fernando-peres
commited on
Commit
·
bab7bc3
1
Parent(s):
98c3f82
resolving card bugs
Browse files- py_legislation.py +9 -8
py_legislation.py
CHANGED
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@@ -134,9 +134,10 @@ _metadata = {
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"homepage": "https://www.leyes.com.py/",
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"license": "apache-2.0",
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-
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-
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"raw_text": {
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"description": textwrap.dedent("""
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Data extracted from the sources files (URls, PDFs and Word files) without any transformation or sentence splitter. It can be helpful because you can access the raw data extracted from the seeds (PDFs and Word files) and apply other preprocessing tasks from this point to prepare the data without returning to extract texts from source files.
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@@ -215,19 +216,19 @@ class PYLegislation(datasets.GeneratorBasedBuilder):
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datasets.BuilderConfig(
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name="raw_text",
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version=VERSION,
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description=
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),
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datasets.BuilderConfig(
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name="unlabeled_sentences",
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version=VERSION,
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description=
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),
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# datasets.BuilderConfig(
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# name="labeled_sentences",
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# version=VERSION,
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# description=
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# ),
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]
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@@ -242,9 +243,9 @@ class PYLegislation(datasets.GeneratorBasedBuilder):
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"""
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features = None
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description = None
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if self.config.name in
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features = datasets.Features(
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description =
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# if self.config.name == "raw_text":
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# description = _metadata["raw_text"]["description"]
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"homepage": "https://www.leyes.com.py/",
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"license": "apache-2.0",
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+
}
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# [@] Config Names:
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_CONFIGS = {
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"raw_text": {
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"description": textwrap.dedent("""
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Data extracted from the sources files (URls, PDFs and Word files) without any transformation or sentence splitter. It can be helpful because you can access the raw data extracted from the seeds (PDFs and Word files) and apply other preprocessing tasks from this point to prepare the data without returning to extract texts from source files.
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datasets.BuilderConfig(
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name="raw_text",
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version=VERSION,
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description=_CONFIGS["raw_text"]["description"],
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),
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datasets.BuilderConfig(
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name="unlabeled_sentences",
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version=VERSION,
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description=_CONFIGS["unlabeled_sentences"]["description"],
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),
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# datasets.BuilderConfig(
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# name="labeled_sentences",
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# version=VERSION,
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# description=_CONFIGS["labeled_sentences"]["description"],
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# ),
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]
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"""
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features = None
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description = None
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if self.config.name in _CONFIGS.keys():
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features = datasets.Features(_CONFIGS[self.config.name]["features"])
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description =_CONFIGS[self.config.name]["description"]
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# if self.config.name == "raw_text":
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# description = _metadata["raw_text"]["description"]
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