# 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,label_classes, 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="dyda", description=textwrap.dedent( """\ """ ), label_classes = {"commissive": { "base": "Commissive", "ISO": "commissive" }, "directive": { "base": "Directive", "ISO": "directive" }, "inform": { "base": "Inform", "ISO": "inform" }, "question": { "base": "Question", "ISO": "" } }, 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} if self.config.label_classes: features["Label"] = datasets.features.ClassLabel(names=list(self.config.label_classes.keys())) features["Label_ISO"] = datasets.features.ClassLabel(names=[ map["ISO"] for map in self.config.label_classes.values()]) 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: splits.append(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: splits.append(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.features ] 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 example["Label_ISO"] = self.config.label_classes.get(label, {}).get("ISO") yield example["Idx"], example