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orchid_pos.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{sornlertlamvanich1999building,
+  title={Building a Thai part-of-speech tagged corpus (ORCHID)},
+  author={Sornlertlamvanich, Virach and Takahashi, Naoto and Isahara, Hitoshi},
+  journal={Journal of the Acoustical Society of Japan (E)},
+  volume={20},
+  number={3},
+  pages={189--198},
+  year={1999},
+  publisher={Acoustical Society of Japan}
+}
+"""
+
+_DATASETNAME = "orchid_pos"
+
+_DESCRIPTION = """\
+The ORCHID corpus is a Thai part-of-speech (POS) tagged dataset, resulting from a collaboration between\
+Japan's Communications Research Laboratory (CRL) and Thailand's National Electronics and Computer Technology\
+Center (NECTEC). It is structured at three levels: paragraph, sentence, and word. The dataset incorporates a\
+unique tagset designed for use in multi-lingual machine translation projects, and is tailored to address the\
+challenges of Thai text, which lacks explicit word and sentence boundaries, punctuation, and inflection.\
+This dataset includes text information along with numbering for retrieval, and employs a probabilistic trigram\
+model for word segmentation and POS tagging. The ORCHID corpus is specifically structured to reduce ambiguity in\
+POS assignments, making it a valuable resource for Thai language processing and computational linguistics research.
+"""
+
+_HOMEPAGE = "https://github.com/wannaphong/corpus_mirror/releases/tag/orchid-v1.0"
+
+_LANGUAGES = ["tha"]
+
+_LICENSE = Licenses.CC_BY_NC_SA_3_0.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: "https://github.com/wannaphong/corpus_mirror/releases/download/orchid-v1.0/orchid97.crp.utf",
+}
+
+_SUPPORTED_TASKS = [Tasks.POS_TAGGING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class OrchidPOSDataset(datasets.GeneratorBasedBuilder):
+
+    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_seq_label",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_seq_label",
+            subset_id=f"{_DATASETNAME}",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        label_names = [
+            "NPRP",
+            "NCNM",
+            "NONM",
+            "NLBL",
+            "NCMN",
+            "NTTL",
+            "PPRS",
+            "PDMN",
+            "PNTR",
+            "PREL",
+            "VACT",
+            "VSTA",
+            "VATT",
+            "XVBM",
+            "XVAM",
+            "XVMM",
+            "XVBB",
+            "XVAE",
+            "DDAN",
+            "DDAC",
+            "DDBQ",
+            "DDAQ",
+            "DIAC",
+            "DIBQ",
+            "DIAQ",
+            "DCNM",
+            "DONM",
+            "ADVN",
+            "ADVI",
+            "ADVP",
+            "ADVS",
+            "CNIT",
+            "CLTV",
+            "CMTR",
+            "CFQC",
+            "CVBL",
+            "JCRG",
+            "JCMP",
+            "JSBR",
+            "RPRE",
+            "INT",
+            "FIXN",
+            "FIXV",
+            "EAFF",
+            "EITT",
+            "NEG",
+            "PUNC",
+            "CMTR@PUNC",
+        ]
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "ttitle": datasets.Value("string"),
+                    "etitle": datasets.Value("string"),
+                    "tauthor": datasets.Value("string"),
+                    "eauthor": datasets.Value("string"),
+                    "tinbook": datasets.Value("string"),
+                    "einbook": datasets.Value("string"),
+                    "tpublisher": datasets.Value("string"),
+                    "epublisher": datasets.Value("string"),
+                    "year": datasets.Value("string"),
+                    "file": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "labels": datasets.Sequence(datasets.ClassLabel(names=label_names)),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_seq_label":
+            features = schemas.seq_label_features(label_names)
+
+        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)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, ""),
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _get_tokens_labels(self, paragraphs):
+        tokens = []
+        labels = []
+        token_mapping = {
+            "<space>": " ",
+            "<exclamation>": "!",
+            "<quotation>": '"',
+            "<number>": "#",
+            "<dollar>": "$",
+            "<percent>": "%",
+            "<ampersand>": "&",
+            "<apostrophe>": "'",
+            "<slash>": "/",
+            "<colon>": ":",
+            "<semi_colon>": ";",
+            "<less_than>": "<",
+            "<equal>": "=",
+            "<greater than>": ">",
+            "<question_mark>": "?",
+            "<at_mark>": "@",
+            "<left_parenthesis>": "(",
+            "<left_square_bracket>": "[",
+            "<right_parenthesis>": ")",
+            "<right_square_bracket>": "]",
+            "<asterisk>": "*",
+            "<circumflex_accent>": "^",
+            "<plus>": "+",
+            "<low_line>": "_",
+            "<comma>": ",",
+            "left_curly_bracket": "{",
+            "<minus>": "-",
+            "<right_curly_bracket>": "}",
+            "<full_stop>": ".",
+            "<tilde>": "~",
+        }
+        for paragraph in paragraphs:
+            sentences = re.split(r"#\d+\n", paragraph)
+            for sentence in sentences[1:]:
+                token_pos_pairs = sentence.split("//")[1]
+                for token_pos_pair in token_pos_pairs.split("\n")[1:-1]:
+                    if "/" in token_pos_pair:
+                        token = token_pos_pair.split("/")[0]
+                        tokens.append(token_mapping[token] if token in token_mapping.keys() else token)
+                        labels.append(token_pos_pair.split("/")[1])
+                    else:
+                        token = token_pos_pair.split("@")[0]
+                        tokens.append(token_mapping[token] if token in token_mapping.keys() else token)
+                        labels.append(token_pos_pair.split("@")[1])
+        return tokens, labels
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        file_content = open(filepath, "r").read()
+        texts = file_content.split("%TTitle:")
+
+        idx = 0
+        for text in texts[1:]:
+            file_part = text.split("%File")[-1]
+            tokens, labels = self._get_tokens_labels(re.split(r"#P\d+\n", file_part)[1:])
+            if self.config.schema == "source":
+                parts = text.split("%")
+                example = {
+                    "ttitle": parts[0],
+                    "etitle": ":".join(parts[1].split(":")[1:]).strip(),
+                    "tauthor": ":".join(parts[2].split(":")[1:]).strip(),
+                    "eauthor": ":".join(parts[3].split(":")[1:]).strip(),
+                    "tinbook": ":".join(parts[4].split(":")[1:]).strip(),
+                    "einbook": ":".join(parts[5].split(":")[1:]).strip(),
+                    "tpublisher": ":".join(parts[6].split(":")[1:]).strip(),
+                    "epublisher": ":".join(parts[7].split(":")[1:]).strip(),
+                    "year": ":".join(parts[9].split(":")[1:]).strip(),
+                    "file": file_part.strip(),
+                    "tokens": tokens,
+                    "labels": labels,
+                }
+            elif self.config.schema == "seacrowd_seq_label":
+                example = {
+                    "id": idx,
+                    "tokens": tokens,
+                    "labels": labels,
+                }
+            yield idx, example
+            idx += 1