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vilexnorm.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.
+
+"""
+The ViLexNorm corpus is a collection of comment pairs in Vietnamese, designed for the task of lexical normalization. The corpus contains 10,467 comment pairs, carefully curated and annotated for lexical normalization purposes.
+These comment pairs are partitioned into three subsets: training, development, and test, distributed in an 8:1:1 ratio.
+"""
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{nguyen-etal-2024-vilexnorm,
+    title = "{V}i{L}ex{N}orm: A Lexical Normalization Corpus for {V}ietnamese Social Media Text",
+    author = "Nguyen, Thanh-Nhi  and
+      Le, Thanh-Phong  and
+      Nguyen, Kiet",
+    editor = "Graham, Yvette  and
+      Purver, Matthew",
+    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = mar,
+    year = "2024",
+    address = "St. Julian{'}s, Malta",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.eacl-long.85",
+    pages = "1421--1437",
+    abstract = "Lexical normalization, a fundamental task in Natural Language Processing (NLP), involves the transformation of words into their canonical forms. This process has been proven to benefit various downstream NLP tasks greatly.
+    In this work, we introduce Vietnamese Lexical Normalization (ViLexNorm), the first-ever corpus developed for the Vietnamese lexical normalization task. The corpus comprises over 10,000 pairs of sentences meticulously annotated
+    by human annotators, sourced from public comments on Vietnam{'}s most popular social media platforms. Various methods were used to evaluate our corpus, and the best-performing system achieved a result of 57.74% using
+    the Error Reduction Rate (ERR) metric (van der Goot, 2019a) with the Leave-As-Is (LAI) baseline. For extrinsic evaluation, employing the model trained on ViLexNorm demonstrates the positive impact of the Vietnamese lexical normalization task
+    on other NLP tasks. Our corpus is publicly available exclusively for research purposes.",
+}
+"""
+
+_DATASETNAME = "vilexnorm"
+
+_DESCRIPTION = """\
+The ViLexNorm corpus is a collection of comment pairs in Vietnamese, designed for the task of lexical normalization. The corpus contains 10,467 comment pairs, carefully curated and annotated for lexical normalization purposes.
+These comment pairs are partitioned into three subsets: training, development, and test, distributed in an 8:1:1 ratio.
+"""
+
+_HOMEPAGE = "https://github.com/ngxtnhi/ViLexNorm"
+
+_LANGUAGES = ["vie"]
+
+_LICENSE = Licenses.CC_BY_NC_SA_4_0.value
+
+_LOCAL = False
+
+_URLS = {
+    "train": "https://raw.githubusercontent.com/ngxtnhi/ViLexNorm/main/data/train.csv",
+    "dev": "https://raw.githubusercontent.com/ngxtnhi/ViLexNorm/main/data/dev.csv",
+    "test": "https://raw.githubusercontent.com/ngxtnhi/ViLexNorm/main/data/test.csv",
+}
+
+_SUPPORTED_TASKS = [Tasks.MULTILEXNORM]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class VilexnormDataset(datasets.GeneratorBasedBuilder):
+    """The ViLexNorm corpus is a collection of comment pairs in Vietnamese, designed for the task of lexical normalization. The corpus contains 10,467 comment pairs, carefully curated and annotated for lexical normalization purposes.
+    These comment pairs are partitioned into three subsets: training, development, and test, distributed in an 8:1:1 ratio."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    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_t2t",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_t2t",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "original": datasets.Value("string"),
+                    "normalized": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_t2t":
+            features = schemas.text2text_features
+
+        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."""
+
+        data_dir = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir["test"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir["dev"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        df = pd.read_csv(filepath)
+
+        if self.config.schema == "source":
+            for i, row in df.iterrows():
+                yield i, {
+                    "id": i,
+                    "original": row["original"],
+                    "normalized": row["normalized"],
+                }
+
+        elif self.config.schema == "seacrowd_t2t":
+            for i, row in df.iterrows():
+                yield i, {
+                    "id": str(i),
+                    "text_1": row["original"],
+                    "text_2": row["normalized"],
+                    "text_1_name": "original",
+                    "text_2_name": "normalized",
+                }