Convert dataset to Parquet (#4)

- Convert dataset to Parquet (cbc4c478c1a6a1975b46e0b10e2b5a9c4bd2bcc7)
- Add fold_0 data files (11f8e212e109b4570372a488bed4c066696e470f)
- Add fold_1 data files (2663839510ef595241de6377754548901793990b)
- Add fold_2 data files (600c081aabdbd68c9510a2969743f4b8d6cc3dcc)
- Add fold_3 data files (5eb0fd20dc2be8156206d845d8b29e1582c3164b)
- Add fold_4 data files (b2b5d0368c9804b7baaaf912fa5dcca1e3b8073a)
- Delete loading script (2fe4ec4a27b2e56b8227b69ad95cc96fc404f722)

README.md

@@ -42,10 +42,10 @@ dataset_info:
     dtype: int32
   splits:
   - name: train
-    num_bytes: 571208
+    num_bytes: 571196
     num_examples: 2776
-  download_size: 485130
-  dataset_size: 571208
+  download_size: 352902
+  dataset_size: 571196
 - config_name: fold_0
   features:
   - name: id
@@ -68,16 +68,16 @@ dataset_info:
     dtype: int32
   splits:
   - name: train
-    num_bytes: 344912
+    num_bytes: 344900
     num_examples: 1665
   - name: validation
-    num_bytes: 114211
+    num_bytes: 114199
     num_examples: 556
   - name: test
-    num_bytes: 112109
+    num_bytes: 112097
     num_examples: 555
-  download_size: 485130
-  dataset_size: 571232
+  download_size: 379179
+  dataset_size: 571196
 - config_name: fold_1
   features:
   - name: id
@@ -100,16 +100,16 @@ dataset_info:
     dtype: int32
   splits:
   - name: train
-    num_bytes: 340990
+    num_bytes: 340978
     num_examples: 1665
   - name: validation
-    num_bytes: 114211
+    num_bytes: 114199
     num_examples: 556
   - name: test
-    num_bytes: 116031
+    num_bytes: 116019
     num_examples: 555
-  download_size: 485130
-  dataset_size: 571232
+  download_size: 379728
+  dataset_size: 571196
 - config_name: fold_2
   features:
   - name: id
@@ -132,16 +132,16 @@ dataset_info:
     dtype: int32
   splits:
   - name: train
-    num_bytes: 342293
+    num_bytes: 342281
     num_examples: 1665
   - name: validation
-    num_bytes: 114211
+    num_bytes: 114199
     num_examples: 556
   - name: test
-    num_bytes: 114728
+    num_bytes: 114716
     num_examples: 555
-  download_size: 485130
-  dataset_size: 571232
+  download_size: 379126
+  dataset_size: 571196
 - config_name: fold_3
   features:
   - name: id
@@ -164,16 +164,16 @@ dataset_info:
     dtype: int32
   splits:
   - name: train
-    num_bytes: 342844
+    num_bytes: 342832
     num_examples: 1665
   - name: validation
-    num_bytes: 114211
+    num_bytes: 114199
     num_examples: 556
   - name: test
-    num_bytes: 114177
+    num_bytes: 114165
     num_examples: 555
-  download_size: 485130
-  dataset_size: 571232
+  download_size: 379178
+  dataset_size: 571196
 - config_name: fold_4
   features:
   - name: id
@@ -196,16 +196,61 @@ dataset_info:
     dtype: int32
   splits:
   - name: train
-    num_bytes: 342844
+    num_bytes: 342832
     num_examples: 1665
   - name: validation
-    num_bytes: 114177
+    num_bytes: 114165
     num_examples: 555
   - name: test
-    num_bytes: 114211
+    num_bytes: 114199
     num_examples: 556
-  download_size: 485130
-  dataset_size: 571232
+  download_size: 379178
+  dataset_size: 571196
+configs:
+- config_name: codah
+  data_files:
+  - split: train
+    path: codah/train-*
+- config_name: fold_0
+  data_files:
+  - split: train
+    path: fold_0/train-*
+  - split: validation
+    path: fold_0/validation-*
+  - split: test
+    path: fold_0/test-*
+- config_name: fold_1
+  data_files:
+  - split: train
+    path: fold_1/train-*
+  - split: validation
+    path: fold_1/validation-*
+  - split: test
+    path: fold_1/test-*
+- config_name: fold_2
+  data_files:
+  - split: train
+    path: fold_2/train-*
+  - split: validation
+    path: fold_2/validation-*
+  - split: test
+    path: fold_2/test-*
+- config_name: fold_3
+  data_files:
+  - split: train
+    path: fold_3/train-*
+  - split: validation
+    path: fold_3/validation-*
+  - split: test
+    path: fold_3/test-*
+- config_name: fold_4
+  data_files:
+  - split: train
+    path: fold_4/train-*
+  - split: validation
+    path: fold_4/validation-*
+  - split: test
+    path: fold_4/test-*
 ---
 
 # Dataset Card for COmmonsense Dataset Adversarially-authored by Humans

codah.py

@@ -1,141 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# 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.
-"""The COmmonsense Dataset Adversarially-authored by Humans (CODAH)"""
-
-
-import csv
-
-import datasets
-
-
-_CITATION = """\
-@inproceedings{chen2019codah,
-  title={CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense},
-  author={Chen, Michael and D'Arcy, Mike and Liu, Alisa and Fernandez, Jared and Downey, Doug},
-  booktitle={Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP},
-  pages={63--69},
-  year={2019}
-}
-"""
-
-_DESCRIPTION = """\
-The COmmonsense Dataset Adversarially-authored by Humans (CODAH) is an evaluation set for commonsense \
-question-answering in the sentence completion style of SWAG. As opposed to other automatically \
-generated NLI datasets, CODAH is adversarially constructed by humans who can view feedback \
-from a pre-trained model and use this information to design challenging commonsense questions. \
-Our experimental results show that CODAH questions present a complementary extension to the SWAG dataset, testing additional modes of common sense.
-"""
-
-_URL = "https://raw.githubusercontent.com/Websail-NU/CODAH/master/data/"
-_FULL_DATA_URL = _URL + "full_data.tsv"
-
-QUESTION_CATEGORIES_MAPPING = {
-    "i": "Idioms",
-    "r": "Reference",
-    "p": "Polysemy",
-    "n": "Negation",
-    "q": "Quantitative",
-    "o": "Others",
-}
-
-
-class CodahConfig(datasets.BuilderConfig):
-    """BuilderConfig for CODAH."""
-
-    def __init__(self, fold=None, **kwargs):
-        """BuilderConfig for CODAH.
-
-        Args:
-          fold: `string`, official cross validation fold.
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(CodahConfig, self).__init__(**kwargs)
-        self.fold = fold
-
-
-class Codah(datasets.GeneratorBasedBuilder):
-    """The COmmonsense Dataset Adversarially-authored by Humans (CODAH)"""
-
-    VERSION = datasets.Version("1.0.0")
-    BUILDER_CONFIGS = [
-        CodahConfig(name="codah", version=datasets.Version("1.0.0"), description="Full CODAH dataset", fold=None),
-        CodahConfig(
-            name="fold_0", version=datasets.Version("1.0.0"), description="Official CV split (fold_0)", fold="fold_0"
-        ),
-        CodahConfig(
-            name="fold_1", version=datasets.Version("1.0.0"), description="Official CV split (fold_1)", fold="fold_1"
-        ),
-        CodahConfig(
-            name="fold_2", version=datasets.Version("1.0.0"), description="Official CV split (fold_2)", fold="fold_2"
-        ),
-        CodahConfig(
-            name="fold_3", version=datasets.Version("1.0.0"), description="Official CV split (fold_3)", fold="fold_3"
-        ),
-        CodahConfig(
-            name="fold_4", version=datasets.Version("1.0.0"), description="Official CV split (fold_4)", fold="fold_4"
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("int32"),
-                    "question_category": datasets.features.ClassLabel(
-                        names=["Idioms", "Reference", "Polysemy", "Negation", "Quantitative", "Others"]
-                    ),
-                    "question_propmt": datasets.Value("string"),
-                    "candidate_answers": datasets.features.Sequence(datasets.Value("string")),
-                    "correct_answer_idx": datasets.Value("int32"),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/Websail-NU/CODAH",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        if self.config.name == "codah":
-            data_file = dl_manager.download(_FULL_DATA_URL)
-            return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_file": data_file})]
-
-        base_url = f"{_URL}cv_split/{self.config.fold}/"
-        _urls = {
-            "train": base_url + "train.tsv",
-            "dev": base_url + "dev.tsv",
-            "test": base_url + "test.tsv",
-        }
-        downloaded_files = dl_manager.download_and_extract(_urls)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_file": downloaded_files["train"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"data_file": downloaded_files["dev"]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"data_file": downloaded_files["test"]}),
-        ]
-
-    def _generate_examples(self, data_file):
-        with open(data_file, encoding="utf-8") as f:
-            rows = csv.reader(f, delimiter="\t")
-            for i, row in enumerate(rows):
-                question_category = QUESTION_CATEGORIES_MAPPING[row[0]] if row[0] != "" else -1
-                example = {
-                    "id": i,
-                    "question_category": question_category,
-                    "question_propmt": row[1],
-                    "candidate_answers": row[2:-1],
-                    "correct_answer_idx": int(row[-1]),
-                }
-                yield i, example

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fold_0/test-00000-of-00001.parquet

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