Duplicate from tyqiangz/multilingual-sentiments

Co-authored-by: Tay Yong Qiang <tyqiangz@users.noreply.huggingface.co>

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README.md

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+---
+language:
+- de
+- en
+- es
+- fr
+- ja
+- zh
+- id
+- ar
+- hi
+- it
+- ms
+- pt
+license: apache-2.0
+multilinguality:
+- monolingual
+- multilingual
+size_categories:
+- 100K<n<1M
+- 1M<n<10M
+task_categories:
+- text-classification
+task_ids:
+- sentiment-analysis
+- sentiment-classification
+---
+
+# Multilingual Sentiments Dataset
+
+A collection of multilingual sentiments datasets grouped into 3 classes -- positive, neutral, negative.
+
+Most multilingual sentiment datasets are either 2-class positive or negative, 5-class ratings of products reviews (e.g. Amazon multilingual dataset) or multiple classes of emotions. However, to an average person, sometimes positive, negative and neutral classes suffice and are more straightforward to perceive and annotate. Also, a positive/negative classification is too naive, most of the text in the world is actually neutral in sentiment. Furthermore, most multilingual sentiment datasets don't include Asian languages (e.g. Malay, Indonesian) and are dominated by Western languages (e.g. English, German).
+
+Git repo: https://github.com/tyqiangz/multilingual-sentiment-datasets
+
+## Dataset Description
+
+- **Webpage:** https://github.com/tyqiangz/multilingual-sentiment-datasets

dataset_infos.json

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multilingual-sentiments.py

@@ -0,0 +1,165 @@
+# 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
+
+import pandas as pd
+
+LANGUAGES = ['malay', 'hindi', 'japanese', 'german',
+             'italian', 'english', 'portuguese', 'french',
+             'spanish', 'chinese', 'indonesian', 'arabic'
+             ]
+
+
+class MultilingualSentimentsConfig(datasets.BuilderConfig):
+    """BuilderConfig for Multilingual Sentiments"""
+
+    def __init__(
+        self,
+        text_features,
+        label_column,
+        label_classes,
+        train_url,
+        valid_url,
+        test_url,
+        citation,
+        **kwargs,
+    ):
+        """BuilderConfig for Multilingual Sentiments.
+
+        Args:
+          text_features: `dict[string, 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(MultilingualSentimentsConfig, 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 MultilingualSentiments(datasets.GeneratorBasedBuilder):
+    """Multilingual Sentiments benchmark"""
+
+    BUILDER_CONFIGS = []
+
+    BUILDER_CONFIGS.append(
+        MultilingualSentimentsConfig(
+            name="all",
+            description=textwrap.dedent(
+                f"""\
+                All datasets."""
+            ),
+            text_features={"text": "text", "source": "source", "language": "language"},
+            label_classes=["positive", "neutral", "negative"],
+            label_column="label",
+            train_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/all/train.csv",
+            valid_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/all/valid.csv",
+            test_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/all/test.csv",
+            citation=textwrap.dedent(
+                f"""\
+            All citation"""
+            ),
+        ),
+    )
+
+    for lang in LANGUAGES:
+        BUILDER_CONFIGS.append(
+            MultilingualSentimentsConfig(
+                name=lang,
+                description=textwrap.dedent(
+                    f"""\
+                    {lang} dataset."""
+                ),
+                text_features={"text": "text", "source": "source"},
+                label_classes=["positive", "neutral", "negative"],
+                label_column="label",
+                train_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/{lang}/train.csv",
+                valid_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/{lang}/valid.csv",
+                test_url=f"https://raw.githubusercontent.com/tyqiangz/multilingual-sentiment-datasets/main/data/{lang}/test.csv",
+                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)
+
+        print('-'*100)
+        print(df.head())
+        print('-'*100)
+
+        for id_, row in df.iterrows():
+            if self.config.name != "all":
+                text = row["text"]
+                label = row["label"]
+                source = row["source"]
+
+                yield id_, {"text": text, "label": label, "source": source}
+
+            else:
+                text = row["text"]
+                label = row["label"]
+                source = row["source"]
+                language = row["language"]
+
+                yield id_, {"text": text, "label": label, "source": source, "language": language}