Delete loading script

working-memory.py

@@ -1,127 +0,0 @@
-# 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