Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
sentiment-classification
Languages:
English
Size:
1K - 10K
License:
File size: 5,494 Bytes
abd0271 |
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# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""SemEval 2015: Aspect-based Sentiment Analysis"""
import csv
import json
import os
import datasets
_DESCRIPTION = """\
This dataset is built as a playground for aspect-based sentiment analysis.
"""
_HOMEPAGE = "https://alt.qcri.org/semeval2015/"
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_TRAIN_LAPTOP_URL = "https://drive.google.com/uc?id=1Zvh4bZOZgSkIHrrA5WVvyPQO6-wWk4xQ"
_VAL_LAPTOP_URL = "https://drive.google.com/uc?id=14NgRdqcEHFfki0z49iMR8wqOEBnqdLH9"
_TRAIN_RESTAURANT_URL = "https://drive.google.com/uc?id=1fx1fWemdTYjonYSVfX-vcgU3KQa7C85V"
_VAL_RESTAURANT_URL = "https://drive.google.com/uc?id=1fHD0USeUgiLrnTo6zvRajk8whvsTVdAX"
DOMAINS = ['laptop', 'restaurant']
class ABSAConfig(datasets.BuilderConfig):
"""SemEval 2015 - ABSA Configs"""
def __init__(self, domain: str, **kwargs):
if domain not in DOMAINS:
raise ValueError(f"Invalild domain: {domain}. Available domains: {DOMAINS}",)
name = domain
super(ABSAConfig, self).__init__(name=name, description=_DESCRIPTION, **kwargs)
self.domain = domain
self.url_train = _TRAIN_LAPTOP_URL if domain == 'laptop' else _TRAIN_RESTAURANT_URL
self.url_val = _VAL_LAPTOP_URL if domain == 'laptop' else _VAL_RESTAURANT_URL
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class ABSA(datasets.GeneratorBasedBuilder):
"""SemEval 2015: Aspect-based Sentiment Analysis."""
_VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
ABSAConfig(
domain='laptop',
version=_VERSION
),
ABSAConfig(
domain='restaurant',
version=_VERSION
)
]
def _info(self):
if self.config.domain == 'restaurant':
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"aspects": datasets.Sequence({
'term': datasets.Value("string"),
'polarity': datasets.Value("string"),
'from': datasets.Value("int16"),
'to': datasets.Value("int16"),
}),
"category": datasets.Sequence({
'category': datasets.Value("string"),
'polarity': datasets.Value("string")
})
}
)
else:
features = datasets.Features(
{
"id": datasets.Value("string"),
"text": datasets.Value("string"),
"aspects": datasets.Sequence({
'term': datasets.Value("string"),
'polarity': datasets.Value("string"),
'from': datasets.Value("int16"),
'to': datasets.Value("int16"),
})
}
)
# features = datasets.Features(
# {
# "id": datasets.Value("int16"),
# "text": datasets.Value("string"),
# "aspects": datasets.Sequence([{
# 'term': datasets.Value("string"),
# 'polarity': datasets.Value("string"),
# 'from': datasets.Value("int8"),
# 'to': datasets.Value("int8"),
# }]),
# "category": datasets.Sequence([{
# 'category': datasets.Value("string"),
# 'polarity': datasets.Value("string")
# }])
# }
# )
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download(self.config.url_train)
val_path = dl_manager.download(self.config.url_val)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path})
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath):
"""Generate examples."""
with open(filepath, 'r') as f:
contents = json.load(f)
for id_, row in enumerate(contents):
yield id_, row |