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Model: flax-community/gpt2-medium-indonesian Source: Original Platform
2026-06-07 05:43:18 +08:00
commit e9d472cebf
24 changed files with 51340 additions and 0 deletions
--- a/text_collection/text_collection.py
+++ b/text_collection/text_collection.py
@@ -0,0 +1,125 @@
+# coding=utf-8
+# Copyright 2020 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.
+"""Large-scale Indonesian Summarization Dataset"""
+
+
+import glob
+import json
+import os
+import re
+from pathlib import Path
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+
+"""
+
+_DESCRIPTION = """\
+This module load text dataset from local directory. The text dataset should have the format like Oscar dataset
+where each new entry is separated by empty lines.
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = ""
+
+
+class TextCollectionConfig(datasets.BuilderConfig):
+    """BuilderConfig for TextCollection"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for TextCollection.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(TextCollectionConfig, self).__init__(**kwargs)
+
+
+class TextCollection(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        TextCollectionConfig(
+            name="text_collection",
+            version=VERSION,
+            description="Id Collection dataset",
+        ),
+    ]
+
+    @property
+    def manual_download_instructions(self):
+        return """\
+            You need to manually collect text datasets in a directory.  The text dataset can then be loaded 
+            using the following command:
+            `datasets.load_dataset("text_collection", data_dir="<path/to/dataset>")`.
+            """
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({"id": datasets.Value("int64"), "text": datasets.Value("string")}),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))
+        print("# Data directory", data_dir)
+        if not os.path.exists(data_dir):
+            raise FileNotFoundError(
+                "{} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('id_liputan6', "
+                "'canonical', data_dir=...)`. Manual download instructions:\n{}".format(
+                    data_dir, self.manual_download_instructions
+                )
+            )
+        split_generators = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "article_dir": os.path.join(data_dir, ""),
+                    "split": "train",
+                },
+            )
+        ]
+        return split_generators
+
+    def _generate_examples(self, article_dir, split):
+        logger.info("⏳ Generating %s examples from = %s", split, article_dir)
+        id_ = 0
+        current_lines = []
+        for path in sorted(glob.glob(os.path.join(article_dir, "**/*.txt"), recursive=True)):
+            with open(path, "r") as f:
+                print("# Reading", path)
+                for line in f:
+                    if len(line.strip()) > -1:
+                        current_lines.append(line)
+                    elif current_lines:
+                        feature = id_, {"id": id_, "text": "".join(current_lines).rstrip()}
+                        yield feature
+                        id_ += 1
+                        current_lines = []
+                # last paragraph
+                if current_lines:
+                    feature = id_, {"id": id_, "text": "".join(current_lines).rstrip()}
+                    yield feature
+                    id_ += 1
+                    current_lines = []