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tcope.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.
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """
+@article{gonzales_broadening_2023,
+  author    = {Gonzales, Wilkinson Daniel Wong},
+  title     = {Broadening horizons in the diachronic and sociolinguisstic study of
+  Philippine Englishes with the Twitter Corpus of Philippine Englishes (TCOPE)},
+  journal   = {English World-Wide},
+  year      = {2023},
+  url       = {https://osf.io/k3qzx},
+  doi       = {10.17605/OSF.IO/3Q5PW},
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["eng", "fil"]
+_DATASETNAME = "tcope"
+_DESCRIPTION = """
+The TCOPE dataset consists of public tweets (amounting to about 13.5 million words) collected from 13 major cities from the Philippines.
+Tweets are either purely in English or involve code-switching between English and Filipino.
+Tweets are tagged for part-of-speech and dependency parsing using spaCy. Tweets collected are from 2010 to 2021.
+The publicly available dataset is only a random sample (10%) from the whole TCOPE dataset, which consist of roughly 27 million tweets
+(amounting to about 135 million words) collected from 29 major cities during the same date range.
+"""
+
+_HOMEPAGE = "https://osf.io/3q5pw/wiki/home/"
+_LICENSE = Licenses.CC0_1_0.value
+_URL = "https://files.osf.io/v1/resources/3q5pw/providers/osfstorage/63737a5b0e715d3616a998f7"
+
+_SUPPORTED_TASKS = [Tasks.POS_TAGGING, Tasks.DEPENDENCY_PARSING]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class TCOPEDataset(datasets.GeneratorBasedBuilder):
+    """TCOPE is a dataset of Philippine English tweets by Gonzales (2023)."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    # Actual data has invalid "labels" likely due to coding errors,
+    # such as "BODY", "BIRTHDAY", "HAVAIANAS", etc. Only valid
+    # POS tags are included here and in loaded data.
+    POS_LABELS = ["NOUN", "PUNCT", "PROPN", "VERB", "PRON", "ADP", "ADJ", "ADV", "DET", "AUX", "PART", "CCONJ", "INTJ", "SPACE", "SCONJ", "NUM", "X", "SYM"]
+
+    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_seq_label",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd sequence labeling schema",
+            schema="seacrowd_seq_label",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "tcope_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "copeid": datasets.Value("string"),
+                    "userid": datasets.Value("int64"),
+                    "divided_tweet": datasets.Value("string"),
+                    "postag": datasets.Value("string"),
+                    "deptag": datasets.Value("string"),
+                    "citycode": datasets.Value("string"),
+                    "year": datasets.Value("int64"),
+                    "extendedcope": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_seq_label":
+            features = schemas.seq_label_features(label_names=self.POS_LABELS)
+
+        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."""
+        # First ZIP contains second ZIP
+        # Second ZIP has spreadsheet data
+        folder_zip_dir = dl_manager.download_and_extract(_URL)
+        spreadsheet_zip_dir = dl_manager.extract(f"{folder_zip_dir}/public_v1/spreadsheet_format.zip")
+        spreadsheet_fp = f"{spreadsheet_zip_dir}/spreadsheet_format/tcope_v1_public_sample.csv"
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": spreadsheet_fp,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        if self.config.schema not in ("source", "seacrowd_seq_label"):
+            raise ValueError(f"Received unexpected config schema {self.config.schema}")
+
+        df = pd.read_csv(filepath, index_col=None)
+        df = df.rename(columns={"divided.tweet": "divided_tweet"}).query("divided_tweet.notna()")
+
+        for index, row in df.iterrows():
+            if self.config.schema == "source":
+                example = row.to_dict()
+            elif self.config.schema == "seacrowd_seq_label":
+                tokens, tags = self.split_token_and_tag(row["postag"], valid_tags=self.POS_LABELS)
+                example = {
+                    "id": str(index),
+                    "tokens": tokens,
+                    "labels": tags,
+                }
+            yield index, example
+
+    def split_token_and_tag(self, tweet: str, valid_tags: List[str]) -> Tuple[List[str], List[str]]:
+        """Split tweet into two separate lists of tokens and tags."""
+        tokens_with_tags = tweet.split()
+        tokens = []
+        tags = []
+        for indiv_token_with_tag in tokens_with_tags:
+            token, tag = indiv_token_with_tag.rsplit("_", 1)
+            tokens.append(token)
+            if tag in valid_tags:
+                tags.append(tag)
+            else:  # Use "X"/other spaCy tag for invalid POS tags
+                tags.append("X")
+        return tokens, tags