Hugging Face's logo

Datasets:
McGill-NLP
/
FaithDial

Tasks:
Text Generation
Modalities:
Text
Sub-tasks:
dialogue-modeling
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
faithful-dialogue-modeling
trustworthy-dialogue-modeling
Libraries:
Datasets
License:
Dataset card Viewer Files Community
2
FaithDial
File size: 6,787 Bytes 
fcee47c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
 
# 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