Delete loading script

civil_comments_helm.py

@@ -1,75 +0,0 @@
-import datasets
-import os
-import json
-
-
-categories = ["male", "female", "LGBTQ", "christian", "muslim", "other_religions", "black", "white", "all"]
-
-_CITATION = """
-        @inproceedings{wilds2021,
-            title = {{WILDS}: A Benchmark of in-the-Wild Distribution Shifts},
-            author = {Pang Wei Koh and Shiori Sagawa and Henrik Marklund and Sang Michael Xie and Marvin Zhang and
-            Akshay Balsubramani and Weihua Hu and Michihiro Yasunaga and Richard Lanas Phillips and Irena Gao and
-            Tony Lee and Etienne David and Ian Stavness and Wei Guo and Berton A. Earnshaw and Imran S. Haque and
-            Sara Beery and Jure Leskovec and Anshul Kundaje and Emma Pierson and Sergey Levine and Chelsea Finn
-            and Percy Liang},
-            booktitle = {International Conference on Machine Learning (ICML)},
-            year = {2021}
-        }
-
-        @inproceedings{borkan2019nuanced,
-            title={Nuanced metrics for measuring unintended bias with real data for text classification},
-            author={Borkan, Daniel and Dixon, Lucas and Sorensen, Jeffrey and Thain, Nithum and Vasserman, Lucy},
-            booktitle={Companion Proceedings of The 2019 World Wide Web Conference},
-            pages={491--500},
-            year={2019}
-        }"""
-
-class CivilCommentsHELM(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name=name, version=datasets.Version("1.0.0"), description="")
-            for name in categories
-        ]
-
-    #"references": [{"output": "The gay couple", "tags": ["negative", "ambiguous", "B", "A", "C", "B"]}, {"output": "Not enough info", "tags": ["correct", "negative", "ambiguous", "B", "A", "C", "B"]}, {"output": "The pansexual couple", "tags": ["negative", "ambiguous", "B", "A", "C", "B"]}], "choices": ["The gay couple", "Not enough info", "The pansexual couple"], "gold_index": 1}
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "text": datasets.Value("string"),
-                "gold": datasets.Value("string"),
-                "sub_split": datasets.Value("string"),
-
-            }
-        )
-        return datasets.DatasetInfo(
-            description=f"Civil comments, as preprocessed and shuffled in HELM",
-            features=features,
-            homepage="",
-            license="",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        train_json = dl_manager.download(os.path.join(self.config.name, "train.json"))
-        test_json = dl_manager.download(os.path.join(self.config.name, "test.json"))
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"path": train_json},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"path": test_json},
-            )
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, path):
-        with open(path, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                yield key, json.loads(row)
-