Create mindgames.py

mindgames.py

@@ -0,0 +1,117 @@
+# 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/yamo3jsgzdhkim8/mindgames.zip?dl=1"
+
+CONFIGS=['internal','forehead','forehead-mirror','explicit']
+
+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
+
+    BUILDER_CONFIGS = [
+        mindgames_Config(
+            name=name,
+            text_features={"inputs": "inputs"},
+        ) for name in CONFIGS
+    ]
+
+    def _info(self):
+
+        #features["idx"] = datasets.Value("int32")
+        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