feat(dev): add download script

daiso.py

@@ -0,0 +1,211 @@
+# 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+import textwrap
+import csv
+import pandas as pd
+import json
+import os
+
+import datasets
+
+_VERSION = datasets.Version("1.1.0")
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_DAISO_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={Igor Kuzmin
+},
+year={2023}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DAISO_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URL = "https://raw.githubusercontent.com/igorktech/DAISO-benchmark/dev"
+
+
+class DAISOConfig(datasets.BuilderConfig):
+    """BuilderConfig for DAISO."""
+
+    def __init__(self, features, data_url, citation, url, **kwargs):
+        """BuilderConfig for DAISO.
+        Args:
+          features: `list[string]`, list of the features that will appear in the
+            feature dict. Should not include "label".
+          data_url: `string`, url to download the csv file from.
+          citation: `string`, citation for the data set.
+          url: `string`, url for information about the data set.
+          label_classes: `list[string]`, the list of classes for the label if the
+            label is present as a string. Non-string labels will be cast to either
+            'False' or 'True'.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(DAISOConfig, self).__init__(version=_VERSION, **kwargs)
+        # self.label_classes = label_classes
+        self.features = features
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class DAISO(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        DAISOConfig(
+            name="",
+            description=textwrap.dedent(
+                """\
+                """
+            ),
+            features=[
+                "Utterance",
+                "Dialogue_Act",
+                "Emotion",
+                "Dialogue_ID",
+                "Dialogue_Act_ISO"
+            ],
+            data_url={
+                "train": _URL + "/dyda/train.csv",
+                "dev": _URL + "/dyda/dev.csv",
+                "test": _URL + "/dyda/test.csv",
+            },
+            citation=textwrap.dedent(
+                """\
+            @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}
+            }"""
+            ),
+            url="http://yanran.li/dailydialog.html",
+        )
+    ]
+
+    DEFAULT_CONFIG_NAME = "dyda"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        features = {feature: datasets.Value("string") for feature in self.config.features}
+        features["Idx"] = datasets.Value("int32")
+        # if self.config.name == "":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+        #     features = datasets.Features(
+        #         {
+        #             "sentence": datasets.Value("string"),
+        #             "option1": datasets.Value("string"),
+        #             "answer": datasets.Value("string")
+        #             # These are the features of your dataset like images, labels ...
+        #         }
+        #     )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DAISO_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=datasets.Features(features),
+            # Here we define them above because they are different between the two configurations
+            # Homepage of the dataset for documentation
+            homepage=self.config.url,
+            # License for the dataset if available
+            # license=_LICENSE,
+            # Citation for the dataset
+            citation=self.config.citation + "\n" + _DAISO_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        data_files = dl_manager.download(self.config.data_url)
+        splits = []
+        if "train" in data_files:
+            splits.append(datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "file": data_files["train"],
+                    "split": "train",
+                },
+            ))
+        if "dev" in data_files:
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "file": data_files["dev"],
+                    "split": "dev",
+                },
+            )
+        if "test" in data_files:
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "file": data_files["test"],
+                    "split": "test"
+                },
+            )
+        return splits
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, file, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        df = pd.read_csv(file, delimiter=",", header=0, quotechar='"', dtype=str)[
+            self.config.text_features.keys()
+        ]
+
+        rows = df.to_dict(orient="records")
+
+        for n, row in enumerate(rows):
+            example = row
+            example["Idx"] = n
+
+            if "Dialogue_Act" in example:
+                label = example["Dialogue_Act"]
+                example["Label"] = label
+
+            yield example["Idx"], example