Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
ArXiv:
License:
# coding=utf-8 | |
# Copyright 2020 HuggingFace Datasets Authors. | |
# | |
# 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. | |
# Lint as: python3 | |
"""GoEmotions dataset""" | |
import csv | |
import os | |
import datasets | |
_DESCRIPTION = """\ | |
The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral. | |
The emotion categories are admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, | |
disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, | |
optimism, pride, realization, relief, remorse, sadness, surprise. | |
""" | |
_CITATION = """\ | |
@inproceedings{demszky2020goemotions, | |
author = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith}, | |
booktitle = {58th Annual Meeting of the Association for Computational Linguistics (ACL)}, | |
title = {{GoEmotions: A Dataset of Fine-Grained Emotions}}, | |
year = {2020} | |
} | |
""" | |
_CLASS_NAMES = [ | |
"admiration", | |
"amusement", | |
"anger", | |
"annoyance", | |
"approval", | |
"caring", | |
"confusion", | |
"curiosity", | |
"desire", | |
"disappointment", | |
"disapproval", | |
"disgust", | |
"embarrassment", | |
"excitement", | |
"fear", | |
"gratitude", | |
"grief", | |
"joy", | |
"love", | |
"nervousness", | |
"optimism", | |
"pride", | |
"realization", | |
"relief", | |
"remorse", | |
"sadness", | |
"surprise", | |
"neutral", | |
] | |
_BASE_DOWNLOAD_URL = "https://github.com/google-research/google-research/raw/master/goemotions/data/" | |
_RAW_DOWNLOAD_URLS = [ | |
"https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_1.csv", | |
"https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_2.csv", | |
"https://storage.googleapis.com/gresearch/goemotions/data/full_dataset/goemotions_3.csv", | |
] | |
_HOMEPAGE = "https://github.com/google-research/google-research/tree/master/goemotions" | |
class GoEmotionsConfig(datasets.BuilderConfig): | |
def features(self): | |
if self.name == "simplified": | |
return { | |
"text": datasets.Value("string"), | |
"labels": datasets.Sequence(datasets.ClassLabel(names=_CLASS_NAMES)), | |
"id": datasets.Value("string"), | |
} | |
elif self.name == "raw": | |
d = { | |
"text": datasets.Value("string"), | |
"id": datasets.Value("string"), | |
"author": datasets.Value("string"), | |
"subreddit": datasets.Value("string"), | |
"link_id": datasets.Value("string"), | |
"parent_id": datasets.Value("string"), | |
"created_utc": datasets.Value("float"), | |
"rater_id": datasets.Value("int32"), | |
"example_very_unclear": datasets.Value("bool"), | |
} | |
d.update({label: datasets.Value("int32") for label in _CLASS_NAMES}) | |
return d | |
class GoEmotions(datasets.GeneratorBasedBuilder): | |
"""GoEmotions dataset""" | |
BUILDER_CONFIGS = [ | |
GoEmotionsConfig( | |
name="raw", | |
), | |
GoEmotionsConfig( | |
name="simplified", | |
), | |
] | |
BUILDER_CONFIG_CLASS = GoEmotionsConfig | |
DEFAULT_CONFIG_NAME = "simplified" | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features(self.config.features), | |
homepage=_HOMEPAGE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
if self.config.name == "raw": | |
paths = dl_manager.download_and_extract(_RAW_DOWNLOAD_URLS) | |
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": paths, "raw": True})] | |
if self.config.name == "simplified": | |
train_path = dl_manager.download_and_extract(os.path.join(_BASE_DOWNLOAD_URL, "train.tsv")) | |
dev_path = dl_manager.download_and_extract(os.path.join(_BASE_DOWNLOAD_URL, "dev.tsv")) | |
test_path = dl_manager.download_and_extract(os.path.join(_BASE_DOWNLOAD_URL, "test.tsv")) | |
return [ | |
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": [train_path]}), | |
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": [dev_path]}), | |
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": [test_path]}), | |
] | |
def _generate_examples(self, filepaths, raw=False): | |
"""Generate AG News examples.""" | |
for file_idx, filepath in enumerate(filepaths): | |
with open(filepath, "r", encoding="utf-8") as f: | |
if raw: | |
reader = csv.DictReader(f) | |
else: | |
reader = csv.DictReader(f, delimiter="\t", fieldnames=list(self.config.features.keys())) | |
for row_idx, row in enumerate(reader): | |
if raw: | |
row["example_very_unclear"] = row["example_very_unclear"] == "TRUE" | |
else: | |
row["labels"] = [int(ind) for ind in row["labels"].split(",")] | |
yield f"{file_idx}_{row_idx}", row | |