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Update files from the datasets library (from 1.0.0)

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

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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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dataset_infos.json ADDED
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+ {"default": {"description": "PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset\n", "citation": "@inproceedings{rashkin2019towards,\n title = {Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset},\n author = {Hannah Rashkin and Eric Michael Smith and Margaret Li and Y-Lan Boureau},\n booktitle = {ACL},\n year = {2019},\n}\n", "homepage": "https://github.com/facebookresearch/EmpatheticDialogues", "license": "", "features": {"conv_id": {"dtype": "string", "id": null, "_type": "Value"}, "utterance_idx": {"dtype": "int32", "id": null, "_type": "Value"}, "context": {"dtype": "string", "id": null, "_type": "Value"}, "prompt": {"dtype": "string", "id": null, "_type": "Value"}, "speaker_idx": {"dtype": "int32", "id": null, "_type": "Value"}, "utterance": {"dtype": "string", "id": null, "_type": "Value"}, "selfeval": {"dtype": "string", "id": null, "_type": "Value"}, "tags": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "empathetic_dialogues", "config_name": "default", "version": {"version_str": "0.1.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 3011332, "num_examples": 10943, "dataset_name": "empathetic_dialogues"}, "train": {"name": "train", "num_bytes": 19040509, "num_examples": 76673, "dataset_name": "empathetic_dialogues"}, "validation": {"name": "validation", "num_bytes": 3077481, "num_examples": 12030, "dataset_name": "empathetic_dialogues"}}, "download_checksums": {"https://dl.fbaipublicfiles.com/parlai/empatheticdialogues/empatheticdialogues.tar.gz": {"num_bytes": 28022709, "checksum": "56f234d77b7dd1f005fd365bb17769cfe346c3c84295b69bc069c8ccb83be03d"}}, "download_size": 28022709, "dataset_size": 25129322, "size_in_bytes": 53152031}}
dummy/0.1.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:236f42ae807d4795c7569fd0cb5e22614d5da4c292bbe859c6077c226fc01772
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+ size 1800
empathetic_dialogues.py ADDED
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+ """TODO(empathetic_dialogues): Add a description here."""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import csv
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+ import os
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+
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+ import datasets
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+
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+
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+ _CITATION = """\
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+ @inproceedings{rashkin2019towards,
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+ title = {Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset},
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+ author = {Hannah Rashkin and Eric Michael Smith and Margaret Li and Y-Lan Boureau},
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+ booktitle = {ACL},
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+ year = {2019},
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
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+ """
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+ _URL = "https://dl.fbaipublicfiles.com/parlai/empatheticdialogues/empatheticdialogues.tar.gz"
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+
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+
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+ class EmpatheticDialogues(datasets.GeneratorBasedBuilder):
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+ """TODO(empathetic_dialogues): Short description of my dataset."""
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+
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+ # TODO(empathetic_dialogues): Set up version.
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+ VERSION = datasets.Version("0.1.0")
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+
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+ def _info(self):
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+ # TODO(empathetic_dialogues): Specifies the datasets.DatasetInfo object
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+ return datasets.DatasetInfo(
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+ # This is the description that will appear on the datasets page.
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+ description=_DESCRIPTION,
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+ # datasets.features.FeatureConnectors
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+ features=datasets.Features(
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+ {
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+ "conv_id": datasets.Value("string"),
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+ "utterance_idx": datasets.Value("int32"),
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+ "context": datasets.Value("string"),
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+ "prompt": datasets.Value("string"),
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+ "speaker_idx": datasets.Value("int32"),
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+ "utterance": datasets.Value("string"),
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+ "selfeval": datasets.Value("string"),
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+ "tags": datasets.Value("string")
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+ # These are the features of your dataset like images, labels ...
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+ }
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+ ),
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+ # If there's a common (input, target) tuple from the features,
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+ # specify them here. They'll be used if as_supervised=True in
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+ # builder.as_dataset.
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+ supervised_keys=None,
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+ # Homepage of the dataset for documentation
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+ homepage="https://github.com/facebookresearch/EmpatheticDialogues",
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ """Returns SplitGenerators."""
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+ # TODO(empathetic_dialogues): Downloads the data and defines the splits
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+ # dl_manager is a datasets.download.DownloadManager that can be used to
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+ # download and extract URLs
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+ dl_dir = dl_manager.download_and_extract(_URL)
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+ data_dir = os.path.join(dl_dir, "empatheticdialogues")
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={"filepath": os.path.join(data_dir, "train.csv")},
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={"filepath": os.path.join(data_dir, "valid.csv")},
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={"filepath": os.path.join(data_dir, "test.csv")},
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+ ),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ """Yields examples."""
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+ # TODO(empathetic_dialogues): Yields (key, example) tuples from the dataset
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+ with open(filepath, encoding="utf-8") as f:
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+ data = csv.DictReader(f)
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+ for id_, row in enumerate(data):
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+ utterance = row["utterance"]
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+ speaker_id = int(row["speaker_idx"])
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+ context = row["context"]
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+ conv_id = row["conv_id"]
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+ tags = row["tags"] if row["tags"] else ""
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+ selfeval = row["selfeval"] if row["selfeval"] else ""
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+ utterance_id = int(row["utterance_idx"])
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+ prompt = row["prompt"]
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+ yield id_, {
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+ "utterance": utterance,
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+ "utterance_idx": utterance_id,
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+ "context": context,
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+ "speaker_idx": speaker_id,
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+ "conv_id": conv_id,
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+ "selfeval": selfeval,
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+ "prompt": prompt,
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+ "tags": tags,
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+ }