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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() |