Datasets:
File size: 4,613 Bytes
17e9c1a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
# 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.
import csv
import textwrap
import pandas as pd
import datasets
LANGUAGES = ['amh', 'hau', 'ibo', 'arq', 'ary', 'yor', 'por', 'twi', 'tso', 'tir', 'orm', 'pcm', 'kin', 'swa']
class AfriSentiConfig(datasets.BuilderConfig):
"""BuilderConfig for AfriSenti"""
def __init__(
self,
text_features,
label_column,
label_classes,
train_url,
valid_url,
test_url,
citation,
**kwargs,
):
"""BuilderConfig for AfriSenti.
Args:
text_features: `dict[string]`, map from the name of the feature
dict for each text field to the name of the column in the txt/csv/tsv file
label_column: `string`, name of the column in the txt/csv/tsv file corresponding
to the label
label_classes: `list[string]`, the list of classes if the label is categorical
train_url: `string`, url to train file from
valid_url: `string`, url to valid file from
test_url: `string`, url to test file from
citation: `string`, citation for the data set
**kwargs: keyword arguments forwarded to super.
"""
super(AfriSentiConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.text_features = text_features
self.label_column = label_column
self.label_classes = label_classes
self.train_url = train_url
self.valid_url = valid_url
self.test_url = test_url
self.citation = citation
class AfriSenti(datasets.GeneratorBasedBuilder):
"""AfriSenti benchmark"""
BUILDER_CONFIGS = []
for lang in LANGUAGES:
BUILDER_CONFIGS.append(
AfriSentiConfig(
name=lang,
description=textwrap.dedent(
f"""\
{lang} dataset."""
),
text_features={"tweet": "tweet"},
label_classes=["positive", "neutral", "negative"],
label_column="label",
train_url=f"https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/data/{lang}/train.tsv",
valid_url=f"https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/data/{lang}/dev.tsv",
test_url=f"https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/data/{lang}/test.tsv",
citation=textwrap.dedent(
f"""\
{lang} citation"""
),
),
)
def _info(self):
features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features}
features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(features),
citation=self.config.citation,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
train_path = dl_manager.download_and_extract(self.config.train_url)
valid_path = dl_manager.download_and_extract(self.config.valid_url)
test_path = dl_manager.download_and_extract(self.config.test_url)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
df = pd.read_csv(filepath, sep='\t')
print('-'*100)
print(df.head())
print('-'*100)
for id_, row in df.iterrows():
tweet = row["tweet"]
label = row["label"]
yield id_, {"tweet": tweet, "label": label}
|