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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
import csv
import json
import os
import datasets
_CITATION = """"""
_DESCRIPTION = """This new dataset is designed to measure Language Models abstractness and inclusiveness understanding in Italian."""
_HOMEPAGE = ""
_LICENSE = "CC BY 4.0"
_URLS = {
"abs": "https://raw.githubusercontent.com/aramelior/ABRICOT-ABstRactness-and-Inclusiveness-in-COntexT/main/dataset_it.csv"
}
class abricot(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="abs", version=VERSION, description="Abstraction assessment"),
# datasets.BuilderConfig(name="ita", version=VERSION, description="Italian Understanding"),
]
DEFAULT_CONFIG_NAME = "abs"
def _info(self):
if self.config.name == "abs":
features = datasets.Features(
# TODO: add after the image col is there "immagine": datasets.Value("string"),
{
"ID": datasets.Value("string"),
"domain": datasets.Value("string"),
"begin": datasets.Value("int64"),
"end": datasets.Value("int64"),
"text": datasets.Value("string"),
"target_token": datasets.Value("string"),
"target_lemma": datasets.Value("string"),
"inc_mean": datasets.Value("float"),
"inc_std": datasets.Value("float"),
"abs_mean": datasets.Value("float"),
"abs_std": datasets.Value("float"),
"target_number": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS[self.config.name]
# data_dir = dl_manager.extract(urls)
# if self.config.name == "abs":
# data_file = "dataset_it.csv"
data_file = dl_manager.download(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_file,
"split": "val",
},
),
]
def _generate_examples(self, filepath, split):
ds = datasets.load_dataset("csv", data_files=filepath)["train"]
for key, row in enumerate(ds):
# data = json.loads(row)
if self.config.name == "abs":
# Yields examples as (key, example) tuples
out = {
"ID": row["ID"],
"domain": row["domain"],
"begin": row["begin"],
"end": row["end"],
"text": row["text"],
"target_token": row["target_token"],
"target_lemma": row["target_lemma"],
"inc_mean": row["inc_mean"],
"inc_std": row["inc_std"],
"abs_mean": row["abs_mean"],
"abs_std": row["abs_std"],
"target_number": row["target_number"],
}
yield key, out
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