| # Copyright 2020 The HuggingFace Datasets Authors, the initial dataset script creator (Nouha Drizi), | |
| # the current dataset script contributor (Abbas Ghaddar). | |
| # | |
| # 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. | |
| """CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems""" | |
| import json | |
| import datasets | |
| from datasets import NamedSplit | |
| # Find for instance the citation on arxiv or on the dataset repo/website | |
| _CITATION = """\ | |
| @article{ghaddar2024charp, | |
| title={CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems}, | |
| author={Abbas Ghaddar and David Alfonso-Hermelo and Philippe Langlais and Mehdi Rezagholizadeh and Boxing Chen and Prasanna Parthasarathi}, | |
| year={2024}, | |
| eprint={2405.15110}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL} | |
| } | |
| """ | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| CHARP is a testbed, designed for evaluating supposedly non-hallucinatory models abilities to reason over the conversational history of knowledge-grounded dialogue systems. | |
| """ | |
| _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 = { | |
| "eCHARP": "data/eCHARP.json", | |
| "hCHARP": "data/hCHARP.json" | |
| } | |
| class CHARPDataset(datasets.GeneratorBasedBuilder): | |
| """CHARP is a new benchmark for evaluating contextual history reasoning abilities of knowledge-grounded dialogue systems.""" | |
| 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( | |
| { | |
| "row_idx": datasets.Value("int32"), | |
| "history": datasets.features.Sequence(datasets.Value("string")), | |
| "knowledge": datasets.Value("string"), | |
| "response": 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"), | |
| # 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 = { | |
| "eCHARP": NamedSplit("eCHARP"), | |
| "hCHARP": NamedSplit("hCHARP") | |
| } | |
| 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: | |
| rows = json.load(f) | |
| print(type(rows)) | |
| key = 0 | |
| for row in rows: | |
| print(row) | |
| yield key, { | |
| "row_idx": row["row_idx"], | |
| "history": row["history"], | |
| "knowledge": row["knowledge"], | |
| "response": row["response"] | |
| } | |
| key += 1 |