Upload vistec_tp_th_21.py with huggingface_hub

vistec_tp_th_21.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 = """\
+@inproceedings{limkonchotiwat-etal-2021-handling,
+    title = "Handling Cross- and Out-of-Domain Samples in {T}hai Word Segmentation",
+    author = "Limkonchotiwat, Peerat  and
+      Phatthiyaphaibun, Wannaphong  and
+      Sarwar, Raheem  and
+      Chuangsuwanich, Ekapol  and
+      Nutanong, Sarana",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.findings-acl.86",
+    doi = "10.18653/v1/2021.findings-acl.86",
+    pages = "1003--1016",
+}
+"""
+
+_DATASETNAME = "vistec_tp_th_21"
+
+_DESCRIPTION = """\
+The largest social media domain datasets for Thai text processing (word segmentation, 
+misspell correction and detection, and named-entity boundary) called "VISTEC-TP-TH-2021" or VISTEC-2021. 
+VISTEC corpus contains 49,997 sentences with 3.39M words where the collection was manually annotated by 
+linguists on four tasks, namely word segmentation, misspelling detection and correction, 
+and named entity recognition.
+"""
+
+_HOMEPAGE = "https://github.com/mrpeerat/OSKut/tree/main/VISTEC-TP-TH-2021"
+
+
+_LANGUAGES = ["tha"]
+
+
+_LICENSE = Licenses.CC_BY_SA_3_0.value
+
+_LOCAL = False
+
+_URLS = {
+    "train": "https://raw.githubusercontent.com/mrpeerat/OSKut/main/VISTEC-TP-TH-2021/train/VISTEC-TP-TH-2021_train_proprocessed.txt",
+    "test": "https://raw.githubusercontent.com/mrpeerat/OSKut/main/VISTEC-TP-TH-2021/test/VISTEC-TP-TH-2021_test_proprocessed.txt",
+}
+
+_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class VISTEC21Dataset(datasets.GeneratorBasedBuilder):
+    """
+    The largest social media domain datasets for Thai text processing (word segmentation,
+    misspell correction and detection, and named-entity boundary) called "VISTEC-TP-TH-2021" or VISTEC-2021.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    SEACROWD_SCHEMA_NAME = "seq_label"
+    LABEL_CLASSES = ["0", "1"]
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(datasets.features.ClassLabel(names=self.LABEL_CLASSES)),
+                }
+            )
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            features = schemas.seq_label_features(self.LABEL_CLASSES)
+
+        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_files = {
+            "train": Path(dl_manager.download_and_extract(_URLS["train"])),
+            "test": Path(dl_manager.download_and_extract(_URLS["test"])),
+        }
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_files["train"], "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_files["test"], "split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        label_key = "ner_tags" if self.config.schema == "source" else "labels"
+
+        with open(filepath, "r", encoding="utf-8") as f:
+            lines = f.readlines()
+            id = 0
+            for line in lines:
+                tokens = line.split("|")
+                token_list = []
+                ner_tag = []
+                for token in tokens:
+                    if "<ne>" in token:
+                        token = token.replace("<ne>", "")
+                        token = token.replace("</ne>", "")
+                        token_list.append(token)
+                        ner_tag.append(1)
+                        continue
+                    if "</msp>" in token and "<msp value=" in token:
+                        token_list.append(re.findall(r"<msp value=([^>]*)>", token)[0])
+                        ner_tag.append(0)
+                        continue
+                    if "<compound>" in token or "</compound>" in token:
+                        token = token.replace("<compound>", "")
+                        token = token.replace("</compound>", "")
+                        token_list.append(token)
+                        ner_tag.append(0)
+                        continue
+                    token_list.append(token)
+                    ner_tag.append(0)
+                id += 1
+                yield id, {
+                    "id": str(id),
+                    "tokens": token_list,
+                    label_key: ner_tag,
+                }