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diwank
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silicone-merged

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silicone-merged / silicone-merged.py

Diwank Singh

Balanced dataset

41f5e2c about 2 years ago

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	# silicone-merged.py

	## About: This is a dataset script for diwank/silicone-merged
	## Docs: https://huggingface.co/docs/datasets/dataset_script.html


	"""Merged and simplified dialog act datasets from the silicone collection."""


	import pandas as pd
	import os

	import datasets

	_DESCRIPTION = """\
	Merged and simplified dialog act datasets from the silicone collection.
	"""

	_HOMEPAGE = "https://huggingface.co/datasets/diwank/silicone-merged"
	_LICENSE = "MIT"

	_URLS = {
	"default": dict(
	train="https://huggingface.co/datasets/diwank/silicone-merged/resolve/main/train.h5",
	validation="https://huggingface.co/datasets/diwank/silicone-merged/resolve/main/validation.h5",
	test="https://huggingface.co/datasets/diwank/silicone-merged/resolve/main/test.h5",
	),
	"balanced": dict(
	train="https://huggingface.co/datasets/diwank/silicone-merged/resolve/main/balanced.h5",
	validation="https://huggingface.co/datasets/diwank/silicone-merged/resolve/main/validation.h5",
	test="https://huggingface.co/datasets/diwank/silicone-merged/resolve/main/test.h5",
	)

	}

	class SiliconeMergedDataset(datasets.GeneratorBasedBuilder):
	"""Merged and simplified dialog act datasets from the silicone collection."""

	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="default", version=VERSION, description="Default config"),
	datasets.BuilderConfig(name="balanced", version=VERSION, description="Balanced dataset config"),
	]

	DEFAULT_CONFIG_NAME = "default"


	def _info(self):

	features = datasets.Features(
	{
	"text_a": datasets.Value("string"),
	"text_b": datasets.Value("string"),
	"labels": datasets.Value("int64")
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=("text_b", "labels"),
	homepage=_HOMEPAGE,
	license=_LICENSE,
	)

	def _split_generators(self, dl_manager):
	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
	# 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.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	urls = _URLS[self.config.name]
	filepaths = dl_manager.download_and_extract(urls)
	return [
	datasets.SplitGenerator(
	name=getattr(datasets.Split, split.upper()),
	# These kwargs will be passed to _generate_examples
	gen_kwargs=dict(
	filepath=filepaths[split],
	split=split,
	),
	)
	for split in ["train", "test", "validation"]
	]

	def _generate_examples(self, filepath, split):
	df = pd.read_hdf(filepath, "data")

	for key, tuple in enumerate(df.itertuples(index=False)):
	yield key, tuple._asdict()