Convert dataset to Parquet (#3)

- Convert dataset to Parquet (74fb3a34ea0b09e1bad5bdcf771496e3547fe2a4)
- Delete loading script (085e84eb036e7d4f967d00e82ce953cd82349380)

README.md

@@ -56,10 +56,15 @@ dataset_info:
           '4': Other
   splits:
   - name: train
-    num_bytes: 8149489
+    num_bytes: 8149409
     num_examples: 34268
-  download_size: 7766077
-  dataset_size: 8149489
+  download_size: 2278280
+  dataset_size: 8149409
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
 ---
 
 # Dataset Card for CIRCA

circa.py

@@ -1,153 +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.
-"""Dataset containing polar questions and indirect answers."""
-
-
-import csv
-
-import datasets
-
-
-_CITATION = """\
-@InProceedings{louis_emnlp2020,
-  author =      "Annie Louis and Dan Roth and Filip Radlinski",
-  title =       ""{I}'d rather just go to bed": {U}nderstanding {I}ndirect {A}nswers",
-  booktitle =   "Proceedings of the 2020 Conference on Empirical Methods
-  in Natural Language Processing",
-  year =        "2020",
-}
-"""
-
-_DESCRIPTION = """\
-The Circa (meaning ‘approximately’) dataset aims to help machine learning systems
-to solve the problem of interpreting indirect answers to polar questions.
-
-The dataset contains pairs of yes/no questions and indirect answers, together with
-annotations for the interpretation of the answer. The data is collected in 10
-different social conversational situations (eg. food preferences of a friend).
-
-NOTE: There might be missing labels in the dataset and we have replaced them with -1.
-The original dataset contains no train/dev/test splits.
-"""
-
-_LICENSE = "Creative Commons Attribution 4.0 License"
-
-_DATA_URL = "https://raw.githubusercontent.com/google-research-datasets/circa/main/circa-data.tsv"
-
-
-class Circa(datasets.GeneratorBasedBuilder):
-    """Dataset containing polar questions and indirect answers."""
-
-    VERSION = datasets.Version("1.1.0")
-
-    def _info(self):
-        features = datasets.Features(
-            {
-                "context": datasets.Value("string"),
-                "question-X": datasets.Value("string"),
-                "canquestion-X": datasets.Value("string"),
-                "answer-Y": datasets.Value("string"),
-                "judgements": datasets.Value("string"),
-                "goldstandard1": datasets.features.ClassLabel(
-                    names=[
-                        "Yes",
-                        "No",
-                        "In the middle, neither yes nor no",
-                        "Probably yes / sometimes yes",
-                        "Probably no",
-                        "Yes, subject to some conditions",
-                        "Other",
-                        "I am not sure how X will interpret Y’s answer",
-                    ]
-                ),
-                "goldstandard2": datasets.features.ClassLabel(
-                    names=[
-                        "Yes",
-                        "No",
-                        "In the middle, neither yes nor no",
-                        "Yes, subject to some conditions",
-                        "Other",
-                    ]
-                ),
-            }
-        )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # 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://github.com/google-research-datasets/circa",
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        train_path = dl_manager.download_and_extract(_DATA_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": train_path,
-                    "split": datasets.Split.TRAIN,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        with open(filepath, encoding="utf-8") as f:
-            goldstandard1_labels = [
-                "Yes",
-                "No",
-                "In the middle, neither yes nor no",
-                "Probably yes / sometimes yes",
-                "Probably no",
-                "Yes, subject to some conditions",
-                "Other",
-                "I am not sure how X will interpret Y’s answer",
-            ]
-            goldstandard2_labels = [
-                "Yes",
-                "No",
-                "In the middle, neither yes nor no",
-                "Yes, subject to some conditions",
-                "Other",
-            ]
-            data = csv.reader(f, delimiter="\t")
-            next(data, None)  # skip the headers
-            for id_, row in enumerate(data):
-                row = [x if x != "nan" else -1 for x in row]
-                _, context, question_X, canquestion_X, answer_Y, judgements, goldstandard1, goldstandard2 = row
-                if goldstandard1 not in goldstandard1_labels:
-                    goldstandard1 = -1
-                if goldstandard2 not in goldstandard2_labels:
-                    goldstandard2 = -1
-
-                yield id_, {
-                    "context": context,
-                    "question-X": question_X,
-                    "canquestion-X": canquestion_X,
-                    "answer-Y": answer_Y,
-                    "judgements": judgements,
-                    "goldstandard1": goldstandard1,
-                    "goldstandard2": goldstandard2,
-                }

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f1964d250da30e20b688f2ad1570ba7ea5aa6af4ef70b01bf599b3f95f33f9ee
+size 2278280