Convert dataset to Parquet

README.md

@@ -19,6 +19,7 @@ task_ids:
 - sentiment-classification
 pretty_name: WisesightSentiment
 dataset_info:
+  config_name: wisesight_sentiment
   features:
   - name: texts
     dtype: string
@@ -30,19 +31,28 @@ dataset_info:
           '1': neu
           '2': neg
           '3': q
-  config_name: wisesight_sentiment
   splits:
   - name: train
-    num_bytes: 5328819
+    num_bytes: 5328807
     num_examples: 21628
   - name: validation
-    num_bytes: 593570
+    num_bytes: 593566
     num_examples: 2404
   - name: test
-    num_bytes: 662137
+    num_bytes: 662133
     num_examples: 2671
-  download_size: 2102326
-  dataset_size: 6584526
+  download_size: 3195627
+  dataset_size: 6584506
+configs:
+- config_name: wisesight_sentiment
+  data_files:
+  - split: train
+    path: wisesight_sentiment/train-*
+  - split: validation
+    path: wisesight_sentiment/validation-*
+  - split: test
+    path: wisesight_sentiment/test-*
+  default: true
 train-eval-index:
 - config: wisesight_sentiment
   task: text-classification

wisesight_sentiment.py

@@ -1,119 +0,0 @@
-"""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}

wisesight_sentiment/test-00000-of-00001.parquet

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+size 326621

wisesight_sentiment/train-00000-of-00001.parquet

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+size 2582961

wisesight_sentiment/validation-00000-of-00001.parquet

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+size 286045