Upload gklmip_sentiment.py with huggingface_hub

gklmip_sentiment.py

@@ -0,0 +1,147 @@
+# 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
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@InProceedings{,
+author="Jiang, Shengyi
+and Huang, Xiuwen
+and Cai, Xiaonan
+and Lin, Nankai",
+title="Pre-trained Models and Evaluation Data for the Myanmar Language",
+booktitle="The 28th International Conference on Neural Information Processing",
+year="2021",
+publisher="Springer International Publishing",
+address="Cham",
+}
+"""
+
+_DATASETNAME = "gklmip_sentiment"
+_DESCRIPTION = """\
+The GKLMIP Product Sentiment Dataset is a Burmese dataset for sentiment analysis. \
+It was created by crawling comments on an e-commerce website. The sentiment labels range \
+from 1 to 5, with 1 and 2 being negative, 3 and 4 being neutral, and 5 being positive.
+"""
+
+_HOMEPAGE = "https://github.com/GKLMIP/Pretrained-Models-For-Myanmar/tree/main"
+_LANGUAGES = ["mya"]
+_LICENSE = Licenses.UNKNOWN.value
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://github.com/GKLMIP/Pretrained-Models-For-Myanmar/raw/main/Product%20Sentiment%20Dataset.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+_LABELS = [1, 2, 3, 4, 5]
+
+
+class GklmipSentimentDataset(datasets.GeneratorBasedBuilder):
+    """The GKLMIP Product Sentiment Dataset is a Burmese dataset for sentiment analysis."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    SEACROWD_SCHEMA_NAME = "text"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features({"bpe": datasets.Value("string"), "text": datasets.Value("string"), "label": datasets.Value("string")})
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text_features(_LABELS)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "product_sentiment_dataset_train.json"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "product_sentiment_dataset_test.json"),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "product_sentiment_dataset_dev.json"),
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        with open(filepath) as file:
+            dataset = json.load(file)
+
+        if self.config.schema == "source":
+            for i, line in enumerate(dataset):
+                yield i, {"bpe": line["bpe"], "text": line["text"], "label": line["label"]}
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            for i, line in enumerate(dataset):
+                yield i, {"id": i, "text": line["text"], "label": line["label"]}