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vlsp2016_sa.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 os
+import re
+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 = """\
+@article{nguyen2018vlsp,
+  title={VLSP shared task: sentiment analysis},
+  author={Nguyen, Huyen TM and Nguyen, Hung V and Ngo, \
+Quyen T and Vu, Luong X and Tran, Vu Mai and Ngo, Bach X and Le, Cuong A},
+  journal={Journal of Computer Science and Cybernetics},
+  volume={34},
+  number={4},
+  pages={295--310},
+  year={2018}
+}
+"""
+_DATASETNAME = "vlsp2016_sa"
+
+_DESCRIPTION = """\
+The SA-VLSP2016 dataset were collected from three source sites which are tinhte.vn, \
+vnexpress.net and Facebook, and used for the sentiment analysis task. The data consists \
+of comments of technical articles on those sites. Each comment is given one of \
+four labels: POS (positive), NEG (negative), NEU (neutral) and USELESS (filter-out).
+"""
+
+_HOMEPAGE = "https://vlsp.org.vn/resources-vlsp2016"
+_LANGUAGES = ["vie"]
+
+_LICENSE = Licenses.CC_BY_NC_SA_4_0.value
+_LOCAL = True
+
+_URLS = {}
+
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+_TAGS = ["POS", "NEG", "NEU"]
+
+
+class VLSP2016SADataset(datasets.GeneratorBasedBuilder):
+    """The SA-VLSP2016 dataset, used for sentiment analysis, comprises comments from technical \
+    articles on tinhte.vn, vnexpress.net, and Facebook, each labeled as positive, negative, neutral, or filter-out."""
+
+    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}",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_tokenized_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}_tokenized",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=_TAGS),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.text_features(_TAGS)
+
+        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."""
+        if self.config.data_dir is None:
+            raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.")
+        else:
+            data_dir = self.config.data_dir
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "SA2016-training_data"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "SA2016-TestData-Ans"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        if split == "dev":
+            if self.config.schema in ["source", f"seacrowd_{self.SEACROWD_SCHEMA_NAME}"]:
+                labelfile = "test_raw_ANS.txt"
+            elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}_tokenized":
+                labelfile = "test_tokenized_ANS.txt"
+
+            with open(os.path.join(filepath, labelfile)) as file:
+                data = file.read()
+
+            pattern = re.compile("(?P<sentence>.+)\n(?P<label>(POS|NEG|NEU))\n")
+
+            if self.config.schema in ["source", f"seacrowd_{self.SEACROWD_SCHEMA_NAME}", f"seacrowd_{self.SEACROWD_SCHEMA_NAME}_tokenized"]:
+                for i, match in enumerate(pattern.finditer(data)):
+                    yield i, {"id": i, "text": match.group("sentence").replace("\xa0", " "), "label": match.group("label")}
+
+        else:
+            labeltext = {"POS": [], "NEG": [], "NEU": []}
+            if self.config.schema in ["source", f"seacrowd_{self.SEACROWD_SCHEMA_NAME}"]:
+                positive = "SA-training_positive.txt"
+                negative = "SA-training_negative.txt"
+                neutral = "SA-training_neutral.txt"
+            elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}_tokenized":
+                positive = "train_positive_tokenized.txt"
+                negative = "train_negative_tokenized.txt"
+                neutral = "train_neutral_tokenized.txt"
+
+            for labelsplit, labelfile in zip(labeltext.keys(), [positive, negative, neutral]):
+                with open(os.path.join(filepath, labelfile)) as file:
+                    data = file.read()
+                labeltext[labelsplit] = data.split("\n\n")[:-1]
+
+            if self.config.schema in ["source", f"seacrowd_{self.SEACROWD_SCHEMA_NAME}", f"seacrowd_{self.SEACROWD_SCHEMA_NAME}_tokenized"]:
+                idcounter = 0
+                for label, sentences in labeltext.items():
+                    for sentence in sentences:
+                        yield idcounter, {"id": idcounter, "text": sentence, "label": label}
+                        idcounter = idcounter + 1