Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-analysis
Languages:
Yoruba
Size:
1K<n<10K
ArXiv:
License:
Commit
•
ab6beed
1
Parent(s):
81df0aa
Add a script to load the data (#1)
Browse files- Add a script to load the data (4303f6fabcdec7d7e2bd4f707d43617f3ebadef8)
Co-authored-by: Odunayo Ogundepo <[email protected]>
YOSM.py
ADDED
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# coding=utf-8
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# Copyright 2020 HuggingFace Datasets Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""YOSM: A NEW YORUBA SENTIMENT CORPUS FOR MOVIE REVIEWS"""
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import datasets
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import json
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import pandas as pd
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logger = datasets.logging.get_logger(__name__)
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_CITATION = """\
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@inproceedings{
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shode2022yosm,
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title={{YOSM}: A {NEW} {YORUBA} {SENTIMENT} {CORPUS} {FOR} {MOVIE} {REVIEWS}},
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author={Iyanuoluwa Shode and David Ifeoluwa Adelani and Anna Feldman},
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booktitle={3rd Workshop on African Natural Language Processing},
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year={2022},
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url={https://openreview.net/forum?id=rRzx5qzVIb9}
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}
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"""
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_DESCRIPTION = """\
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YOSM: A NEW YORUBA SENTIMENT CORPUS FOR MOVIE REVIEWS
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- Yoruba
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"""
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languages=[
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'yoruba'
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]
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_URL = "https://github.com/IyanuSh/YOSM"
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_TRAINING_FILE = "train.tsv"
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_DEV_FILE = "dev.tsv"
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_TEST_FILE = "test.tsv"
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class Yosm(datasets.GeneratorBasedBuilder):
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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version=datasets.Version('1.0.0'),
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name=lang,
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description='YOSM: A NEW YORUBA SENTIMENT CORPUS FOR MOVIE REVIEWS'
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) for lang in languages
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]
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def _info(self):
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features = datasets.features({
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'yo_review': datasets.Value('string'),
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'sentiment': datasets.Value('string')
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})
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features, # Here we define them above because they are different between the two configurations
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supervised_keys=None,
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# Homepage of the dataset for documentation
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homepage=_URL,
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# License for the dataset if available
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license='',
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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lang = self.config.name
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splits = [
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datasets.SplitGenerator(
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name='train',
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gen_kwargs={
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'filepath': _TRAINING_FILE,
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},
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),
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datasets.SplitGenerator(
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name='dev',
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gen_kwargs={
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'filepath': _DEV_FILE,
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},
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),
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datasets.SplitGenerator(
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name='test',
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gen_kwargs={
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'filepath': _TEST_FILE,
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},
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)
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]
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return splits
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def _generate_examples(self, filepath):
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yosm_df = pd.read_csv(filepath, sep="\t", lineterminator='\n')
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for index, row in yosm_df.iterrows():
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yield row["yo_review"], row["sentiment"]
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