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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.
"""LogiQA dataset."""
import datasets
import json
import ast
import pandas as pd
import csv
_CITATION = """\
@ARTICLE{10174688,
author={Liu, Hanmeng and Liu, Jian and Cui, Leyang and Teng, Zhiyang and Duan, Nan and Zhou, Ming and Zhang, Yue},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
title={LogiQA 2.0 — An Improved Dataset for Logical Reasoning in Natural Language Understanding},
year={2023},
volume={},
number={},
pages={1-16},
doi={10.1109/TASLP.2023.3293046}}
"""
_HOMEPAGE = "https://github.com/csitfun/LogiQA2.0/tree/main"
_LICENSE = (
"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License"
)
HEAD = 'https://raw.githubusercontent.com/ruixiangcui/AGIEval/main/data/v1/'
_DESCRIPTION = "AGIEval is a human-centric benchmark specifically designed to evaluate the general abilities of foundation models in tasks pertinent to human cognition and problem-solving. This benchmark is derived from 20 official, public, and high-standard admission and qualification exams intended for general human test-takers, such as general college admission tests"
_URLS = {
"sat_en": {
"test": HEAD + 'sat-en.jsonl',
},
"sat_en_wop": {
"test": HEAD + 'sat-en-without-passage.jsonl',
},
"sat_math": {
"test": HEAD + 'sat-math.jsonl'
},
"lsat_ar": {
"test": HEAD + 'lsat-ar.jsonl'
},
"lsat_lr": {
"test": HEAD + 'lsat-lr.jsonl'
},
"lsat_rc": {
"test": HEAD + 'lsat-rc.jsonl'
},
"logiqa": {
"test": HEAD + 'logiqa-en.jsonl'
},
"aqua_rat": {
"test": HEAD + 'aqua-rat.jsonl'
},
'math_agieval': {
"test": HEAD + 'math.jsonl'
},
'few_shot': {
'few_shot': 'https://raw.githubusercontent.com/ruixiangcui/AGIEval/main/data/few_shot_prompts.csv'
}
}
class AgiEval(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("2.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="aqua_rat",
version=VERSION,
description=_DESCRIPTION,
),
datasets.BuilderConfig(
name="sat_en",
version=VERSION,
description=_DESCRIPTION,
),
datasets.BuilderConfig(
name="sat_en_wop",
version=VERSION,
description=_DESCRIPTION,
),
datasets.BuilderConfig(
name="sat_math",
version=VERSION,
description=_DESCRIPTION,
),
datasets.BuilderConfig(
name="lsat_ar",
version=VERSION,
description=_DESCRIPTION,
),
datasets.BuilderConfig(
name="lsat_lr",
version=VERSION,
description=_DESCRIPTION,
),
datasets.BuilderConfig(
name="lsat_rc",
version=VERSION,
description=_DESCRIPTION,
),
datasets.BuilderConfig(
name="logiqa",
version=VERSION,
description=_DESCRIPTION,
),
datasets.BuilderConfig(
name="math_agieval",
version=VERSION,
description=_DESCRIPTION,
),
]
DEFAULT_CONFIG_NAME = "aqua_rat"
def _info(self):
if self.config.name == "aqua_rat":
features = datasets.Features(
{
"question": datasets.Value("string"),
"options": datasets.features.Sequence(datasets.Value("string")),
"label": datasets.ClassLabel(num_classes=5, names=["A", "B", "C", "D", "E"]),
"solution": datasets.Value("string"),
}
)
elif self.config.name in ("sat_en", "sat_math"):
features = datasets.Features(
{"passage": datasets.Value("string"),
"question": datasets.Value("string"),
"options": datasets.features.Sequence(datasets.Value("string")),
"label": datasets.ClassLabel(num_classes=4, names=["A", "B", "C", "D"]),
"solution": datasets.Value("string"),
}
)
elif self.config.name == "sat_en_wop":
features = datasets.Features(
{"question": datasets.Value("string"),
"options": datasets.features.Sequence(datasets.Value("string")),
"label": datasets.ClassLabel(num_classes=4, names=["A", "B", "C", "D"]),
"solution": datasets.Value("string"),
}
)
elif self.config.name == "logiqa":
features = datasets.Features(
{"passage": datasets.Value("string"),
"question": datasets.Value("string"),
"options": datasets.features.Sequence(datasets.Value("string")),
"label": datasets.ClassLabel(num_classes=4, names=["A", "B", "C", "D"]),
"solution": datasets.Value("string"),
}
)
elif self.config.name == "math_agieval":
features = datasets.Features(
{"question": datasets.Value("string"),
"answer": datasets.Value("string"),
"solution": datasets.Value("string"),
"level": datasets.Value("int32"),
"type": datasets.Value("string"),
}
)
elif self.config.name in ['lsat_lr', 'lsat_rc', 'lsat_ar']:
features = datasets.Features(
{"passage": datasets.Value("string"),
"question": datasets.Value("string"),
"options": datasets.features.Sequence(datasets.Value("string")),
"label": datasets.ClassLabel(num_classes=5, names=["A", "B", "C", "D", "E"]),
"solution": 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]
urls = {
"test": _urls["test"],
"few_shot": _URLS["few_shot"]["few_shot"],
}
data_dir = dl_manager.download_and_extract(urls)
splits = [datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": data_dir["test"], "split": "test"},
), datasets.SplitGenerator(
name="few_shot",
gen_kwargs={"filepath": data_dir["few_shot"], "split": "few_shot"},
)]
return splits
def _generate_examples(self, filepath, split):
# Mapping for column names in CSV to dataset names
names = {'aqua_rat': 'aqua-rat', 'sat_en': 'sat-en', 'sat_en_wop': 'sat-en','sat_math': 'sat-math',
'lsat_ar': 'lsat-ar', 'lsat_lr': 'lsat-lr', 'lsat_rc': 'lsat-rc',
'logiqa': 'logiqa-en', 'math_agieval': 'math'}
if split == "few_shot":
# Load the data from the CSV
df = pd.read_csv(filepath, keep_default_na=False)
# Extract samples and explanations
samples = df[df.index % 2 == 0].reset_index(drop=True)
explanations = df[df.index % 2 != 0].reset_index(drop=True)
for key in range(samples.shape[0]):
try:
data = ast.literal_eval(samples[names[self.config.name]][key])
explanation_row = explanations[names[self.config.name]][key]
if self.config.name == "aqua_rat":
yield key, {
"question": data["question"],
"options": data["options"],
"label": data["label"],
"solution": str(explanation_row),
}
elif self.config.name == "logiqa":
yield key, {
"passage": data["passage"],
"question": data["question"],
"options": data["options"],
"label": data["label"],
"solution": str(explanation_row),
}
elif self.config.name == "math_agieval":
if not data.get("level"):
data["level"] = data['other']['level']
if not data.get("type"):
data["type"] = data['other']['type']
yield key, {
"question": data["question"],
"answer": data["answer"],
"level": data["level"],
"type": data["type"],
"solution": str(explanation_row),
}
elif self.config.name in ("sat_en", "sat_math"):
yield key, {
"passage": data["passage"],
"question": data["question"],
"options": data["options"],
"label": data["label"],
"solution": str(explanation_row),
}
elif self.config.name == "sat_en_wop":
yield key, {
"question": data["question"],
"options": data["options"],
"label": data["label"],
"solution": str(explanation_row),
}
elif self.config.name in ['lsat_lr', 'lsat_rc', 'lsat_ar']:
yield key, {
"passage": data["passage"],
"question": data["question"],
"options": data["options"],
"label": data["label"],
"solution": str(explanation_row),
}
except:
pass
else:
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
if self.config.name == "aqua_rat":
yield key, {
"question": data["question"],
"options": data["options"],
"label": data["label"],
"solution": data["other"]["solution"],
}
elif self.config.name == "logiqa":
yield key, {
"passage": data["passage"],
"question": data["question"],
"options": data["options"],
"label": data["label"],
"solution": data["label"],
}
elif self.config.name == "math_agieval":
if not data.get("level"):
data["level"] = data['other']['level']
if not data.get("type"):
data["type"] = data['other']['type']
yield key, {
"question": data["question"],
"answer": data["answer"],
"solution": data["other"]["solution"],
"level": data["level"],
"type": data["type"],
}
elif self.config.name in ("sat_en", "sat_math"):
label_index = "ABCDE".index(data["label"])
if label_index > len(data["options"]) - 1:
continue
else:
yield key, {
"passage": data["passage"],
"question": data["question"],
"options": data["options"],
"label": data["label"],
"solution": data["other"]["solution"],
}
elif self.config.name == "sat_en_wop":
yield key, {
"question": data["question"],
"options": data["options"],
"label": data["label"],
"solution": data["other"]["solution"],
}
elif self.config.name in ['lsat_lr', 'lsat_rc', 'lsat_ar']:
yield key, {
"passage": data["passage"],
"question": data["question"],
"options": data["options"],
"label": data["label"],
"solution": data["label"],
}
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