# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor (Nouha Dziri). # # 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. """FaithDial: A Faithful Benchmark for Information-Seeking Dialogue""" import json import datasets # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @article{dziri2022faithdial, title={FaithDial: A Faithful Benchmark for Information-Seeking Dialogue}, author={Dziri, Nouha and Kamalloo, Ehsan and Milton, Sivan and Zaiane, Osmar and Yu, Mo and Ponti, Edoardo and Reddy, Siva}, journal={arXiv preprint, arXiv:2204.xxxxx}, year={2022}, url={https://arxiv.org/abs/2204.xxxxx} } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ FaithDial is a new benchmark for hallucination-free dialogues, created by manually editing hallucinated and uncooperative responses in Wizard of Wikipedia. """ _HOMEPAGE = "https://mcgill-nlp.github.io/FaithDial/" _LICENSE = "MIT" # 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) _URLS = { "train": "data/train.json", "valid": "data/valid.json", "valid_random_split": "data/valid_random_split.json", "valid_topic_split": "data/valid_topic_split.json", "test": "data/test.json", "test_random_split": "data/test_random_split.json", "test_topic_split": "data/test_topic_split.json", } class FaithDialDataset(datasets.GeneratorBasedBuilder): """FaithDial is a new benchmark for hallucination-free dialogues.""" VERSION = datasets.Version("1.0.0") # 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 = [ datasets.BuilderConfig(name="plain_text", version=VERSION, description="Plain text"), ] DEFAULT_CONFIG_NAME = ( "plain_text" # It's not mandatory to have a default configuration. Just use one if it make sense. ) def _info(self): features = datasets.Features( { "dialog_idx": datasets.Value("int32"), "response": datasets.Value("string"), "original_response": datasets.Value("string"), "history": datasets.features.Sequence(datasets.Value("string")), "knowledge": datasets.Value("string"), "BEGIN": datasets.features.Sequence(datasets.Value("string")), "VRM": datasets.features.Sequence(datasets.Value("string")), } ) 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, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): # 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 downloaded_files = dl_manager.download_and_extract(_URLS) split_dict = { "train": datasets.Split.TRAIN, "valid": datasets.Split.VALIDATION, "test": datasets.Split.TEST, } return [ datasets.SplitGenerator( name=split_dict.get(split, split), # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": downloaded_file, "split": split, }, ) for split, downloaded_file in sorted(downloaded_files.items(), key=lambda x: x[0]) ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split): # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. with open(filepath, encoding="utf-8") as f: data = json.load(f) key = 0 for dialogue in data: for utterance in dialogue["utterances"]: yield key, { "dialog_idx": dialogue["dialog_idx"], "response": utterance["response"], "original_response": utterance["original_response"], "history": utterance["history"], "knowledge": utterance["knowledge"], "BEGIN": utterance["BEGIN"], "VRM": utterance["VRM"], } key += 1