First version of FaithDial

FaithDial.py

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+# 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

data/dummy/plain_text/1.0.0/dummy_data.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7d44546ee0397c1634eadfc40f347c0f24d5c5950570cb349b4bf234214ec56a
+size 28210

dataset_infos.json

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+{"plain_text": {"description": "FaithDial is a new benchmark for hallucination-free dialogues, created by manually editing hallucinated and uncooperative responses in Wizard of Wikipedia.\n", "citation": "@article{dziri2022faithdial,\n  title={FaithDial: A Faithful Benchmark for Information-Seeking Dialogue},\n  author={Dziri, Nouha and Kamalloo, Ehsan and Milton, Sivan and Zaiane, Osmar and Yu, Mo and Ponti, Edoardo and Reddy, Siva},\n  journal={arXiv preprint, arXiv:2204.xxxxx},\n  year={2022},\n  url={https://arxiv.org/abs/2204.xxxxx}\n}\n", "homepage": "https://mcgill-nlp.github.io/FaithDial/", "license": "MIT", "features": {"dialog_idx": {"dtype": "int32", "id": null, "_type": "Value"}, "response": {"dtype": "string", "id": null, "_type": "Value"}, "original_response": {"dtype": "string", "id": null, "_type": "Value"}, "history": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "knowledge": {"dtype": "string", "id": null, "_type": "Value"}, "BEGIN": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "VRM": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "faith_dial_dataset", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 2770907, "num_examples": 3539, "dataset_name": "faith_dial_dataset"}, "test_random_split": {"name": "test_random_split", "num_bytes": 1356913, "num_examples": 1716, "dataset_name": "faith_dial_dataset"}, "test_topic_split": {"name": "test_topic_split", "num_bytes": 1416547, "num_examples": 1823, "dataset_name": "faith_dial_dataset"}, "train": {"name": "train", "num_bytes": 14393919, "num_examples": 18357, "dataset_name": "faith_dial_dataset"}, "validation": {"name": "validation", "num_bytes": 2703363, "num_examples": 3417, "dataset_name": "faith_dial_dataset"}, "valid_random_split": {"name": "valid_random_split", "num_bytes": 1346226, "num_examples": 1666, "dataset_name": "faith_dial_dataset"}, "valid_topic_split": {"name": "valid_topic_split", "num_bytes": 1358013, "num_examples": 1751, "dataset_name": "faith_dial_dataset"}}, "download_checksums": {"data/train.json": {"num_bytes": 19499858, "checksum": "51eff8212d804b1954b7eefa8987265750f0412b0b18b534ed2dbe636e524512"}, "data/valid.json": {"num_bytes": 3650206, "checksum": "0f4da554dc07a2f5a14b96d6997f6e21358c6e7c159161526fc55a7f39751d5f"}, "data/valid_random_split.json": {"num_bytes": 1808271, "checksum": "1ca3a70c71fc212f399679b8090d8edad733c2c7d2672a2f1960bec466de596f"}, "data/valid_topic_split.json": {"num_bytes": 1842813, "checksum": "e3b161e6bd1a83bb8cabe0ff07530649473f485cd61c76048bf12cfa5ebbb751"}, "data/test.json": {"num_bytes": 3749121, "checksum": "acae13df566cdb2bf28274465a9b314dd0f66211fb9d40781c3c6922df33f674"}, "data/test_random_split.json": {"num_bytes": 1831274, "checksum": "6b8779a7442e5c60378b4a6ac7ce0681fc0923dc8a3189ec804abd9cad8b916b"}, "data/test_topic_split.json": {"num_bytes": 1920402, "checksum": "ee966f854b52688810209b2d93afba223151fa3f6d8e2df311cd88ac86ff5f4d"}}, "download_size": 34301945, "post_processing_size": null, "dataset_size": 25345888, "size_in_bytes": 59647833}}