Datasets:
Tasks:
Text Classification
Sub-tasks:
natural-language-inference
Languages:
English
Size:
10K - 100K
License:
# coding=utf-8 | |
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
# | |
# 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. | |
# Lint as: python3 | |
"""OSGD-CD: The OSDG Community Dataset.""" | |
import csv | |
import json | |
import datasets | |
from datasets.tasks import TextClassification | |
logger = datasets.logging.get_logger(__name__) | |
_CITATION = """\ | |
@dataset{osdg_2023_8397907, | |
author = {OSDG and | |
UNDP IICPSD SDG AI Lab and | |
PPMI}, | |
title = {OSDG Community Dataset (OSDG-CD)}, | |
month = oct, | |
year = 2023, | |
note = {{This CSV file uses UTF-8 character encoding. For | |
easy access on MS Excel, open the file using Data | |
→ From Text/CSV. Please split CSV data into | |
different columns by using a TAB delimiter.}}, | |
publisher = {Zenodo}, | |
version = {2023.10}, | |
doi = {10.5281/zenodo.8397907}, | |
url = {https://doi.org/10.5281/zenodo.8397907} | |
} | |
""" | |
_HOMEPAGE = "https://zenodo.org/record/8397907" | |
_LICENSE = "https://creativecommons.org/licenses/by/4.0/" | |
_DESCRIPTION = """\ | |
The OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, \ | |
which were validated by approximately 1,000 OSDG Community Platform (OSDG-CP) \ | |
citizen scientists from over 110 countries, with respect to the Sustainable Development Goals (SDGs). | |
""" | |
_VERSIONS = { | |
"2021.09": "1.0.0", | |
"2022.01": "1.0.1", | |
"2022.04": "1.0.2", | |
"2022.07": "1.0.3", | |
"2022.10": "1.0.4", | |
"2023.01": "1.0.5", | |
"2023.04": "1.0.6", | |
"2023.07": "1.0.7", | |
"2023.10": "1.0.8", | |
} | |
_VERSION = _VERSIONS["2023.10"] | |
_URLS = { | |
#"train": "https://zenodo.org/record/8107038/files/osdg-community-data-v2023-07-01.csv", | |
"train": "https://zenodo.org/record/8397907/files/osdg-community-data-v2023-10-01.csv", | |
} | |
class OSDGCDConfig(datasets.BuilderConfig): | |
"""BuilderConfig for OSDG-CD.""" | |
def __init__(self, **kwargs): | |
"""BuilderConfig for OSDG-CD. | |
Args: | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(OSDGCDConfig, self).__init__(**kwargs) | |
class OSDGCD(datasets.GeneratorBasedBuilder): | |
"""OSDG-CD: The OSDG Community Dataset (OSDG-CD)""" | |
# This is an example of a dataset with multiple configurations. | |
# If you don't want/need to define several sub-sets in your dataset, | |
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
# If you need to make complex sub-parts in the datasets with configurable options | |
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
# BUILDER_CONFIG_CLASS = MyBuilderConfig | |
# You will be able to load one or the other configurations in the following list with | |
# data = datasets.load_dataset('my_dataset', 'first_domain') | |
# data = datasets.load_dataset('my_dataset', 'second_domain') | |
BUILDER_CONFIGS = [ | |
OSDGCDConfig( | |
name="main_config", | |
version=datasets.Version(_VERSION, ""), | |
description="Main configuration", | |
), | |
] | |
DEFAULT_CONFIG_NAME = "main_config" | |
def _info(self) -> datasets.DatasetInfo: | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
license=_LICENSE, | |
features=datasets.Features( | |
{ | |
"doi": datasets.Value("string"), | |
"text_id": datasets.Value("string"), | |
"text": datasets.Value("string"), | |
"sdg": datasets.Value("uint16"), | |
"label": datasets.ClassLabel(num_classes=16, names=[f"SDG {sdg}" for sdg in range(1, 17)]), | |
"labels_negative": datasets.Value("uint16"), | |
"labels_positive": datasets.Value("uint16"), | |
"agreement": datasets.Value("float"), | |
} | |
), | |
homepage=_HOMEPAGE, | |
citation=_CITATION, | |
task_templates=[ | |
TextClassification( | |
text_column="text", label_column="label", | |
) | |
], | |
) | |
def _split_generators(self, dl_manager): | |
downloaded_files = dl_manager.download_and_extract(_URLS) | |
return [ | |
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), | |
] | |
def _generate_examples(self, filepath): | |
"""This function returns the examples in the raw (text) form.""" | |
logger.info("generating examples from = %s", filepath) | |
with open(filepath, encoding="utf-8") as f: | |
osdg = csv.DictReader(f, delimiter="\t") | |
for row in osdg: | |
id_ = row["text_id"] | |
sdg = int(row["sdg"]) | |
row["label"] = f"SDG {sdg}" | |
yield id_, row | |