Convert dataset to Parquet

.gitattributes

@@ -15,3 +15,4 @@
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
 data/default.jsonl filter=lfs diff=lfs merge=lfs -text
+data/test-00000-of-00001.parquet filter=lfs diff=lfs merge=lfs -text

README.md

@@ -21,6 +21,45 @@ task_categories:
 task_ids:
 - multiple-choice-qa
 - open-domain-qa
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test-*
+dataset_info:
+  features:
+  - name: A
+    dtype: string
+  - name: B
+    dtype: string
+  - name: C
+    dtype: string
+  - name: D
+    dtype: string
+  - name: context
+    dtype: string
+  - name: question
+    dtype: string
+  - name: ex_question
+    dtype: string
+  - name: group
+    dtype: string
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': A
+          '1': B
+          '2': C
+          '3': D
+  - name: name
+    dtype: string
+  splits:
+  - name: test
+    num_bytes: 5698184
+    num_examples: 18736
+  download_size: 336196
+  dataset_size: 5698184
 ---
 
 # PROST: Physical Reasoning about Objects Through Space and Time

data/{default.jsonl → test-00000-of-00001.parquet}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:05d773ae2768047579ea22415fc593199430747027b3df5621e60044fe893bde
-size 7197063
+oid sha256:71e042a340deff611e48d25ae864290adf49993c9fda60f1fee26432222d426b
+size 336196

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "*Physical Reasoning about Objects Through Space and Time* (PROST) is a probing dataset to evaluate the ability of pretrained LMs to understand and reason about the physical world. PROST consists of 18,736 cloze-style multiple choice questions from 14 manually curated templates, covering 10 physical reasoning concepts:  direction, mass, height, circumference, stackable, rollable, graspable, breakable, slideable, and bounceable.\n", "citation": "@inproceedings{aroca-ouellette-etal-2021-prost,\n  title = \"{PROST}: {P}hysical Reasoning about Objects through Space and Time\",\n  author = \"Aroca-Ouellette, St{'e}phane  and\n    Paik, Cory  and\n    Roncone, Alessandro  and\n    Kann, Katharina\",\n  booktitle = \"Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021\",\n  month = aug,\n  year = \"2021\",\n  address = \"Online\",\n  publisher = \"Association for Computational Linguistics\",\n  url = \"https://aclanthology.org/2021.findings-acl.404\",\n  pages = \"4597--4608\",\n}\n", "homepage": "https://github.com/nala-cub/prost", "license": "Apache 2.0", "features": {"A": {"dtype": "string", "id": null, "_type": "Value"}, "B": {"dtype": "string", "id": null, "_type": "Value"}, "C": {"dtype": "string", "id": null, "_type": "Value"}, "D": {"dtype": "string", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "ex_question": {"dtype": "string", "id": null, "_type": "Value"}, "group": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 4, "names": ["A", "B", "C", "D"], "names_file": null, "id": null, "_type": "ClassLabel"}, "name": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "prost", "config_name": "default", "version": {"version_str": "1.0.1", "description": null, "major": 1, "minor": 0, "patch": 1}, "splits": {"test": {"name": "test", "num_bytes": 5698256, "num_examples": 18736, "dataset_name": "prost"}}, "download_checksums": {"https://huggingface.co/datasets/corypaik/prost/resolve/main/data/default.jsonl": {"num_bytes": 7197063, "checksum": "05d773ae2768047579ea22415fc593199430747027b3df5621e60044fe893bde"}}, "download_size": 7197063, "post_processing_size": null, "dataset_size": 5698256, "size_in_bytes": 12895319}}

prost.py

@@ -1,85 +0,0 @@
-# Copyright 2020 The HuggingFace Datasets Authors and Cory Paik
-#
-# 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.
-# ==============================================================================
-""" Physical Reasoning about Objects Through Space and Time (PROST)
-  
-  PROST is a probing dataset to evaluate the ability of pretrained LMs to 
-  understand and reason about the physical world. 
-"""
-import json
-import datasets
-
-
-_CITATION = """\
-@inproceedings{aroca-ouellette-etal-2021-prost,
-  title = "{PROST}: {P}hysical Reasoning about Objects through Space and Time",
-  author = "Aroca-Ouellette, St{\'e}phane  and
-    Paik, Cory  and
-    Roncone, Alessandro  and
-    Kann, Katharina",
-  booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
-  month = aug,
-  year = "2021",
-  address = "Online",
-  publisher = "Association for Computational Linguistics",
-  url = "https://aclanthology.org/2021.findings-acl.404",
-  pages = "4597--4608",
-}
-"""
-
-
-_DESCRIPTION = """\
-*Physical Reasoning about Objects Through Space and Time* (PROST) is a probing dataset to evaluate the ability of pretrained LMs to understand and reason about the physical world. PROST consists of 18,736 cloze-style multiple choice questions from 14 manually curated templates, covering 10 physical reasoning concepts:  direction, mass, height, circumference, stackable, rollable, graspable, breakable, slideable, and bounceable.
-"""
-
-_HOMEPAGE = 'https://github.com/nala-cub/prost'
-_LICENSE = 'Apache 2.0'
-
-_URL = 'https://huggingface.co/datasets/corypaik/prost/resolve/main/data'
-
-_URLs = {'default': f'{_URL}/default.jsonl'}
-
-MC_LABELS = list('ABCD')
-
-
-class Prost(datasets.GeneratorBasedBuilder):
-
-  VERSION = datasets.Version('1.0.1')
-
-  def _info(self):
-    features = datasets.Features({
-      'A': datasets.Value('string'),
-      'B': datasets.Value('string'),
-      'C': datasets.Value('string'),
-      'D': datasets.Value('string'),
-      'context': datasets.Value('string'),
-      'question': datasets.Value('string'),
-      'ex_question': datasets.Value('string'),
-      'group': datasets.Value('string'),
-      'label': datasets.ClassLabel(names=MC_LABELS),
-      'name': datasets.Value('string'),})
-    return datasets.DatasetInfo(description=_DESCRIPTION, features=features,
-                                supervised_keys=None, homepage=_HOMEPAGE,
-                                license=_LICENSE, citation=_CITATION)
-
-  def _split_generators(self, dl_manager):
-    """ Returns SplitGenerators."""
-    path = dl_manager.download_and_extract(_URLs[self.config.name])
-    kwargs = {'path': path}
-    return [datasets.SplitGenerator(datasets.Split.TEST, gen_kwargs=kwargs)]
-
-  def _generate_examples(self, path):
-    with open(path, 'r') as f:
-      for _id, line in enumerate(f.readlines()):
-        yield _id, json.loads(line)