Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.xz filter=lfs diff=lfs merge=lfs -text
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+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

daily_dialog.py

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+# 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"""
+
+from __future__ import absolute_import, division, print_function
+
+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):
+        """Returns SplitGenerators."""
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        dl_dir = dl_manager.download_and_extract(_URL)
+        data_dir = os.path.join(dl_dir, "ijcnlp_dailydialog")
+
+        # The splits are nested inside the zip
+        for name in ("train", "validation", "test"):
+            zip_fpath = os.path.join(data_dir, f"{name}.zip")
+            with ZipFile(zip_fpath) as zip_file:
+                zip_file.extractall(path=data_dir)
+                zip_file.close()
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "file_path": os.path.join(data_dir, "train", "dialogues_train.txt"),
+                    "act_path": os.path.join(data_dir, "train", "dialogues_act_train.txt"),
+                    "emotion_path": os.path.join(data_dir, "train", "dialogues_emotion_train.txt"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "file_path": os.path.join(data_dir, "test", "dialogues_test.txt"),
+                    "act_path": os.path.join(data_dir, "test", "dialogues_act_test.txt"),
+                    "emotion_path": os.path.join(data_dir, "test", "dialogues_emotion_test.txt"),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "file_path": os.path.join(data_dir, "validation", "dialogues_validation.txt"),
+                    "act_path": os.path.join(data_dir, "validation", "dialogues_act_validation.txt"),
+                    "emotion_path": os.path.join(data_dir, "validation", "dialogues_emotion_validation.txt"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, file_path, act_path, emotion_path, split):
+        """ Yields examples. """
+        # Yields (key, example) tuples from the dataset
+        with open(file_path, "r", encoding="utf-8") as f, open(act_path, "r", encoding="utf-8") as act, open(
+            emotion_path, "r", encoding="utf-8"
+        ) as emotion:
+            for i, (line_f, line_act, line_emotion) in enumerate(zip(f, act, emotion)):
+                if len(line_f.strip()) == 0:
+                    break
+                dialog = line_f.split(self.__EOU__)[:-1]
+                act = line_act.split(" ")[:-1]
+                emotion = line_emotion.split(" ")[:-1]
+
+                assert len(dialog) == len(act) == len(emotion), "Different turns btw dialogue & emotion & action"
+
+                yield f"{split}-{i}", {
+                    "dialog": dialog,
+                    "act": [act_label[x] for x in act],
+                    "emotion": [emotion_label[x] for x in emotion],
+                }

dataset_infos.json

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+{"default": {"description": "We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. \nThe language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way \nand cover various topics about our daily life. We also manually label the developed dataset with communication \nintention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it \nbenefit the research field of dialog systems.\n", "citation": "@InProceedings{li2017dailydialog,\n    author = {Li, Yanran and Su, Hui and Shen, Xiaoyu and Li, Wenjie and Cao, Ziqiang and Niu, Shuzi},\n    title = {DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset},\n    booktitle = {Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017)},\n    year = {2017}\n}\n", "homepage": "http://yanran.li/dailydialog", "license": "", "features": {"dialog": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "act": {"feature": {"num_classes": 5, "names": ["__dummy__", "inform", "question", "directive", "commissive"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "emotion": {"feature": {"num_classes": 7, "names": ["no emotion", "anger", "disgust", "fear", "happiness", "sadness", "surprise"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": {"features": null, "resources_checksums": {"train": {}, "test": {}, "validation": {}}}, "supervised_keys": null, "builder_name": "daily_dialog", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7296715, "num_examples": 11118, "dataset_name": "daily_dialog"}, "test": {"name": "test", "num_bytes": 655844, "num_examples": 1000, "dataset_name": "daily_dialog"}, "validation": {"name": "validation", "num_bytes": 673943, "num_examples": 1000, "dataset_name": "daily_dialog"}}, "download_checksums": {"http://yanran.li/files/ijcnlp_dailydialog.zip": {"num_bytes": 4475921, "checksum": "c641e88cbf21fd7c1b57289387f9107d33fe8685a2b37fe8066b82776535ea89"}}, "download_size": 4475921, "post_processing_size": 0, "dataset_size": 8626502, "size_in_bytes": 13102423}}

dummy/1.0.0/dummy_data.zip

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+size 3744