Convert dataset to Parquet

README.md

@@ -48,16 +48,25 @@ dataset_info:
           '6': surprise
   splits:
   - name: train
-    num_bytes: 7296715
+    num_bytes: 7296683
     num_examples: 11118
-  - name: test
-    num_bytes: 655844
-    num_examples: 1000
   - name: validation
-    num_bytes: 673943
+    num_bytes: 673927
+    num_examples: 1000
+  - name: test
+    num_bytes: 655828
     num_examples: 1000
-  download_size: 4475921
-  dataset_size: 8626502
+  download_size: 4273740
+  dataset_size: 8626438
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
 ---
 
 # Dataset Card for "daily_dialog"

daily_dialog.py

@@ -1,122 +0,0 @@
-# coding=utf-8
-# Copyright 2020 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.
-"""DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset"""
-
-
-import os
-from zipfile import ZipFile
-
-import datasets
-
-
-_CITATION = """\
-@InProceedings{li2017dailydialog,
-    author = {Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi},
-    title = {DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset},
-    booktitle = {Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017)},
-    year = {2017}
-}
-"""
-
-_DESCRIPTION = """\
-We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects.
-The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way
-and cover various topics about our daily life. We also manually label the developed dataset with communication
-intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it
-benefit the research field of dialog systems.
-"""
-
-_URL = "http://yanran.li/files/ijcnlp_dailydialog.zip"
-
-act_label = {
-    "0": "__dummy__",  # Added to be compatible out-of-the-box with datasets.ClassLabel
-    "1": "inform",
-    "2": "question",
-    "3": "directive",
-    "4": "commissive",
-}
-
-emotion_label = {
-    "0": "no emotion",
-    "1": "anger",
-    "2": "disgust",
-    "3": "fear",
-    "4": "happiness",
-    "5": "sadness",
-    "6": "surprise",
-}
-
-
-class DailyDialog(datasets.GeneratorBasedBuilder):
-    """DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset"""
-
-    VERSION = datasets.Version("1.0.0")
-
-    __EOU__ = "__eou__"
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "dialog": datasets.features.Sequence(datasets.Value("string")),
-                    "act": datasets.features.Sequence(datasets.ClassLabel(names=list(act_label.values()))),
-                    "emotion": datasets.features.Sequence(datasets.ClassLabel(names=list(emotion_label.values()))),
-                }
-            ),
-            supervised_keys=None,
-            homepage="http://yanran.li/dailydialog",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager):
-        dl_dir = dl_manager.download_and_extract(_URL)
-        data_dir = os.path.join(dl_dir, "ijcnlp_dailydialog")
-        splits = [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]
-        return [
-            datasets.SplitGenerator(
-                name=split,
-                gen_kwargs={
-                    "data_zip": os.path.join(data_dir, f"{split}.zip"),
-                    "dialog_path": f"{split}/dialogues_{split}.txt",
-                    "act_path": f"{split}/dialogues_act_{split}.txt",
-                    "emotion_path": f"{split}/dialogues_emotion_{split}.txt",
-                },
-            )
-            for split in splits
-        ]
-
-    def _generate_examples(self, data_zip, dialog_path, act_path, emotion_path):
-        with open(data_zip, "rb") as data_file:
-            with ZipFile(data_file) as zip_file:
-                with zip_file.open(dialog_path) as dialog_file, zip_file.open(act_path) as act_file, zip_file.open(
-                    emotion_path
-                ) as emotion_file:
-                    for idx, (dialog_line, act_line, emotion_line) in enumerate(
-                        zip(dialog_file, act_file, emotion_file)
-                    ):
-                        if not dialog_line.strip():
-                            break
-                        dialog = dialog_line.decode().split(self.__EOU__)[:-1]
-                        act = act_line.decode().split(" ")[:-1]
-                        emotion = emotion_line.decode().split(" ")[:-1]
-                        assert (
-                            len(dialog) == len(act) == len(emotion)
-                        ), "Different turns btw dialogue & emotion & action"
-                        yield idx, {
-                            "dialog": dialog,
-                            "act": [act_label[x] for x in act],
-                            "emotion": [emotion_label[x] for x in emotion],
-                        }

data/test-00000-of-00001.parquet

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+size 331465

data/train-00000-of-00001.parquet

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+size 3608039

data/validation-00000-of-00001.parquet

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+size 334236