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# 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 = [
"❤",
"😍",
"😂",
"💕",
"🔥",
"😊",
"😎",
"✨",
"💙",
"😘",
"📷",
"🇺🇸",
"☀",
"💜",
"😉",
"💯",
"😁",
"🎄",
"📸",
"😜",
]
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())}
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