Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

dataset_infos.json

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+{"train": {"description": "Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter.\n", "citation": "@article{DBLP:journals/corr/LowePSP15,\n  author    = {Ryan Lowe and\n               Nissan Pow and\n               Iulian Serban and\n               Joelle Pineau},\n  title     = {The Ubuntu Dialogue Corpus: {A} Large Dataset for Research in Unstructured\n               Multi-Turn Dialogue Systems},\n  journal   = {CoRR},\n  volume    = {abs/1506.08909},\n  year      = {2015},\n  url       = {http://arxiv.org/abs/1506.08909},\n  archivePrefix = {arXiv},\n  eprint    = {1506.08909},\n  timestamp = {Mon, 13 Aug 2018 16:48:23 +0200},\n  biburl    = {https://dblp.org/rec/journals/corr/LowePSP15.bib},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://github.com/rkadlec/ubuntu-ranking-dataset-creator", "license": "", "features": {"Context": {"dtype": "string", "id": null, "_type": "Value"}, "Utterance": {"dtype": "string", "id": null, "_type": "Value"}, "Label": {"dtype": "int32", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "ubuntu_dialogs_corpus", "config_name": "train", "version": {"version_str": "2.0.0", "description": null, "datasets_version_to_prepare": null, "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 65497027, "num_examples": 127422, "dataset_name": "ubuntu_dialogs_corpus"}}, "download_checksums": {}, "download_size": 0, "dataset_size": 65497027, "size_in_bytes": 65497027}}

dummy/dev_test/2.0.0/dummy_data.zip

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+oid sha256:cd0d07d667dea3347a6dbe380ad1040e94ef149153909c7b20425cbb048001df
+size 1824

dummy/train/2.0.0/dummy_data.zip

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+size 1152

ubuntu_dialogs_corpus.py

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+"""TODO(ubuntu_dialogs_corpus): Add a description here."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+# TODO(ubuntu_dialogs_corpus): BibTeX citation
+_CITATION = """\
+@article{DBLP:journals/corr/LowePSP15,
+  author    = {Ryan Lowe and
+               Nissan Pow and
+               Iulian Serban and
+               Joelle Pineau},
+  title     = {The Ubuntu Dialogue Corpus: {A} Large Dataset for Research in Unstructured
+               Multi-Turn Dialogue Systems},
+  journal   = {CoRR},
+  volume    = {abs/1506.08909},
+  year      = {2015},
+  url       = {http://arxiv.org/abs/1506.08909},
+  archivePrefix = {arXiv},
+  eprint    = {1506.08909},
+  timestamp = {Mon, 13 Aug 2018 16:48:23 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/LowePSP15.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+# TODO(ubuntu_dialogs_corpus):
+_DESCRIPTION = """\
+Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter.
+"""
+
+
+class UbuntuDialogsCorpusConfig(datasets.BuilderConfig):
+    """BuilderConfig for UbuntuDialogsCorpus."""
+
+    def __init__(self, features, **kwargs):
+        """BuilderConfig for UbuntuDialogsCorpus.
+
+        Args:
+
+          **kwargs: keyword arguments forwarded to super.
+        """
+
+        super(UbuntuDialogsCorpusConfig, self).__init__(version=datasets.Version("2.0.0"), **kwargs)
+        self.features = features
+
+
+class UbuntuDialogsCorpus(datasets.GeneratorBasedBuilder):
+    """TODO(ubuntu_dialogs_corpus): Short description of my dataset."""
+
+    # TODO(ubuntu_dialogs_corpus): Set up version.
+    VERSION = datasets.Version("2.0.0")
+    BUILDER_CONFIGS = [
+        UbuntuDialogsCorpusConfig(
+            name="train", features=["Context", "Utterance", "Label"], description="training features"
+        ),
+        UbuntuDialogsCorpusConfig(
+            name="dev_test",
+            features=["Context", "Ground Truth Utterance"] + ["Distractor_" + str(i) for i in range(9)],
+            description="test and dev features",
+        ),
+    ]
+
+    @property
+    def manual_download_instructions(self):
+        return """\
+  Please download the Ubuntu Dialog Corpus from https://github.com/rkadlec/ubuntu-ranking-dataset-creator. Run ./generate.sh -t -s -l to download the
+   data. Others arguments are left to their default values here. Please save train.csv, test.csv and valid.csv in the same path"""
+
+    def _info(self):
+        # TODO(ubuntu_dialogs_corpus): Specifies the datasets.DatasetInfo object
+        features = {feature: datasets.Value("string") for feature in self.config.features}
+        if self.config.name == "train":
+            features["Label"] = datasets.Value("int32")
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                # These are the features of your dataset like images, labels ...
+                features
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="https://github.com/rkadlec/ubuntu-ranking-dataset-creator",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(ubuntu_dialogs_corpus): Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        manual_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+
+        if self.config.name == "train":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"filepath": os.path.join(manual_dir, "train.csv")},
+                ),
+            ]
+        else:
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"filepath": os.path.join(manual_dir, "test.csv")},
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={"filepath": os.path.join(manual_dir, "valid.csv")},
+                ),
+            ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        # TODO(ubuntu_dialogs_corpus): Yields (key, example) tuples from the dataset
+        with open(filepath, encoding="utf-8") as f:
+            data = csv.DictReader(f)
+            for id_, row in enumerate(data):
+                yield id_, row