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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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  1. .gitattributes +27 -0
  2. dataset_infos.json +1 -0
  3. dummy/0.0.0/dummy_data.zip +3 -0
  4. emotion.py +67 -0
.gitattributes ADDED
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
dataset_infos.json ADDED
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+ {"emotion": {"description": "Emotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation,\ndisgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the\npaper.\n", "citation": "@inproceedings{saravia-etal-2018-carer,\n title = \"{CARER}: Contextualized Affect Representations for Emotion Recognition\",\n author = \"Saravia, Elvis and\n Liu, Hsien-Chi Toby and\n Huang, Yen-Hao and\n Wu, Junlin and\n Chen, Yi-Shin\",\n booktitle = \"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing\",\n month = oct # \"-\" # nov,\n year = \"2018\",\n address = \"Brussels, Belgium\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/D18-1404\",\n doi = \"10.18653/v1/D18-1404\",\n pages = \"3687--3697\",\n abstract = \"Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.\",\n}\n", "homepage": "https://github.com/dair-ai/emotion_dataset", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "emotion", "config_name": "emotion", "version": {"version_str": "0.1.0", "description": "First Emotion release", "datasets_version_to_prepare": null, "major": 0, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1754632, "num_examples": 16000, "dataset_name": "emotion"}, "validation": {"name": "validation", "num_bytes": 216248, "num_examples": 2000, "dataset_name": "emotion"}, "test": {"name": "test", "num_bytes": 218768, "num_examples": 2000, "dataset_name": "emotion"}}, "download_checksums": {"https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1": {"num_bytes": 1658616, "checksum": "3ab03d945a6cb783d818ccd06dafd52d2ed8b4f62f0f85a09d7d11870865b190"}, "https://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt?dl=1": {"num_bytes": 204240, "checksum": "34faaa31962fe63cdf5dbf6c132ef8ab166c640254ab991af78f3aea375e79ef"}, "https://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt?dl=1": {"num_bytes": 206760, "checksum": "60f531690d20127339e7f054edc299a82c627b5ec0dd5d552d53d544e0cfcc17"}}, "download_size": 2069616, "dataset_size": 2189648, "size_in_bytes": 4259264}, "default": {"description": "Emotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation,\ndisgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the\npaper.\n", "citation": "@inproceedings{saravia-etal-2018-carer,\n title = \"{CARER}: Contextualized Affect Representations for Emotion Recognition\",\n author = \"Saravia, Elvis and\n Liu, Hsien-Chi Toby and\n Huang, Yen-Hao and\n Wu, Junlin and\n Chen, Yi-Shin\",\n booktitle = \"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing\",\n month = oct # \"-\" # nov,\n year = \"2018\",\n address = \"Brussels, Belgium\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/D18-1404\",\n doi = \"10.18653/v1/D18-1404\",\n pages = \"3687--3697\",\n abstract = \"Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.\",\n}\n", "homepage": "https://github.com/dair-ai/emotion_dataset", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "emotion", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1754632, "num_examples": 16000, "dataset_name": "emotion"}, "validation": {"name": "validation", "num_bytes": 216248, "num_examples": 2000, "dataset_name": "emotion"}, "test": {"name": "test", "num_bytes": 218768, "num_examples": 2000, "dataset_name": "emotion"}}, "download_checksums": {"https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1": {"num_bytes": 1658616, "checksum": "3ab03d945a6cb783d818ccd06dafd52d2ed8b4f62f0f85a09d7d11870865b190"}, "https://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt?dl=1": {"num_bytes": 204240, "checksum": "34faaa31962fe63cdf5dbf6c132ef8ab166c640254ab991af78f3aea375e79ef"}, "https://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt?dl=1": {"num_bytes": 206760, "checksum": "60f531690d20127339e7f054edc299a82c627b5ec0dd5d552d53d544e0cfcc17"}}, "download_size": 2069616, "dataset_size": 2189648, "size_in_bytes": 4259264}}
dummy/0.0.0/dummy_data.zip ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:c60e08161d1303b9f97eec1f180176fb8d63ec750dc1ac2bbbe3595e967375d1
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+ size 283
emotion.py ADDED
@@ -0,0 +1,67 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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+ import datasets
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+
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+
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+ _CITATION = """\
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+ @inproceedings{saravia-etal-2018-carer,
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+ title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
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+ author = "Saravia, Elvis and
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+ Liu, Hsien-Chi Toby and
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+ Huang, Yen-Hao and
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+ Wu, Junlin and
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+ Chen, Yi-Shin",
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+ booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
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+ month = oct # "-" # nov,
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+ year = "2018",
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+ address = "Brussels, Belgium",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://www.aclweb.org/anthology/D18-1404",
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+ doi = "10.18653/v1/D18-1404",
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+ pages = "3687--3697",
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+ abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
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+ }
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+ """
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+
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+ _DESCRIPTION = """\
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+ Emotion is a dataset of English Twitter messages with eight basic emotions: anger, anticipation,
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+ disgust, fear, joy, sadness, surprise, and trust. For more detailed information please refer to the
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+ paper.
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+ """
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+ _URL = "https://github.com/dair-ai/emotion_dataset"
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+ # use dl=1 to force browser to download data instead of displaying it
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+ _TRAIN_DOWNLOAD_URL = "https://www.dropbox.com/s/1pzkadrvffbqw6o/train.txt?dl=1"
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+ _VALIDATION_DOWNLOAD_URL = "https://www.dropbox.com/s/2mzialpsgf9k5l3/val.txt?dl=1"
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+ _TEST_DOWNLOAD_URL = "https://www.dropbox.com/s/ikkqxfdbdec3fuj/test.txt?dl=1"
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+
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+
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+ class Emotion(datasets.GeneratorBasedBuilder):
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features({"text": datasets.Value("string"), "label": datasets.Value("string")}),
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+ supervised_keys=("text", "label"),
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+ homepage=_URL,
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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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+ train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
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+ valid_path = dl_manager.download_and_extract(_VALIDATION_DOWNLOAD_URL)
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+ test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
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+ return [
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+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
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+ datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
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+ datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
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+ ]
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+
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+ def _generate_examples(self, filepath):
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+ """Generate examples."""
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+ with open(filepath, encoding="utf-8") as csv_file:
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+ csv_reader = csv.reader(csv_file, delimiter=";")
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+ for id_, row in enumerate(csv_reader):
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+ text, label = row
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+ yield id_, {"text": text, "label": label}