Datasets:
Tasks:
Text Classification
Formats:
csv
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
License:
Francisco Castillo
commited on
Commit
•
bdc2a7b
1
Parent(s):
cb52154
wip
Browse files- reviews_with_drift.py +46 -12
reviews_with_drift.py
CHANGED
@@ -106,14 +106,13 @@ 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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-
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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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-
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)
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def _split_generators(self, dl_manager):
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@@ -123,39 +122,74 @@ 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=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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"
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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
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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=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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"
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"split": "production",
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},
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),
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]
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, filepath, split):
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# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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with open(filepath, encoding="utf-8") as f:
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-
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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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# 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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extracted_paths = dl_manager.download_and_extract(_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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"filepath": extracted_paths['training'],
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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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"filepath": extracted_paths['validation'],
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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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"filepath": extracted_paths['production'],
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"split": "production",
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},
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),
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]
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# def _split_generators(self, dl_manager):
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# # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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# # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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+
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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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# ]
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+
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, filepath, split):
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# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
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with open(filepath, encoding="utf-8") as f:
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yield {
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"text": f.read().decode("utf-8")
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}
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