DatetimeIndex.hour",
|
| 220 |
+
"205": "pandas.DatetimeIndex.indexer_at_time",
|
| 221 |
+
"206": "pandas.DatetimeIndex.indexer_between_time",
|
| 222 |
+
"207": "pandas.DatetimeIndex.minute",
|
| 223 |
+
"208": "pandas.DatetimeIndex.month",
|
| 224 |
+
"209": "pandas.DatetimeIndex.month_name",
|
| 225 |
+
"210": "pandas.DatetimeIndex.normalize",
|
| 226 |
+
"211": "pandas.DatetimeIndex.quarter",
|
| 227 |
+
"212": "pandas.DatetimeIndex.round",
|
| 228 |
+
"213": "pandas.DatetimeIndex.shift",
|
| 229 |
+
"214": "pandas.DatetimeIndex.strftime",
|
| 230 |
+
"215": "pandas.DatetimeIndex.time",
|
| 231 |
+
"216": "pandas.DatetimeIndex.to_period",
|
| 232 |
+
"217": "pandas.DatetimeIndex.to_pydatetime",
|
| 233 |
+
"218": "pandas.DatetimeIndex.to_series",
|
| 234 |
+
"219": "pandas.DatetimeIndex.tz_convert",
|
| 235 |
+
"220": "pandas.DatetimeIndex.tz_localize",
|
| 236 |
+
"221": "pandas.DatetimeIndex.weekday",
|
| 237 |
+
"222": "pandas.DatetimeIndex.year",
|
| 238 |
+
"223": "pandas.DatetimeTZDtype",
|
| 239 |
+
"224": "pandas.ExcelFile.parse",
|
| 240 |
+
"225": "pandas.ExcelWriter",
|
| 241 |
+
"226": "pandas.Grouper",
|
| 242 |
+
"227": "pandas.HDFStore.append",
|
| 243 |
+
"228": "pandas.Index",
|
| 244 |
+
"229": "pandas.Index.any",
|
| 245 |
+
"230": "pandas.Index.append",
|
| 246 |
+
"231": "pandas.Index.argsort",
|
| 247 |
+
"232": "pandas.Index.astype",
|
| 248 |
+
"233": "pandas.Index.difference",
|
| 249 |
+
"234": "pandas.Index.drop",
|
| 250 |
+
"235": "pandas.Index.drop_duplicates",
|
| 251 |
+
"236": "pandas.Index.droplevel",
|
| 252 |
+
"237": "pandas.Index.dropna",
|
| 253 |
+
"238": "pandas.Index.duplicated",
|
| 254 |
+
"239": "pandas.Index.fillna",
|
| 255 |
+
"240": "pandas.Index.get_indexer",
|
| 256 |
+
"241": "pandas.Index.get_indexer_for",
|
| 257 |
+
"242": "pandas.Index.get_level_values",
|
| 258 |
+
"243": "pandas.Index.get_loc",
|
| 259 |
+
"244": "pandas.Index.insert",
|
| 260 |
+
"245": "pandas.Index.intersection",
|
| 261 |
+
"246": "pandas.Index.is_monotonic_increasing",
|
| 262 |
+
"247": "pandas.Index.isin",
|
| 263 |
+
"248": "pandas.Index.item",
|
| 264 |
+
"249": "pandas.Index.map",
|
| 265 |
+
"250": "pandas.Index.max",
|
| 266 |
+
"251": "pandas.Index.name",
|
| 267 |
+
"252": "pandas.Index.reindex",
|
| 268 |
+
"253": "pandas.Index.rename",
|
| 269 |
+
"254": "pandas.Index.repeat",
|
| 270 |
+
"255": "pandas.Index.searchsorted",
|
| 271 |
+
"256": "pandas.Index.set_names",
|
| 272 |
+
"257": "pandas.Index.shift",
|
| 273 |
+
"258": "pandas.Index.size",
|
| 274 |
+
"259": "pandas.Index.slice_indexer",
|
| 275 |
+
"260": "pandas.Index.str",
|
| 276 |
+
"261": "pandas.Index.symmetric_difference",
|
| 277 |
+
"262": "pandas.Index.to_frame",
|
| 278 |
+
"263": "pandas.Index.to_list",
|
| 279 |
+
"264": "pandas.Index.to_series",
|
| 280 |
+
"265": "pandas.Index.tolist",
|
| 281 |
+
"266": "pandas.Index.union",
|
| 282 |
+
"267": "pandas.Index.unique",
|
| 283 |
+
"268": "pandas.Index.value_counts",
|
| 284 |
+
"269": "pandas.Index.values",
|
| 285 |
+
"270": "pandas.Index.where",
|
| 286 |
+
"271": "pandas.IndexSlice",
|
| 287 |
+
"272": "pandas.Interval",
|
| 288 |
+
"273": "pandas.IntervalIndex",
|
| 289 |
+
"274": "pandas.IntervalIndex.from_arrays",
|
| 290 |
+
"275": "pandas.IntervalIndex.from_breaks",
|
| 291 |
+
"276": "pandas.IntervalIndex.from_tuples",
|
| 292 |
+
"277": "pandas.IntervalIndex.overlaps",
|
| 293 |
+
"278": "pandas.MultiIndex",
|
| 294 |
+
"279": "pandas.MultiIndex.droplevel",
|
| 295 |
+
"280": "pandas.MultiIndex.from_arrays",
|
| 296 |
+
"281": "pandas.MultiIndex.from_frame",
|
| 297 |
+
"282": "pandas.MultiIndex.from_product",
|
| 298 |
+
"283": "pandas.MultiIndex.from_tuples",
|
| 299 |
+
"284": "pandas.MultiIndex.get_level_values",
|
| 300 |
+
"285": "pandas.MultiIndex.get_locs",
|
| 301 |
+
"286": "pandas.MultiIndex.isin",
|
| 302 |
+
"287": "pandas.MultiIndex.levels",
|
| 303 |
+
"288": "pandas.MultiIndex.nlevels",
|
| 304 |
+
"289": "pandas.MultiIndex.remove_unused_levels",
|
| 305 |
+
"290": "pandas.MultiIndex.reorder_levels",
|
| 306 |
+
"291": "pandas.MultiIndex.set_levels",
|
| 307 |
+
"292": "pandas.MultiIndex.swaplevel",
|
| 308 |
+
"293": "pandas.MultiIndex.to_flat_index",
|
| 309 |
+
"294": "pandas.MultiIndex.to_frame",
|
| 310 |
+
"295": "pandas.Period",
|
| 311 |
+
"296": "pandas.Period.qyear",
|
| 312 |
+
"297": "pandas.Period.strftime",
|
| 313 |
+
"298": "pandas.Period.to_timestamp",
|
| 314 |
+
"299": "pandas.PeriodIndex",
|
| 315 |
+
"300": "pandas.PeriodIndex.strftime",
|
| 316 |
+
"301": "pandas.PeriodIndex.to_timestamp",
|
| 317 |
+
"302": "pandas.RangeIndex",
|
| 318 |
+
"303": "pandas.Series",
|
| 319 |
+
"304": "pandas.Series.abs",
|
| 320 |
+
"305": "pandas.Series.add",
|
| 321 |
+
"306": "pandas.Series.add_prefix",
|
| 322 |
+
"307": "pandas.Series.agg",
|
| 323 |
+
"308": "pandas.Series.aggregate",
|
| 324 |
+
"309": "pandas.Series.align",
|
| 325 |
+
"310": "pandas.Series.all",
|
| 326 |
+
"311": "pandas.Series.any",
|
| 327 |
+
"312": "pandas.Series.apply",
|
| 328 |
+
"313": "pandas.Series.argmax",
|
| 329 |
+
"314": "pandas.Series.argsort",
|
| 330 |
+
"315": "pandas.Series.array",
|
| 331 |
+
"316": "pandas.Series.asfreq",
|
| 332 |
+
"317": "pandas.Series.asof",
|
| 333 |
+
"318": "pandas.Series.astype",
|
| 334 |
+
"319": "pandas.Series.at",
|
| 335 |
+
"320": "pandas.Series.autocorr",
|
| 336 |
+
"321": "pandas.Series.between",
|
| 337 |
+
"322": "pandas.Series.bfill",
|
| 338 |
+
"323": "pandas.Series.case_when",
|
| 339 |
+
"324": "pandas.Series.cat.add_categories",
|
| 340 |
+
"325": "pandas.Series.cat.categories",
|
| 341 |
+
"326": "pandas.Series.cat.codes",
|
| 342 |
+
"327": "pandas.Series.cat.remove_unused_categories",
|
| 343 |
+
"328": "pandas.Series.cat.rename_categories",
|
| 344 |
+
"329": "pandas.Series.clip",
|
| 345 |
+
"330": "pandas.Series.combine",
|
| 346 |
+
"331": "pandas.Series.combine_first",
|
| 347 |
+
"332": "pandas.Series.compare",
|
| 348 |
+
"333": "pandas.Series.convert_dtypes",
|
| 349 |
+
"334": "pandas.Series.copy",
|
| 350 |
+
"335": "pandas.Series.corr",
|
| 351 |
+
"336": "pandas.Series.count",
|
| 352 |
+
"337": "pandas.Series.cummax",
|
| 353 |
+
"338": "pandas.Series.cummin",
|
| 354 |
+
"339": "pandas.Series.cumprod",
|
| 355 |
+
"340": "pandas.Series.cumsum",
|
| 356 |
+
"341": "pandas.Series.describe",
|
| 357 |
+
"342": "pandas.Series.diff",
|
| 358 |
+
"343": "pandas.Series.div",
|
| 359 |
+
"344": "pandas.Series.divmod",
|
| 360 |
+
"345": "pandas.Series.dot",
|
| 361 |
+
"346": "pandas.Series.drop",
|
| 362 |
+
"347": "pandas.Series.drop_duplicates",
|
| 363 |
+
"348": "pandas.Series.droplevel",
|
| 364 |
+
"349": "pandas.Series.dropna",
|
| 365 |
+
"350": "pandas.Series.dt",
|
| 366 |
+
"351": "pandas.Series.dt.ceil",
|
| 367 |
+
"352": "pandas.Series.dt.components",
|
| 368 |
+
"353": "pandas.Series.dt.date",
|
| 369 |
+
"354": "pandas.Series.dt.day",
|
| 370 |
+
"355": "pandas.Series.dt.day_name",
|
| 371 |
+
"356": "pandas.Series.dt.dayofweek",
|
| 372 |
+
"357": "pandas.Series.dt.dayofyear",
|
| 373 |
+
"358": "pandas.Series.dt.days",
|
| 374 |
+
"359": "pandas.Series.dt.daysinmonth",
|
| 375 |
+
"360": "pandas.Series.dt.floor",
|
| 376 |
+
"361": "pandas.Series.dt.hour",
|
| 377 |
+
"362": "pandas.Series.dt.minute",
|
| 378 |
+
"363": "pandas.Series.dt.month",
|
| 379 |
+
"364": "pandas.Series.dt.month_name",
|
| 380 |
+
"365": "pandas.Series.dt.normalize",
|
| 381 |
+
"366": "pandas.Series.dt.quarter",
|
| 382 |
+
"367": "pandas.Series.dt.round",
|
| 383 |
+
"368": "pandas.Series.dt.second",
|
| 384 |
+
"369": "pandas.Series.dt.seconds",
|
| 385 |
+
"370": "pandas.Series.dt.strftime",
|
| 386 |
+
"371": "pandas.Series.dt.time",
|
| 387 |
+
"372": "pandas.Series.dt.to_period",
|
| 388 |
+
"373": "pandas.Series.dt.to_pydatetime",
|
| 389 |
+
"374": "pandas.Series.dt.total_seconds",
|
| 390 |
+
"375": "pandas.Series.dt.tz_convert",
|
| 391 |
+
"376": "pandas.Series.dt.tz_localize",
|
| 392 |
+
"377": "pandas.Series.dt.weekday",
|
| 393 |
+
"378": "pandas.Series.dt.year",
|
| 394 |
+
"379": "pandas.Series.dtype",
|
| 395 |
+
"380": "pandas.Series.duplicated",
|
| 396 |
+
"381": "pandas.Series.empty",
|
| 397 |
+
"382": "pandas.Series.eq",
|
| 398 |
+
"383": "pandas.Series.equals",
|
| 399 |
+
"384": "pandas.Series.ewm",
|
| 400 |
+
"385": "pandas.Series.expanding",
|
| 401 |
+
"386": "pandas.Series.explode",
|
| 402 |
+
"387": "pandas.Series.factorize",
|
| 403 |
+
"388": "pandas.Series.ffill",
|
| 404 |
+
"389": "pandas.Series.fillna",
|
| 405 |
+
"390": "pandas.Series.first_valid_index",
|
| 406 |
+
"391": "pandas.Series.floordiv",
|
| 407 |
+
"392": "pandas.Series.ge",
|
| 408 |
+
"393": "pandas.Series.get",
|
| 409 |
+
"394": "pandas.Series.groupby",
|
| 410 |
+
"395": "pandas.Series.gt",
|
| 411 |
+
"396": "pandas.Series.head",
|
| 412 |
+
"397": "pandas.Series.hist",
|
| 413 |
+
"398": "pandas.Series.iat",
|
| 414 |
+
"399": "pandas.Series.idxmax",
|
| 415 |
+
"400": "pandas.Series.idxmin",
|
| 416 |
+
"401": "pandas.Series.iloc",
|
| 417 |
+
"402": "pandas.Series.index",
|
| 418 |
+
"403": "pandas.Series.interpolate",
|
| 419 |
+
"404": "pandas.Series.is_monotonic_increasing",
|
| 420 |
+
"405": "pandas.Series.is_unique",
|
| 421 |
+
"406": "pandas.Series.isin",
|
| 422 |
+
"407": "pandas.Series.isna",
|
| 423 |
+
"408": "pandas.Series.isnull",
|
| 424 |
+
"409": "pandas.Series.item",
|
| 425 |
+
"410": "pandas.Series.items",
|
| 426 |
+
"411": "pandas.Series.last_valid_index",
|
| 427 |
+
"412": "pandas.Series.le",
|
| 428 |
+
"413": "pandas.Series.loc",
|
| 429 |
+
"414": "pandas.Series.lt",
|
| 430 |
+
"415": "pandas.Series.map",
|
| 431 |
+
"416": "pandas.Series.mask",
|
| 432 |
+
"417": "pandas.Series.max",
|
| 433 |
+
"418": "pandas.Series.mean",
|
| 434 |
+
"419": "pandas.Series.median",
|
| 435 |
+
"420": "pandas.Series.min",
|
| 436 |
+
"421": "pandas.Series.mod",
|
| 437 |
+
"422": "pandas.Series.mode",
|
| 438 |
+
"423": "pandas.Series.mul",
|
| 439 |
+
"424": "pandas.Series.name",
|
| 440 |
+
"425": "pandas.Series.ne",
|
| 441 |
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|
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|
| 1389 |
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|
| 1390 |
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|
| 1391 |
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|
| 1392 |
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|
| 1393 |
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|
| 1394 |
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|
| 1395 |
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|
| 1397 |
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|
| 1398 |
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|
| 1399 |
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|
| 1400 |
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|
| 1401 |
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|
| 1402 |
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|
| 1403 |
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|
| 1404 |
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|
| 1405 |
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|
| 1406 |
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|
| 1407 |
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|
| 1408 |
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|
| 1409 |
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|
| 1410 |
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|
| 1411 |
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|
| 1412 |
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|
| 1413 |
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|
| 1414 |
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}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:a1c8594d24032bf05bd1731cdc14ec098468b1d95ecc7d7840ccb7f2f5e14e43
|
| 3 |
+
size 330611644
|
tokenizer.json
ADDED
|
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|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"backend": "tokenizers",
|
| 4 |
+
"bos_token": "<s>",
|
| 5 |
+
"cls_token": "<s>",
|
| 6 |
+
"eos_token": "</s>",
|
| 7 |
+
"errors": "replace",
|
| 8 |
+
"is_local": false,
|
| 9 |
+
"local_files_only": false,
|
| 10 |
+
"mask_token": "<mask>",
|
| 11 |
+
"model_max_length": 512,
|
| 12 |
+
"pad_token": "<pad>",
|
| 13 |
+
"sep_token": "</s>",
|
| 14 |
+
"tokenizer_class": "RobertaTokenizer",
|
| 15 |
+
"trim_offsets": true,
|
| 16 |
+
"unk_token": "<unk>"
|
| 17 |
+
}
|