init

civil_comments_helm.py

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+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)
+