Upload thai_tnhc2_books.py with huggingface_hub

thai_tnhc2_books.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.
+
+"""
+This dataset collects all 353 books from the Thai National Historical Corpus 2 (TNHC2) corpus. The dataset has been cleaned to use text for pretraining models and NLP tasks. The TNHC2 corpus is a Thai old books corpus and all books are copyright expired according to Thai law (50 years after the author's death). More information on this corpus can be found here: https://www.arts.chula.ac.th/chulaseal/tnhc2/.
+"""
+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 Tasks, Licenses
+
+_CITATION = """\
+@dataset{phatthiyaphaibun_2024_10783421,
+  author       = {Phatthiyaphaibun, Wannaphong},
+  title        = {Thai TNHC2 Books},
+  month        = mar,
+  year         = 2024,
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.10783421},
+  url          = {https://doi.org/10.5281/zenodo.10783421}
+}
+"""
+
+_DATASETNAME = "thai_tnhc2_books"
+
+_DESCRIPTION = """\
+This dataset collects all 353 books from the Thai National Historical Corpus 2 (TNHC2) corpus. The dataset has been cleaned to use text for pretraining models and NLP tasks. The TNHC2 corpus is a Thai old books corpus and all books are copyright expired according to Thai law (50 years after the author's death). More information on this corpus can be found here: https://www.arts.chula.ac.th/chulaseal/tnhc2/.
+"""
+
+_HOMEPAGE = "https://www.arts.chula.ac.th/chulaseal/tnhc2/"
+
+_LANGUAGES = ["tha"]
+
+_LICENSE = Licenses.CC0_1_0.value
+
+_LOCAL = False
+
+_URLS = "https://huggingface.co/datasets/pythainlp/thai-tnhc2-books/resolve/main/data/train-00000-of-00001.parquet?download=true"
+
+_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+class ThaiTnhc2BooksDataset(datasets.GeneratorBasedBuilder):
+    """This dataset collects all 353 books from the Thai National Historical Corpus 2 (TNHC2) corpus. The dataset has been cleaned to use text for pretraining models and NLP tasks. The TNHC2 corpus is a Thai old books corpus and all books are copyright expired according to Thai law (50 years after the author's death). More information on this corpus can be found here: https://www.arts.chula.ac.th/chulaseal/tnhc2/."""
+
+    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_ssp",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_ssp",
+            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("string"),
+                "book": datasets.Value("string"),
+                "author": datasets.Value("string"),
+                "text": datasets.Value("string"),
+            })
+
+        elif self.config.schema == "seacrowd_ssp":
+            features = schemas.ssp_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,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        df = pd.read_parquet(filepath)
+
+        # Handle multiple books with the same id
+        df["id"] = df["id"] + "_" + df.groupby("id").cumcount().astype(str)
+
+        if self.config.schema == "source":
+            for i, row in df.iterrows():
+                yield i, {
+                    "id": row["id"],
+                    "book": row["book"],
+                    "author": row["author"],
+                    "text": row["text"],
+                }
+
+        elif self.config.schema == "seacrowd_ssp":
+            for i, row in df.iterrows():
+                yield i, {
+                    "id": row["id"],
+                    "text": row["text"],
+                }