Datasets:
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# coding=utf-8
# Lint as: python3
"""mindgames datasets"""
from __future__ import absolute_import, division, print_function
import json
import os
import textwrap
import six
import datasets
CITATION = r"""
@article{sileo2023mindgames,
title={MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic},
author={Sileo, Damien and Lernould, Antoine},
journal={arXiv preprint arXiv:2305.03353},
year={2023}
}
"""
DESCRIPTION = """\
mindgames json tasks
"""
DATA_URL = "https://www.dropbox.com/s/xeh8k1gu1j51ktu/mindgames-v1.zip?dl=1"
CONFIGS=['forehead','forehead-mirror','explicit','internal','all']
class mindgames_Config(datasets.BuilderConfig):
"""BuilderConfig for mindgames."""
def __init__(
self,
text_features,
label_classes=None,
**kwargs,
):
"""BuilderConfig for mindgames.
Args:
text_features: `dict[string, string]`, map from the name of the feature
dict for each text field to the name of the column in the tsv file
data_url: `string`, url to download the zip file from
data_dir: `string`, the path to the folder containing the tsv files in the
downloaded zip
citation: `string`, citation for the data set
url: `string`, url for information about the data set
"""
super(mindgames_Config, self).__init__(
version=datasets.Version("1.0.0", ""), **kwargs
)
self.text_features = text_features
self.data_url = DATA_URL
self.data_dir = self.name#os.path.join("", self.name)
self.citation = textwrap.dedent(CITATION)
self.description = ""
class mindgames(datasets.GeneratorBasedBuilder):
"""The General Language Understanding Evaluation (mindgames) benchmark."""
BUILDER_CONFIG_CLASS = mindgames_Config
DEFAULT_CONFIG_NAME = "all"
BUILDER_CONFIGS = [
mindgames_Config(
name=name,
text_features={"inputs": "inputs"},
) for name in CONFIGS
]
def _info(self):
features = {
"hypothesis": datasets.Value("string"),
"premise": datasets.Value("string"),
"label": datasets.Value("int32"),
"n_announcements": datasets.Value("int32"),
"n_agents": datasets.Value("int32"),
"hypothesis_depth": datasets.Value("int32"),
"index": datasets.Value("int32")
}
for k in ['smcdel_problem',"pbcheck","names","setup","s-l","deberta_pred","deberta_confidence","difficulty"]:
features[k]=datasets.Value("string")
return datasets.DatasetInfo(
description=DESCRIPTION,
features=datasets.Features(features),
citation=self.config.citation + "\n" + CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(self.config.data_url)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"data_file": f"{data_dir}/train-{self.config.name}.jsonl",
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"data_file": f"{data_dir}/validation-{self.config.name}.jsonl",
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"data_file": f"{data_dir}/test-{self.config.name}.jsonl",
"split": "validation",
},
),
]
def _generate_examples(self, data_file,split):
"""Yields examples."""
with open(data_file, "r", encoding="utf-8") as f:
for id_, line in enumerate(f):
line_dict = json.loads(line)
yield id_, line_dict |