Upload working-memory.py

working-memory.py

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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.
+
+
+import json
+import datasets
+from pathlib import Path
+
+
+_CITATION = """\
+@inproceedings{gong2024working,
+  title={Working memory capacity of ChatGPT: An empirical study},
+  author={Gong, Dongyu and Wan, Xingchen and Wang, Dingmin},
+  booktitle={Proceedings of the AAAI conference on artificial intelligence},
+  volume={38},
+  number={9},
+  pages={10048--10056},
+  year={2024}
+}
+"""
+
+_DESCRIPTION = """\
+A benchmark for evaluating Working Memory capabilities in LLMs. Here only the data for the three base 'verbal' experiments are provided."""
+
+_HOMEPAGE = "https://github.com/Daniel-Gong/ChatGPT-WM"
+
+_LICENSE = "MIT"
+
+_URLS_prefix = {
+    "verbal" : "https://raw.githubusercontent.com/momentino/playpen_eval/main/frameworks/playpen_eval_benchmarks/tasks/wm/data/json/verbal",
+}
+_URLS = {
+    "verbal_1back": {
+        "test": _URLS_prefix["verbal"] + "/1back.json"
+    },
+    "verbal_2back": {
+        "test": _URLS_prefix["verbal"] + "/2back.json"
+    },
+    "verbal_3back": {
+        "test": _URLS_prefix["verbal"] + "/3back.json"
+    }
+}
+
+class WorkingMemory(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=config_name,
+            version=datasets.Version("0.0.1"),
+            description=f"{config_name} task from WorkingMemory"
+        )
+        for config_name in _URLS.keys()
+    ]
+    def _info(self):
+        features = {
+            "stimuli": datasets.Value("string"),
+            "target": datasets.Value("string")
+        }
+        features = datasets.Features(features)
+        return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=features,
+                homepage=_HOMEPAGE,
+                citation=_CITATION,
+                license=_LICENSE,
+            )
+
+    """def _split_generators(self, dl_manager):
+        data_dir = Path("path/to/your/local/folder")  # Use Path object
+        subset_dirs = [d for d in data_dir.iterdir() if d.is_dir()]  # Get only directories
+
+        split_generators = []
+        for subset_dir in subset_dirs:
+            for i in range(50):  # Create at least 50 splits per subset
+                split_generators.append(
+                    datasets.SplitGenerator(
+                        name=f"{subset_dir.name}_split_{i}",
+                        gen_kwargs={
+                            "filepath": str(subset_dir),
+                            "split": f"{subset_dir.name}_split_{i}",
+                        },
+                    )
+                )
+
+        return split_generators"""
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        with open(data_dir["test"], encoding="utf-8") as fin:
+            data = json.load(fin)
+
+        # Create one split per instance, naming them uniquely
+        splits = []
+        for idx in range(len(data)):
+            splits.append(
+                datasets.SplitGenerator(
+                    # Name splits as "test_0", "test_1", etc.
+                    name=f"{idx}",
+                    gen_kwargs={
+                        "filepath": data_dir["test"],
+                        "index": idx,
+                    }
+                )
+            )
+        return splits
+
+
+    def _generate_examples(self, filepath, index):
+        # Open the JSON file and load the instance at the provided index
+        with open(filepath, encoding="utf-8") as fin:
+            data = json.load(fin)
+            for id,instance in enumerate(data[index]):
+                # Yield using the instance id as key (make sure it's unique)
+                yield id, instance