Upload afrisenti.py

afrisenti.py

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+# 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}