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Upload cosem.py with huggingface_hub
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cosem.py
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# coding=utf-8
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# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import re
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from typing import Dict, List, Tuple
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import datasets
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from seacrowd.utils import schemas
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from seacrowd.utils.configs import SEACrowdConfig
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from seacrowd.utils.constants import TASK_TO_SCHEMA, Licenses, Tasks
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_CITATION = """\
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@article{gonzales_corpus_2021,
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title = {The {Corpus} of {Singapore} {English} {Messages} ({CoSEM})},
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issn = {0883-2919, 1467-971X},
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url = {https://onlinelibrary.wiley.com/doi/10.1111/weng.12534},
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doi = {10.1111/weng.12534},
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language = {en},
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urldate = {2022-02-19},
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journal = {World Englishes},
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author = {Gonzales, Wilkinson Daniel Wong and Hiramoto, Mie and R. E. Leimgruber, Jakob and Lim, Jun Jie},
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month = feb,
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year = {2021},
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}
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"""
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_DATASETNAME = "cosem"
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_DESCRIPTION = """\
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The CoSEM dataset consists of over 900,000 lines of online messages from the messaging platform WhatsApp collected from personal chat
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logs of students enrolled in an advanced sociolinguistics class from the National University of Singapore. Messages collected were
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from 2016 to 2019. The dataset is in .txt format, where each line of utterance is tagged with a unique identifier that includes its
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metadata such as line number, year message was sent, and age and nationality of sender.
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"""
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_HOMEPAGE = "https://github.com/wdwgonzales/CoSEM/blob/main/Corpus/COSEM_v4_publicrelease_SEP172023.zip"
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_LANGUAGES = ["eng"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
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_LICENSE = Licenses.CC0_1_0.value
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_LOCAL = False
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_URLS = {_DATASETNAME: "https://github.com/wdwgonzales/CoSEM/raw/main/Corpus/COSEM_v4_publicrelease_SEP172023.zip"}
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_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
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_SUPPORTED_SCHEMA_STRINGS = [f"seacrowd_{str(TASK_TO_SCHEMA[task]).lower()}" for task in _SUPPORTED_TASKS]
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_SOURCE_VERSION = "1.0.0"
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_SEACROWD_VERSION = "2024.06.20"
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class CoSEMDataset(datasets.GeneratorBasedBuilder):
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"""The CoSEM dataset consists of over 900,000 lines of online messages from the messaging platform WhatsApp collected from
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personal chat logs of students enrolled in an advanced sociolinguistics class from the National University of Singapore."""
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
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subset_id = _DATASETNAME
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BUILDER_CONFIGS = [
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SEACrowdConfig(
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name=f"{subset_id}_source",
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version=SOURCE_VERSION,
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description=f"{_DATASETNAME} source schema",
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schema="source",
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subset_id=subset_id,
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)
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]
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seacrowd_schema_config: list[SEACrowdConfig] = []
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for seacrowd_schema in _SUPPORTED_SCHEMA_STRINGS:
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seacrowd_schema_config.append(
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SEACrowdConfig(
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name=f"{subset_id}_{seacrowd_schema}",
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version=SEACROWD_VERSION,
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description=f"{_DATASETNAME} {seacrowd_schema} schema",
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schema=f"{seacrowd_schema}",
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subset_id=subset_id,
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)
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)
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BUILDER_CONFIGS.extend(seacrowd_schema_config)
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
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def _info(self) -> datasets.DatasetInfo:
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if self.config.schema == "source":
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features = datasets.Features(
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{
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"id": datasets.Value("string"),
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"text": datasets.Value("string"),
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}
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)
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elif self.config.schema == f"seacrowd_{str(TASK_TO_SCHEMA[Tasks.SELF_SUPERVISED_PRETRAINING]).lower()}":
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features = schemas.ssp_features
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else:
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raise ValueError(f"Invalid config: {self.config.name}")
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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"""Returns SplitGenerators."""
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split_generators = []
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path = dl_manager.download_and_extract(_URLS[_DATASETNAME])
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split_generators.append(
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"path": os.path.join(path, "COSEM_v4_publicrelease_SEP172023"),
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},
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)
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)
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return split_generators
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def _generate_examples(self, path: str) -> Tuple[int, Dict]:
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"""Yields examples as (key, example) tuples."""
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files = os.listdir(path)
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file_paths = [os.path.join(path, file) for file in files]
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pattern = r"<(COSEM:.*?)>(.*?)(?=<COSEM:|$)"
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s = {}
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for file_path in file_paths:
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with open(file_path, "r", encoding="utf-8") as file:
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text = file.read()
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matches = re.findall(pattern, text, re.DOTALL)
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for match in matches:
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key = match[0].strip()
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value = match[1].strip()
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+
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if key in s:
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continue
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s[key] = value
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if self.config.schema == "source" or self.config.schema == f"seacrowd_{str(TASK_TO_SCHEMA[Tasks.SELF_SUPERVISED_PRETRAINING]).lower()}":
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yield key, {"id": key, "text": value}
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else:
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raise ValueError(f"Invalid config: {self.config.name}")
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