added comps loading script

README.md

@@ -1,3 +1,20 @@
 ---
+annotations_creators:
+- expert-generated
+language_creators:
+- machine-generated
+language:
+- en
 license: apache-2.0
+multilinguality:
+- monolingual
+pretty_name: COMPS
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
 ---
+
+# Dataset Card for "blimp"
+
+TBD

comps.py

@@ -0,0 +1,114 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""COMPS: Conceptual Minimal Pair Sentences for analyzing property knowledge."""
+
+import json
+
+import datasets
+
+
+_CITATION = """
+@article{misra2022comps,
+  title={COMPS: Conceptual Minimal Pair Sentences for testing Property Knowledge and Inheritance in Pre-trained Language Models},
+  author={Misra, Kanishka and Rayz, Julia Taylor and Ettinger, Allyson},
+  journal={arXiv preprint arXiv:2210.01963},
+  year={2022}
+}
+"""
+
+_DESCRIPTION = """
+COMPS is a dataset of minimal pair sentences in English that enables the 
+testing of the ability of language models to attribute properties to everyday 
+concepts, and also demonstrate property inheritance -- where subordinate 
+concepts inherit the properties of their superordinate (hypernyms).
+"""
+
+_PROJECT_URL = "https://github.com/kanishkamisra/comps"
+_DOWNLOAD_URL = "https://raw.githubusercontent.com/kanishkamisra/comps/main/" \
+    "data/comps/"
+
+
+class CompsConfig(datasets.BuilderConfig):
+    """BuilderConfig for Blimp."""
+
+    def __init__(self, name, version=datasets.Version("0.1.0"), **kwargs):
+        """BuilderConfig for Blimp.
+
+        Args:
+          name (str): UID of the linguistic paradigm
+          **kwargs: keyword arguments forwarded to super.
+        """
+        description = _DESCRIPTION
+        if name == "base":
+            description += " This subset tests for base property knowledge."
+        elif name == "wugs":
+            description += " This subset tests for property inheritance" \
+                " without distraction."
+        else:
+            description += " This subset tests for property inheritance with" \
+                 f" distraction ({name.replace('wugs-dist-')})."
+
+        super().__init__(name=name, description=description, version=version, **kwargs)
+
+
+class Blimp(datasets.GeneratorBasedBuilder):
+    """Minimal grammatical and ungrammatical pairs of 67 linguistic paradigms."""
+
+    subsets = ("base", "wugs", "wugs_dist", "wugs_dist_before", "wugs_dist_in-between")
+
+    BUILDER_CONFIGS = [CompsConfig(subset) for subset in subsets]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "property": datasets.Value("string"),
+                    "acceptable_concept": datasets.Value("string"),
+                    "unacceptable_concept": datasets.Value("string"),
+                    "prefix_acceptable": datasets.Value("string"),
+                    "prefix_unacceptable": datasets.Value("string"),
+                    "property_phrase": datasets.Value("string"),
+                    "negative_sample_type": datasets.Value("string"),
+                    "similarity": datasets.Value("float32"),
+                    "distraction_type": datasets.Value("string"),
+                }
+            ),
+            homepage=_PROJECT_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        download_urls = _DOWNLOAD_URL + f"comps_{self.config.name}.jsonl"
+        downloaded_file = dl_manager.download_and_extract(download_urls)
+        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file})]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, "r", encoding="utf-8") as f:
+            for line in f:
+                line_dict = json.loads(line)
+                if "wugs" in self.config.name:
+                    id_ = line_dict["base_id"] + "_" + line_dict["distraction_type"]
+                else:
+                    id_ = line_dict['id']
+                feats = {
+                    **line_dict
+                }
+                yield id_, feats