Convert dataset to Parquet

README.md

@@ -32,16 +32,25 @@ dataset_info:
       dtype: string
   splits:
   - name: train
-    num_bytes: 720715
+    num_bytes: 720691
     num_examples: 3510
   - name: validation
-    num_bytes: 208276
+    num_bytes: 208252
     num_examples: 1021
   - name: test
-    num_bytes: 212790
+    num_bytes: 212766
     num_examples: 1184
-  download_size: 2083122
-  dataset_size: 1141781
+  download_size: 620497
+  dataset_size: 1141709
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for RiddleSense

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:ebaaf7b2a5e5ff034ca4a2038eb49f4514c371b12b842fafeff9a355a82c3585
+size 114420

data/train-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:126d6f003ab9b40fb22718a8a3ab161fac66b69096aed4e8203f20271f2291c7
+size 398066

data/validation-00000-of-00001.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:99eee1798a17aad51da9c9522aa52b5151ecc558ba1d0ecf58f9d1c9809b6e59
+size 108011

riddle_sense.py

@@ -1,126 +0,0 @@
-import json
-
-import datasets
-
-
-_CITATION = """\
-@InProceedings{lin-etal-2021-riddlesense,
-title={RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledge},
-author={Lin, Bill Yuchen and Wu, Ziyi and Yang, Yichi and Lee, Dong-Ho and Ren, Xiang},
-journal={Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL-IJCNLP 2021): Findings},
-year={2021}
-}
-"""
-
-_DESCRIPTION = """\
-Answering such a riddle-style question is a challenging cognitive process, in that it requires
-complex commonsense reasoning abilities, an understanding of figurative language, and counterfactual reasoning
-skills, which are all important abilities for advanced natural language understanding (NLU). However,
-there is currently no dedicated datasets aiming to test these abilities. Herein, we present RiddleSense,
-a new multiple-choice question answering task, which comes with the first large dataset (5.7k examples) for answering
-riddle-style commonsense questions. We systematically evaluate a wide range of models over the challenge,
-and point out that there is a large gap between the best-supervised model and human performance — suggesting
-intriguing future research in the direction of higher-order commonsense reasoning and linguistic creativity towards
-building advanced NLU systems.
-
-"""
-
-_LICENSE = """\
-The copyright of RiddleSense dataset is consistent with the terms of use of the fan websites and the intellectual
-property and privacy rights of the original sources. All of our riddles and answers are from fan websites that can be
-accessed freely. The website owners state that you may print and download material from the sites solely for non
-commercial use provided that we agree not to change or delete any copyright or proprietary notices from the
-materials. The dataset users must agree that they will only use the dataset for research purposes before they can
-access the both the riddles and our annotations. We do not vouch for the potential bias or fairness issue that might
-exist within the riddles. You do not have the right to redistribute them. Again, you must not use this dataset for any
-commercial purposes.
-"""
-
-_URL = "https://inklab.usc.edu/RiddleSense/riddlesense_dataset/"
-_URLS = {
-    "train": _URL + "rs_train.jsonl",
-    "dev": _URL + "rs_dev.jsonl",
-    "test": _URL + "rs_test_hidden.jsonl",
-}
-
-
-class RiddleSense(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("0.1.0")
-
-    def _info(self):
-        # These are the features of your dataset like images, labels ...
-        features = datasets.Features(
-            {
-                "answerKey": datasets.Value("string"),
-                "question": datasets.Value("string"),
-                "choices": datasets.features.Sequence(
-                    {
-                        "label": datasets.Value("string"),
-                        "text": datasets.Value("string"),
-                    }
-                ),
-            }
-        )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # datasets.features.FeatureConnectors
-            features=features,
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage="https://inklab.usc.edu/RiddleSense/",
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-
-        download_urls = _URLS
-
-        downloaded_files = dl_manager.download_and_extract(download_urls)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"], "split": "train"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": downloaded_files["dev"],
-                    "split": "dev",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": downloaded_files["test"],
-                    "split": "test",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            for id_, row in enumerate(f):
-                data = json.loads(row)
-                question = data["question"]
-                choices = question["choices"]
-                labels = [label["label"] for label in choices]
-                texts = [text["text"] for text in choices]
-                stem = question["stem"]
-                if split == "test":
-                    answerkey = ""
-                else:
-                    answerkey = data["answerKey"]
-
-                yield id_, {
-                    "answerKey": answerkey,
-                    "question": stem,
-                    "choices": {"label": labels, "text": texts},
-                }