Datasets:
Tasks:
Text Classification
Formats:
csv
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
License:
Francisco Castillo
commited on
Commit
•
cb52154
1
Parent(s):
fc6af0b
wip
Browse files- reviews_with_drift.py +11 -10
reviews_with_drift.py
CHANGED
@@ -106,13 +106,14 @@ class NewDataset(datasets.GeneratorBasedBuilder):
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features=features, # Here we define them above because they are different between the two configurations
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# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
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# specify them. They'll be used if as_supervised=True in builder.as_dataset.
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-
supervised_keys=("text", "label"),
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# Homepage of the dataset for documentation
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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-
task_templates=[TextClassification(text_column="text", label_column="label")],
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)
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def _split_generators(self, dl_manager):
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@@ -122,29 +123,29 @@ class NewDataset(datasets.GeneratorBasedBuilder):
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# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
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# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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-
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return [
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datasets.SplitGenerator(
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-
name="training",
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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-
"
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"split": "training",
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},
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),
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datasets.SplitGenerator(
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-
name=
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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-
"
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"split": "validation"
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},
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),
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datasets.SplitGenerator(
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name="production",
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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-
"
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"split": "production",
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},
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),
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features=features, # Here we define them above because they are different between the two configurations
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# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
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# specify them. They'll be used if as_supervised=True in builder.as_dataset.
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+
# supervised_keys=("text", "label"),
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+
supervised_keys=None,
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# Homepage of the dataset for documentation
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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+
# task_templates=[TextClassification(text_column="text", label_column="label")],
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)
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def _split_generators(self, dl_manager):
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# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
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# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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+
archive = dl_manager.download(_URLS)
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return [
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datasets.SplitGenerator(
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+
name=datasets.Split("training"),
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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+
"files": dl_manager.iter_archive(archive),
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"split": "training",
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},
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),
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datasets.SplitGenerator(
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+
name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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+
"files": dl_manager.iter_archive(archive),
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+
"split": "validation",
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},
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),
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datasets.SplitGenerator(
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+
name=datasets.Split("production"),
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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+
"files": dl_manager.iter_archive(archive),
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"split": "production",
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},
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),
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