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thaigov.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 dataset consists of individual news articles, each corresponding to a unique URL at the
+Thai government website (https://www.thaigov.go.th/). The dataset structure is as follows: a topic header is
+followed by the content of the news article, which is then succeeded by a blank line and the source URL
+"""
+import glob
+import os
+import re
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import jsonlines
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@article{,
+  author    = {PyThaiNLP},
+  title     = {thaigov-v2-corpus},
+  journal   = {},
+  volume    = {},
+  year      = {2023},
+  url       = {https://github.com/PyThaiNLP/thaigov-v2-corpus/tree/master},
+  doi       = {},
+  biburl    = {},
+  bibsource = {}
+}
+"""
+
+_DATASETNAME = "thaigov"
+
+_DESCRIPTION = """\
+This dataset is a corpus from ThaiGov.
+"""
+
+_HOMEPAGE = "https://github.com/PyThaiNLP/thaigov-v2-corpus/tree/master/data"
+
+_LANGUAGES = ["tha"]
+
+_LICENSE = Licenses.PDDL.value
+
+_LOCAL = False
+
+
+_URLS = {
+    _DATASETNAME: "https://github.com/PyThaiNLP/thaigov-v2-corpus/archive/refs/heads/master.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.SUMMARIZATION]
+
+_SOURCE_VERSION = "2.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """This dataset is a corpus from ThaiGov, can be used for summarization tasks."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="thaigov_source",
+            version=SOURCE_VERSION,
+            description="thaigov source schema",
+            schema="source",
+            subset_id="thaigov",
+        ),
+        SEACrowdConfig(
+            name="thaigov_seacrowd_t2t",
+            version=SEACROWD_VERSION,
+            description="thaigov SEACrowd schema",
+            schema="seacrowd_t2t",
+            subset_id="thaigov",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "thaigov_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "src": datasets.Value("string"),
+                    "tgt": datasets.Value("string"),
+                    "url": 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."""
+
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+        # Since the data is stored based on date extracted, it will follow the pattern data/year/month/day/{article_names}.txt
+        list_all_txt_files = list(glob.glob(os.path.join(data_dir, "thaigov-v2-corpus-master", "data", "*", "*", "*", "*.txt")))
+        all_data = []
+        counter = 0
+        for i in list_all_txt_files:
+            d = self._read_file(i)
+            all_data.append({"id": counter, "src": d["context"], "tgt": d["title"], "url": d["url"]})
+            counter += 1
+
+        self._write_jsonl(data_dir + "/train.jsonl", all_data)
+
+        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, "train.jsonl"),
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+            i = 0
+            with jsonlines.open(filepath) as f:
+                for each_data in f.iter():
+                    ex = {
+                        "id": each_data["id"],
+                        "src": each_data["src"],
+                        "tgt": each_data["tgt"],
+                        "url": each_data["url"],
+                    }
+                    yield i, ex
+                    i += 1
+
+        elif self.config.schema == "seacrowd_t2t":
+            i = 0
+            with jsonlines.open(filepath) as f:
+                for each_data in f.iter():
+                    ex = {"id": each_data["id"], "text_1": each_data["src"], "text_2": each_data["tgt"], "text_1_name": "input_document", "text_2_name": "output_summary"}
+                    yield i, ex
+                    i += 1
+
+    def _read_file(self, path):
+        text = {"title": "", "context": "", "url": ""}
+        page_view_line = 0
+        with open(path, "r", encoding="utf-8-sig") as f:
+            for n, line in enumerate(f):
+                line = line.strip()
+                if n == 0:  # title line
+                    text["title"] = line.strip()
+                else:
+                    if line:
+                        if re.match(r"^[\d,]+$", line):
+                            page_view_line = n
+                            continue
+                        if line == "พิมพ์" or page_view_line and page_view_line < n:  # skip 'print'
+                            continue
+                        if re.match(r"^ที่มา : http", line):
+                            text["url"] = line.strip().split(" ")[-1]
+                        else:
+                            text["context"] += line.strip().replace("\xa0", "") + "\n"
+        return text
+
+    def _write_jsonl(self, filepath, values):
+        with jsonlines.open(filepath, "w") as writer:
+            for line in values:
+                writer.write(line)