Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-analysis
Languages:
Yoruba
Size:
1K<n<10K
ArXiv:
License:
File size: 3,396 Bytes
ab6beed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
# 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
"""YOSM: A NEW YORUBA SENTIMENT CORPUS FOR MOVIE REVIEWS"""
import datasets
import json
import pandas as pd
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{
shode2022yosm,
title={{YOSM}: A {NEW} {YORUBA} {SENTIMENT} {CORPUS} {FOR} {MOVIE} {REVIEWS}},
author={Iyanuoluwa Shode and David Ifeoluwa Adelani and Anna Feldman},
booktitle={3rd Workshop on African Natural Language Processing},
year={2022},
url={https://openreview.net/forum?id=rRzx5qzVIb9}
}
"""
_DESCRIPTION = """\
YOSM: A NEW YORUBA SENTIMENT CORPUS FOR MOVIE REVIEWS
- Yoruba
"""
languages=[
'yoruba'
]
_URL = "https://github.com/IyanuSh/YOSM"
_TRAINING_FILE = "train.tsv"
_DEV_FILE = "dev.tsv"
_TEST_FILE = "test.tsv"
class Yosm(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
version=datasets.Version('1.0.0'),
name=lang,
description='YOSM: A NEW YORUBA SENTIMENT CORPUS FOR MOVIE REVIEWS'
) for lang in languages
]
def _info(self):
features = datasets.features({
'yo_review': datasets.Value('string'),
'sentiment': datasets.Value('string')
})
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_URL,
# License for the dataset if available
license='',
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
lang = self.config.name
splits = [
datasets.SplitGenerator(
name='train',
gen_kwargs={
'filepath': _TRAINING_FILE,
},
),
datasets.SplitGenerator(
name='dev',
gen_kwargs={
'filepath': _DEV_FILE,
},
),
datasets.SplitGenerator(
name='test',
gen_kwargs={
'filepath': _TEST_FILE,
},
)
]
return splits
def _generate_examples(self, filepath):
yosm_df = pd.read_csv(filepath, sep="\t", lineterminator='\n')
for index, row in yosm_df.iterrows():
yield row["yo_review"], row["sentiment"]
|