Spaces:
Runtime error
Runtime error
File size: 11,396 Bytes
7bffaaf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from os.path import exists
from os.path import join as pjoin
from datasets import Dataset, load_dataset, load_from_disk
from tqdm import tqdm
_CACHE_DIR = "cache_dir"
# grab first N rows of a dataset from the hub
def load_truncated_dataset(
dataset_name,
config_name=None,
split_name=None,
num_rows=0,
use_streaming=True,
use_auth_token=None,
use_dataset=None,
):
"""
This function loads the first `num_rows` items of a dataset for a
given `config_name` and `split_name`.
When the dataset is streamable, we iterate through the first
`num_rows` examples in streaming mode, write them to a jsonl file,
then create a new dataset from the json.
This is the most direct way to make a Dataset from an IterableDataset
as of datasets version 1.6.1.
Otherwise, we download the full dataset and select the first
`num_rows` items
Args:
dataset_name (string):
dataset id in the dataset library
config_name (string):
dataset configuration
split_name (string):
optional split name, defaults to `train`
num_rows (int):
number of rows to truncate the dataset to, <= 0 means no truncation
use_streaming (bool):
whether to use streaming when the dataset supports it
use_auth_token (string):
HF authentication token to access private datasets
use_dataset (Dataset):
use existing dataset instead of getting one from the hub
Returns:
Dataset:
the truncated dataset as a Dataset object
"""
split_name = "train" if split_name is None else split_name
cache_name = f"{dataset_name.replace('/', '---')}_{'default' if config_name is None else config_name}_{split_name}_{num_rows}"
if use_streaming:
if not exists(pjoin(_CACHE_DIR, "tmp", f"{cache_name}.jsonl")):
iterable_dataset = (
load_dataset(
dataset_name,
name=config_name,
split=split_name,
cache_dir=pjoin(_CACHE_DIR, "tmp", cache_name + "_temp"),
streaming=True,
use_auth_token=use_auth_token,
)
if use_dataset is None
else use_dataset
)
if num_rows > 0:
iterable_dataset = iterable_dataset.take(num_rows)
f = open(
pjoin(_CACHE_DIR, "tmp", f"{cache_name}.jsonl"), "w", encoding="utf-8"
)
for row in tqdm(iterable_dataset):
_ = f.write(json.dumps(row) + "\n")
f.close()
dataset = Dataset.from_json(
pjoin(_CACHE_DIR, "tmp", f"{cache_name}.jsonl"),
cache_dir=pjoin(_CACHE_DIR, "tmp", cache_name + "_jsonl"),
)
else:
full_dataset = (
load_dataset(
dataset_name,
name=config_name,
split=split_name,
use_auth_token=use_auth_token,
cache_dir=pjoin(_CACHE_DIR, "tmp", cache_name + "_temp"),
)
if use_dataset is None
else use_dataset
)
if num_rows > 0:
dataset = full_dataset.select(range(num_rows))
else:
dataset = full_dataset
return dataset
# get all instances of a specific field in a dataset with indices and labels
def extract_features(examples, indices, input_field_path, label_name=None):
"""
This function prepares examples for further processing by:
- returning an "unrolled" list of all the fields denoted by input_field_path
- with the indices corresponding to the example the field item came from
- optionally, the corresponding label is also returned with each field item
Args:
examples (dict):
a dictionary of lists, provided dataset.map with batched=True
indices (list):
a list of indices, provided dataset.map with with_indices=True
input_field_path (tuple):
a tuple indicating the field we want to extract. Can be a singleton
for top-level features (e.g. `("text",)`) or a full path for nested
features (e.g. `("answers", "text")`) to get all answer strings in
SQuAD
label_name (string):
optionally used to align the field items with labels. Currently,
returns the top-most field that has this name, which may fail in some
edge cases
TODO: make it so the label is specified through a full path
Returns:
Dict:
a dictionary of lists, used by dataset.map with batched=True.
labels are all None if label_name!=None but label_name is not found
TODO: raised an error if label_name is specified but not found
"""
top_name = input_field_path[0]
if label_name is not None and label_name in examples:
item_list = [
{"index": i, "label": label, "items": items}
for i, items, label in zip(
indices, examples[top_name], examples[label_name]
)
]
else:
item_list = [
{"index": i, "label": None, "items": items}
for i, items in zip(indices, examples[top_name])
]
for field_name in input_field_path[1:]:
new_item_list = []
for dct in item_list:
if label_name is not None and label_name in dct["items"]:
if isinstance(dct["items"][field_name], list):
new_item_list += [
{"index": dct["index"], "label": label, "items": next_item}
for next_item, label in zip(
dct["items"][field_name], dct["items"][label_name]
)
]
else:
new_item_list += [
{
"index": dct["index"],
"label": dct["items"][label_name],
"items": dct["items"][field_name],
}
]
else:
if isinstance(dct["items"][field_name], list):
new_item_list += [
{
"index": dct["index"],
"label": dct["label"],
"items": next_item,
}
for next_item in dct["items"][field_name]
]
else:
new_item_list += [
{
"index": dct["index"],
"label": dct["label"],
"items": dct["items"][field_name],
}
]
item_list = new_item_list
res = (
{
"ids": [dct["index"] for dct in item_list],
"field": [dct["items"] for dct in item_list],
}
if label_name is None
else {
"ids": [dct["index"] for dct in item_list],
"field": [dct["items"] for dct in item_list],
"label": [dct["label"] for dct in item_list],
}
)
return res
# grab some examples and extract interesting fields
def prepare_clustering_dataset(
dataset_name,
input_field_path,
label_name=None,
config_name=None,
split_name=None,
num_rows=0,
use_streaming=True,
use_auth_token=None,
cache_dir=_CACHE_DIR,
use_dataset=None,
):
"""
This function loads the first `num_rows` items of a dataset for a
given `config_name` and `split_name`, and extracts all instances of a field
of interest denoted by `input_field_path` along with the indices of the
examples the instances came from and optionall their labels (`label_name`)
in the original dataset
Args:
dataset_name (string):
dataset id in the dataset library
input_field_path (tuple):
a tuple indicating the field we want to extract. Can be a singleton
for top-level features (e.g. `("text",)`) or a full path for nested
features (e.g. `("answers", "text")`) to get all answer strings in
SQuAD
label_name (string):
optionally used to align the field items with labels. Currently,
returns the top-most field that has this name, which fails in edge cases
config_name (string):
dataset configuration
split_name (string):
optional split name, defaults to `train`
num_rows (int):
number of rows to truncate the dataset to, <= 0 means no truncation
use_streaming (bool):
whether to use streaming when the dataset supports it
use_auth_token (string):
HF authentication token to access private datasets
use_dataset (Dataset):
use existing dataset instead of getting one from the hub
Returns:
Dataset:
the extracted dataset as a Dataset object. Note that if there is more
than one instance of the field per example in the original dataset
(e.g. multiple answers per QA example), the returned dataset will
have more than `num_rows` rows
string:
the path to the newsly created dataset directory
"""
cache_path = [
cache_dir,
dataset_name.replace("/", "---"),
f"{'default' if config_name is None else config_name}",
f"{'train' if split_name is None else split_name}",
f"field-{'->'.join(input_field_path)}-label-{label_name}",
f"{num_rows}_rows",
"features_dset",
]
if exists(pjoin(*cache_path)):
pre_clustering_dset = load_from_disk(pjoin(*cache_path))
else:
truncated_dset = load_truncated_dataset(
dataset_name,
config_name,
split_name,
num_rows,
use_streaming,
use_auth_token,
use_dataset,
)
def batch_func(examples, indices):
return extract_features(examples, indices, input_field_path, label_name)
pre_clustering_dset = truncated_dset.map(
batch_func,
remove_columns=truncated_dset.features,
batched=True,
with_indices=True,
)
for i in range(1, len(cache_path) - 1):
if not exists(pjoin(*cache_path[:i])):
os.mkdir(pjoin(*cache_path[:i]))
pre_clustering_dset.save_to_disk(pjoin(*cache_path))
return pre_clustering_dset, pjoin(*cache_path)
|