Upload wisesight_thai_sentiment.py with huggingface_hub

wisesight_thai_sentiment.py

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+# coding=utf-8
+# Copyright 2022 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 json
+import os
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME,
+                                      DEFAULT_SOURCE_VIEW_NAME, Licenses,
+                                      Tasks)
+
+_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}
+}
+"""
+
+
+_DATASETNAME = "wisesight_thai_sentiment"
+
+
+_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
+"""
+
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
+
+_HOMEPAGE = "https://github.com/PyThaiNLP/wisesight-sentiment"
+
+_LANGUAGES = ["tha"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+
+_LICENSE = Licenses.CC0_1_0.value
+
+
+_LOCAL = False
+
+
+_URLS = {
+    _DATASETNAME: "https://github.com/PyThaiNLP/wisesight-sentiment/raw/master/huggingface/data.zip",
+}
+
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]  # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class WisesightSentimentDataset(datasets.GeneratorBasedBuilder):
+    """Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)"""
+
+    _DOWNLOAD_URL = _URLS[_DATASETNAME]
+    _TRAIN_FILE = "train.jsonl"
+    _VAL_FILE = "valid.jsonl"
+    _TEST_FILE = "test.jsonl"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="wisesight_thai_sentiment_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="Wisesight Sentiment Corpus Source version (positive, neutral, negative, question)",
+            schema="source",
+            subset_id="wisesight_thai_sentiment",
+        ),
+        SEACrowdConfig(
+            name="wisesight_thai_sentiment_seacrowd_text",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description="Wisesight Sentiment Corpus Seacrowd version (positive, neutral, negative, question)",
+            schema="seacrowd_text",
+            subset_id="wisesight_thai_sentiment",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "wisesight_thai_sentiment_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "texts": datasets.Value("string"),
+                    "category": datasets.features.ClassLabel(names=["pos", "neu", "neg", "q"]),
+                }
+            )
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(["pos", "neu", "neg", "q"])
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    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:
+            if self.config.schema == "source":
+                for id_, row in enumerate(f):
+                    data = json.loads(row)
+                    texts = data["texts"]
+                    category = data["category"]
+                    yield id_, {"texts": texts, "category": category}
+
+            elif self.config.schema == "seacrowd_text":
+                for id_, row in enumerate(f):
+                    data = json.loads(row)
+                    texts = data["texts"]
+                    category = data["category"]
+                    ex = {"id": str(id_), "text": texts, "label": category}
+                    yield id_, ex