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dataset_infos.json

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+{"default": {"description": "This dataset is built as a playground for beginner to make a use case for creating sentiment analysis model.\n", "citation": "", "homepage": "https://github.com/jakartaresearch", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"dtype": "string", "id": null, "_type": "Value"}, "stars": {"dtype": "int8", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "google_play_review", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 480786, "num_examples": 7028, "dataset_name": "google_play_review"}, "validation": {"name": "validation", "num_bytes": 216876, "num_examples": 3012, "dataset_name": "google_play_review"}}, "download_checksums": {"https://huggingface.co/datasets/jakartaresearch/google-play-review/raw/main/train.csv": {"num_bytes": 448923, "checksum": "9482d867937f49aed59d5b7b8a495e5b46c11d763fa977580ba4421742b0aed7"}, "https://huggingface.co/datasets/jakartaresearch/google-play-review/raw/main/validation.csv": {"num_bytes": 203356, "checksum": "06a84a0faf920a850b5a7e0341f9b697ba6164761e00c71338a92120fd7c96dc"}}, "download_size": 652279, "post_processing_size": null, "dataset_size": 697662, "size_in_bytes": 1349941}}

google-play-review.py

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+# 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""Google Play Review: An Indonesian App Sentiment Analysis."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+_DESCRIPTION = """\
+This dataset is built as a playground for beginner to make a use case for creating sentiment analysis model.
+"""
+
+_HOMEPAGE = "https://github.com/jakartaresearch"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_TRAIN_URL = "https://huggingface.co/datasets/jakartaresearch/google-play-review/raw/main/train.csv"
+_VAL_URL = "https://huggingface.co/datasets/jakartaresearch/google-play-review/raw/main/validation.csv"
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class GooglePlayReview(datasets.GeneratorBasedBuilder):
+    """GooglePlayReview: An Indonesian Sentiment Analysis Dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        
+        features = datasets.Features(
+            {
+                "text": datasets.Value("string"),
+                "label": datasets.Value("string"),
+                "stars": datasets.Value("int8")
+            }
+        )
+        
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE
+        )
+
+    def _split_generators(self, dl_manager):
+
+        train_path = dl_manager.download_and_extract(_TRAIN_URL)
+        val_path = dl_manager.download_and_extract(_VAL_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path})
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath):
+        """Generate examples."""
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(csv_file, delimiter=",")
+            next(csv_reader)
+            for id_, row in enumerate(csv_reader):
+                text, label, stars = row
+                yield id_, {"text": text, "label": label, "stars": stars}