Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Language Creators:
found
Annotations Creators:
found
Source Datasets:
extended|other-tweet-datasets
ArXiv:
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# 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. | |
"""The Tweet Eval Datasets""" | |
import datasets | |
_CITATION = """\ | |
@inproceedings{barbieri2020tweeteval, | |
title={{TweetEval:Unified Benchmark and Comparative Evaluation for Tweet Classification}}, | |
author={Barbieri, Francesco and Camacho-Collados, Jose and Espinosa-Anke, Luis and Neves, Leonardo}, | |
booktitle={Proceedings of Findings of EMNLP}, | |
year={2020} | |
} | |
""" | |
_DESCRIPTION = """\ | |
TweetEval consists of seven heterogenous tasks in Twitter, all framed as multi-class tweet classification. All tasks have been unified into the same benchmark, with each dataset presented in the same format and with fixed training, validation and test splits. | |
""" | |
_HOMEPAGE = "https://github.com/cardiffnlp/tweeteval" | |
_LICENSE = "" | |
URL = "https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/" | |
_URLs = { | |
"emoji": { | |
"train_text": URL + "emoji/train_text.txt", | |
"train_labels": URL + "emoji/train_labels.txt", | |
"test_text": URL + "emoji/test_text.txt", | |
"test_labels": URL + "emoji/test_labels.txt", | |
"val_text": URL + "emoji/val_text.txt", | |
"val_labels": URL + "emoji/val_labels.txt", | |
}, | |
"emotion": { | |
"train_text": URL + "emotion/train_text.txt", | |
"train_labels": URL + "emotion/train_labels.txt", | |
"test_text": URL + "emotion/test_text.txt", | |
"test_labels": URL + "emotion/test_labels.txt", | |
"val_text": URL + "emotion/val_text.txt", | |
"val_labels": URL + "emotion/val_labels.txt", | |
}, | |
"hate": { | |
"train_text": URL + "hate/train_text.txt", | |
"train_labels": URL + "hate/train_labels.txt", | |
"test_text": URL + "hate/test_text.txt", | |
"test_labels": URL + "hate/test_labels.txt", | |
"val_text": URL + "hate/val_text.txt", | |
"val_labels": URL + "hate/val_labels.txt", | |
}, | |
"irony": { | |
"train_text": URL + "irony/train_text.txt", | |
"train_labels": URL + "irony/train_labels.txt", | |
"test_text": URL + "irony/test_text.txt", | |
"test_labels": URL + "irony/test_labels.txt", | |
"val_text": URL + "irony/val_text.txt", | |
"val_labels": URL + "irony/val_labels.txt", | |
}, | |
"offensive": { | |
"train_text": URL + "offensive/train_text.txt", | |
"train_labels": URL + "offensive/train_labels.txt", | |
"test_text": URL + "offensive/test_text.txt", | |
"test_labels": URL + "offensive/test_labels.txt", | |
"val_text": URL + "offensive/val_text.txt", | |
"val_labels": URL + "offensive/val_labels.txt", | |
}, | |
"sentiment": { | |
"train_text": URL + "sentiment/train_text.txt", | |
"train_labels": URL + "sentiment/train_labels.txt", | |
"test_text": URL + "sentiment/test_text.txt", | |
"test_labels": URL + "sentiment/test_labels.txt", | |
"val_text": URL + "sentiment/val_text.txt", | |
"val_labels": URL + "sentiment/val_labels.txt", | |
}, | |
"stance": { | |
"abortion": { | |
"train_text": URL + "stance/abortion/train_text.txt", | |
"train_labels": URL + "stance/abortion/train_labels.txt", | |
"test_text": URL + "stance/abortion/test_text.txt", | |
"test_labels": URL + "stance/abortion/test_labels.txt", | |
"val_text": URL + "stance/abortion/val_text.txt", | |
"val_labels": URL + "stance/abortion/val_labels.txt", | |
}, | |
"atheism": { | |
"train_text": URL + "stance/atheism/train_text.txt", | |
"train_labels": URL + "stance/atheism/train_labels.txt", | |
"test_text": URL + "stance/atheism/test_text.txt", | |
"test_labels": URL + "stance/atheism/test_labels.txt", | |
"val_text": URL + "stance/atheism/val_text.txt", | |
"val_labels": URL + "stance/atheism/val_labels.txt", | |
}, | |
"climate": { | |
"train_text": URL + "stance/climate/train_text.txt", | |
"train_labels": URL + "stance/climate/train_labels.txt", | |
"test_text": URL + "stance/climate/test_text.txt", | |
"test_labels": URL + "stance/climate/test_labels.txt", | |
"val_text": URL + "stance/climate/val_text.txt", | |
"val_labels": URL + "stance/climate/val_labels.txt", | |
}, | |
"feminist": { | |
"train_text": URL + "stance/feminist/train_text.txt", | |
"train_labels": URL + "stance/feminist/train_labels.txt", | |
"test_text": URL + "stance/feminist/test_text.txt", | |
"test_labels": URL + "stance/feminist/test_labels.txt", | |
"val_text": URL + "stance/feminist/val_text.txt", | |
"val_labels": URL + "stance/feminist/val_labels.txt", | |
}, | |
"hillary": { | |
"train_text": URL + "stance/hillary/train_text.txt", | |
"train_labels": URL + "stance/hillary/train_labels.txt", | |
"test_text": URL + "stance/hillary/test_text.txt", | |
"test_labels": URL + "stance/hillary/test_labels.txt", | |
"val_text": URL + "stance/hillary/val_text.txt", | |
"val_labels": URL + "stance/hillary/val_labels.txt", | |
}, | |
}, | |
} | |
class TweetEvalConfig(datasets.BuilderConfig): | |
def __init__(self, *args, type=None, sub_type=None, **kwargs): | |
super().__init__( | |
*args, | |
name=f"{type}" if type != "stance" else f"{type}_{sub_type}", | |
**kwargs, | |
) | |
self.type = type | |
self.sub_type = sub_type | |
class TweetEval(datasets.GeneratorBasedBuilder): | |
"""TweetEval Dataset.""" | |
BUILDER_CONFIGS = [ | |
TweetEvalConfig( | |
type=key, | |
sub_type=None, | |
version=datasets.Version("1.1.0"), | |
description=f"This part of my dataset covers {key} part of TweetEval Dataset.", | |
) | |
for key in list(_URLs.keys()) | |
if key != "stance" | |
] + [ | |
TweetEvalConfig( | |
type="stance", | |
sub_type=key, | |
version=datasets.Version("1.1.0"), | |
description=f"This part of my dataset covers stance_{key} part of TweetEval Dataset.", | |
) | |
for key in list(_URLs["stance"].keys()) | |
] | |
def _info(self): | |
if self.config.type == "stance": | |
names = ["none", "against", "favor"] | |
elif self.config.type == "sentiment": | |
names = ["negative", "neutral", "positive"] | |
elif self.config.type == "offensive": | |
names = ["non-offensive", "offensive"] | |
elif self.config.type == "irony": | |
names = ["non_irony", "irony"] | |
elif self.config.type == "hate": | |
names = ["non-hate", "hate"] | |
elif self.config.type == "emoji": | |
names = [ | |
"β€", | |
"π", | |
"π", | |
"π", | |
"π₯", | |
"π", | |
"π", | |
"β¨", | |
"π", | |
"π", | |
"π·", | |
"πΊπΈ", | |
"β", | |
"π", | |
"π", | |
"π―", | |
"π", | |
"π", | |
"πΈ", | |
"π", | |
] | |
else: | |
names = ["anger", "joy", "optimism", "sadness"] | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=names)} | |
), | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
if self.config.type != "stance": | |
my_urls = _URLs[self.config.type] | |
else: | |
my_urls = _URLs[self.config.type][self.config.sub_type] | |
data_dir = dl_manager.download_and_extract(my_urls) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"text_path": data_dir["train_text"], "labels_path": data_dir["train_labels"]}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"text_path": data_dir["test_text"], "labels_path": data_dir["test_labels"]}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"text_path": data_dir["val_text"], "labels_path": data_dir["val_labels"]}, | |
), | |
] | |
def _generate_examples(self, text_path, labels_path): | |
"""Yields examples.""" | |
with open(text_path, encoding="utf-8") as f: | |
texts = f.readlines() | |
with open(labels_path, encoding="utf-8") as f: | |
labels = f.readlines() | |
for i, text in enumerate(texts): | |
yield i, {"text": text.strip(), "label": int(labels[i].strip())} | |