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# 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
"""MasakhaNEWS: News Topic Classification for African languages"""
import datasets
import pandas
import pandas as pd
logger = datasets.logging.get_logger(__name__)
_CITATION = """
@inproceedings{lawallanre-2023-geoNLPSent,
author = "Olanrewaju",
month = "Nov",
year = "2023",
address = "Lagos, Nigeria",
}
"""
_DESCRIPTION = """\
geoNLPSent is dataset of transport tweets extrcted from twitter
The language is:
- English (eng)
"""
_URL = "https://github.com/lawallanre00490038/GeoNLP/raw/main/data/"
_TRAINING_FILE = "train.tsv"
_DEV_FILE = "dev.tsv"
_TEST_FILE = "test.tsv"
class GeoNLPSentiConfig(datasets.BuilderConfig):
"""BuilderConfig for GeoNLPsenti"""
def __init__(self, **kwargs):
"""BuilderConfig for GeoNLPsenti.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(GeoNLPSentiConfig, self).__init__(**kwargs)
class GeoNLPSenti(datasets.GeneratorBasedBuilder):
"""GeoNLPsenti dataset."""
BUILDER_CONFIGS = [
GeoNLPSentiConfig(name="en", version=datasets.Version("1.0.0"), description="Nollysenti English dataset")
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"label": datasets.features.ClassLabel(
names=["Positive", "Negative", "Neutral"]
),
"review": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://github.com/lawallanre00490038/GeoNLP",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": f"{_URL}{self.config.name}/{_TRAINING_FILE}",
"dev": f"{_URL}{self.config.name}/{_DEV_FILE}",
"test": f"{_URL}{self.config.name}/{_TEST_FILE}",
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
df = pd.read_csv(filepath, sep='\t')
df = df.dropna()
N = df.shape[0]
for id_ in range(N):
yield id_, {
"label": df['sentiment'].iloc[id_],
"review": df['tweet'].iloc[id_],
}
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