|
|
|
|
|
|
|
"""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 |
|
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 |
