Create test

test

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+"""Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)"""
+
+
+import json
+import os
+
+import datasets
+from datasets.tasks import TextClassification
+
+
+_CITATION = """\
+@software{bact_2019_3457447,
+  author       = {Suriyawongkul, Arthit and
+                  Chuangsuwanich, Ekapol and
+                  Chormai, Pattarawat and
+                  Polpanumas, Charin},
+  title        = {PyThaiNLP/wisesight-sentiment: First release},
+  month        = sep,
+  year         = 2019,
+  publisher    = {Zenodo},
+  version      = {v1.0},
+  doi          = {10.5281/zenodo.3457447},
+  url          = {https://doi.org/10.5281/zenodo.3457447}
+}
+"""
+
+_DESCRIPTION = """\
+Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)
+* Released to public domain under Creative Commons Zero v1.0 Universal license.
+* Category (Labels): {"pos": 0, "neu": 1, "neg": 2, "q": 3}
+* Size: 26,737 messages
+* Language: Central Thai
+* Style: Informal and conversational. With some news headlines and advertisement.
+* Time period: Around 2016 to early 2019. With small amount from other period.
+* Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs.
+* Privacy:
+    * Only messages that made available to the public on the internet (websites, blogs, social network sites).
+    * For Facebook, this means the public comments (everyone can see) that made on a public page.
+    * Private/protected messages and messages in groups, chat, and inbox are not included.
+* Alternations and modifications:
+    * Keep in mind that this corpus does not statistically represent anything in the language register.
+    * Large amount of messages are not in their original form. Personal data are removed or masked.
+    * Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact.
+    (Mis)spellings are kept intact.
+    * Messages longer than 2,000 characters are removed.
+    * Long non-Thai messages are removed. Duplicated message (exact match) are removed.
+* More characteristics of the data can be explore: https://github.com/PyThaiNLP/wisesight-sentiment/blob/master/exploration.ipynb
+"""
+
+
+class WisesightSentimentConfig(datasets.BuilderConfig):
+    """BuilderConfig for WisesightSentiment."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for WisesightSentiment.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(WisesightSentimentConfig, self).__init__(**kwargs)
+
+
+class WisesightSentiment(datasets.GeneratorBasedBuilder):
+    """Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)"""
+
+    _DOWNLOAD_URL = "https://github.com/PyThaiNLP/wisesight-sentiment/raw/master/huggingface/data.zip"
+    _TRAIN_FILE = "train.jsonl"
+    _VAL_FILE = "valid.jsonl"
+    _TEST_FILE = "test.jsonl"
+
+    BUILDER_CONFIGS = [
+        WisesightSentimentConfig(
+            name="wisesight_sentiment",
+            version=datasets.Version("1.0.0"),
+            description="Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "texts": datasets.Value("string"),
+                    "category": datasets.features.ClassLabel(names=["pos", "neu", "neg", "q"]),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://github.com/PyThaiNLP/wisesight-sentiment",
+            citation=_CITATION,
+            task_templates=[TextClassification(text_column="texts", label_column="category")],
+        )
+
+    def _split_generators(self, dl_manager):
+        arch_path = dl_manager.download_and_extract(self._DOWNLOAD_URL)
+        data_dir = os.path.join(arch_path, "data")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": os.path.join(data_dir, self._TRAIN_FILE)},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": os.path.join(data_dir, self._VAL_FILE)},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": os.path.join(data_dir, self._TEST_FILE)},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate WisesightSentiment examples."""
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                data = json.loads(row)
+                texts = data["texts"]
+                category = data["category"]
+                yield id_, {"texts": texts, "category": category}