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"""The ETHICS dataset is a benchmark that spans concepts in justice, well-being, duties, virtues, and commonsense morality."""
BUILDER_CONFIGS=[
EthicsConfig(
name="commonsense",
prefix="cm",
features=datasets.Features(
{
"label":datasets.Value("int32"),
"input":datasets.Value("string"),
"is_short":datasets.Value("bool"),
"edited":datasets.Value("bool"),
}
),
description="The Commonsense subset contains examples focusing on moral standards and principles that most people intuitively accept.",
),
EthicsConfig(
name="deontology",
prefix="deontology",
features=datasets.Features(
{
"group_id":datasets.Value("int32"),
"label":datasets.Value("int32"),
"scenario":datasets.Value("string"),
"excuse":datasets.Value("string"),
}
),
description="The Deontology subset contains examples focusing on whether an act is required, permitted, or forbidden according to a set of rules or constraints",
),
EthicsConfig(
name="justice",
prefix="justice",
features=datasets.Features(
{
"group_id":datasets.Value("int32"),
"label":datasets.Value("int32"),
"scenario":datasets.Value("string"),
}
),
description="The Justice subset contains examples focusing on how a character treats another person",
),
EthicsConfig(
name="utilitarianism",
prefix="util",
features=datasets.Features(
{
"activity":datasets.Value("string"),
"baseline":datasets.Value("string"),
"rating":datasets.Value("string"),# Empty rating.
}
),
description="The Utilitarianism subset contains scenarios that should be ranked from most pleasant to least pleasant for the person in the scenario",
),
EthicsConfig(
name="virtue",
prefix="virtue",
features=datasets.Features(
{
"group_id":datasets.Value("int32"),
"label":datasets.Value("int32"),
"scenario":datasets.Value("string"),
"trait":datasets.Value("string"),
}
),
description="The Virtue subset contains scenarios focusing on whether virtues or vices are being exemplified",
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# 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.
"""Pile dataset."""
importjson
importdatasets
_CITATION="""\
@article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
"""
_DESCRIPTION="""\
The Pile is a 825 GiB diverse, open source language modeling data set that consists
of 22 smaller, high-quality datasets combined together. To score well on Pile
BPB (bits per byte), a model must be able to understand many disparate domains
including books, github repositories, webpages, chat logs, and medical, physics,
math, computer science, and philosophy papers.
"""
_HOMEPAGE="https://pile.eleuther.ai/"
# TODO: Add the licence for the dataset here if you can find it
{"quac":{"description":"Question Answering in Context (QuAC) is a dataset for modeling, understanding, and \nparticipating in information seeking dialog. Data instances consist of an interactive\ndialog between two crowd workers: (1) a student who poses a sequence of freeform\nquestions to learn as much as possible about a hidden Wikipedia text, and (2)\na teacher who answers the questions by providing short excerpts (spans) from the text.\n","citation":"@article{choi2018quac,\n title={Quac: Question answering in context},\n author={Choi, Eunsol and He, He and Iyyer, Mohit and Yatskar, Mark and Yih, Wen-tau and Choi, Yejin and Liang, Percy and Zettlemoyer, Luke},\n journal={arXiv preprint arXiv:1808.07036},\n year={2018}\n}\n","homepage":"https://quac.ai/","license":"","features":{"title":{"dtype":"string","id":null,"_type":"Value"},"section_title":{"dtype":"string","id":null,"_type":"Value"},"paragraph":{"dtype":"string","id":null,"_type":"Value"},"question":{"dtype":"string","id":null,"_type":"Value"},"answer":{"dtype":"string","id":null,"_type":"Value"}},"post_processed":null,"supervised_keys":null,"task_templates":null,"builder_name":"quac","config_name":"quac","version":{"version_str":"1.1.0","description":null,"major":1,"minor":1,"patch":0},"splits":{"train":{"name":"train","num_bytes":212391958,"num_examples":83568,"dataset_name":"quac"},"validation":{"name":"validation","num_bytes":20678483,"num_examples":7354,"dataset_name":"quac"}},"download_checksums":{"https://s3.amazonaws.com/my89public/quac/train_v0.2.json":{"num_bytes":68114819,"checksum":"ff5cca5a2e4b4d1cb5b5ced68b9fce88394ef6d93117426d6d4baafbcc05c56a"},"https://s3.amazonaws.com/my89public/quac/val_v0.2.json":{"num_bytes":8929167,"checksum":"09e622916280ba04c9352acb1bc5bbe80f11a2598f6f34e934c51d9e6570f378"}},"download_size":77043986,"post_processing_size":null,"dataset_size":233070441,"size_in_bytes":310114427}}
