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cosem.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 typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses, Tasks
+
+_CITATION = """\
+@article{gonzales_corpus_2021,
+    title = {The {Corpus} of {Singapore} {English} {Messages} ({CoSEM})},
+    issn = {0883-2919, 1467-971X},
+    url = {https://onlinelibrary.wiley.com/doi/10.1111/weng.12534},
+    doi = {10.1111/weng.12534},
+    language = {en},
+    urldate = {2022-02-19},
+    journal = {World Englishes},
+    author = {Gonzales, Wilkinson Daniel Wong and Hiramoto, Mie and R. E. Leimgruber, Jakob and Lim, Jun Jie},
+    month = feb,
+    year = {2021},
+}
+"""
+
+_DATASETNAME = "cosem"
+
+_DESCRIPTION = """\
+The CoSEM dataset consists of over 900,000 lines of online messages from the messaging platform WhatsApp collected from personal chat
+logs of students enrolled in an advanced sociolinguistics class from the National University of Singapore. Messages collected were
+from 2016 to 2019. The dataset is in .txt format, where each line of utterance is tagged with a unique identifier that includes its
+metadata such as line number, year message was sent, and age and nationality of sender.
+"""
+
+_HOMEPAGE = "https://github.com/wdwgonzales/CoSEM/blob/main/Corpus/COSEM_v4_publicrelease_SEP172023.zip"
+
+_LANGUAGES = ["eng"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+
+_LICENSE = Licenses.CC0_1_0.value
+
+_LOCAL = False
+
+_URLS = {_DATASETNAME: "https://github.com/wdwgonzales/CoSEM/raw/main/Corpus/COSEM_v4_publicrelease_SEP172023.zip"}
+
+_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
+_SUPPORTED_SCHEMA_STRINGS = [f"seacrowd_{str(TASK_TO_SCHEMA[task]).lower()}" for task in _SUPPORTED_TASKS]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class CoSEMDataset(datasets.GeneratorBasedBuilder):
+    """The CoSEM dataset consists of over 900,000 lines of online messages from the messaging platform WhatsApp collected from
+    personal chat logs of students enrolled in an advanced sociolinguistics class from the National University of Singapore."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    subset_id = _DATASETNAME
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{subset_id}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=subset_id,
+        )
+    ]
+
+    seacrowd_schema_config: list[SEACrowdConfig] = []
+
+    for seacrowd_schema in _SUPPORTED_SCHEMA_STRINGS:
+
+        seacrowd_schema_config.append(
+            SEACrowdConfig(
+                name=f"{subset_id}_{seacrowd_schema}",
+                version=SEACROWD_VERSION,
+                description=f"{_DATASETNAME} {seacrowd_schema} schema",
+                schema=f"{seacrowd_schema}",
+                subset_id=subset_id,
+            )
+        )
+
+    BUILDER_CONFIGS.extend(seacrowd_schema_config)
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == f"seacrowd_{str(TASK_TO_SCHEMA[Tasks.SELF_SUPERVISED_PRETRAINING]).lower()}":
+            features = schemas.ssp_features
+
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")
+
+        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."""
+
+        split_generators = []
+
+        path = dl_manager.download_and_extract(_URLS[_DATASETNAME])
+
+        split_generators.append(
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "path": os.path.join(path, "COSEM_v4_publicrelease_SEP172023"),
+                },
+            )
+        )
+
+        return split_generators
+
+    def _generate_examples(self, path: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        files = os.listdir(path)
+        file_paths = [os.path.join(path, file) for file in files]
+        pattern = r"<(COSEM:.*?)>(.*?)(?=<COSEM:|$)"
+
+        s = {}
+
+        for file_path in file_paths:
+            with open(file_path, "r", encoding="utf-8") as file:
+                text = file.read()
+
+                matches = re.findall(pattern, text, re.DOTALL)
+                for match in matches:
+                    key = match[0].strip()
+                    value = match[1].strip()
+
+                    if key in s:
+                        continue
+                    s[key] = value
+
+                    if self.config.schema == "source" or self.config.schema == f"seacrowd_{str(TASK_TO_SCHEMA[Tasks.SELF_SUPERVISED_PRETRAINING]).lower()}":
+                        yield key, {"id": key, "text": value}
+
+                    else:
+                        raise ValueError(f"Invalid config: {self.config.name}")