Upload codeswitch_reddit.py with huggingface_hub

codeswitch_reddit.py

@@ -0,0 +1,209 @@
+# 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 html
+import os
+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 = """
+@inproccedings{rabinovich-2019-codeswitchreddit,
+  author    = {Rabinovich, Ella and Sultani, Masih and Stevenson, Suzanne},
+  title     = {CodeSwitch-Reddit: Exploration of Written Multilingual Discourse in Online Discussion Forums},
+  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing},
+  publisher = {Association for Computational Linguistics},
+  year      = {2019},
+  url       = {https://aclanthology.org/D19-1484},
+  doi       = {10.18653/v1/D19-1484},
+  pages     = {4776--4786},
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["eng", "ind", "tgl"]
+_DATASETNAME = "codeswitch_reddit"
+_DESCRIPTION = """
+This corpus consists of monolingual English and multilingual (English and one other language) posts
+from country-specific subreddits, including r/indonesia, r/philippines and r/singapore for Southeast Asia.
+Posts were manually classified whether they contained code-switching or not.
+"""
+
+_HOMEPAGE = "https://github.com/ellarabi/CodeSwitch-Reddit"
+_LICENSE = Licenses.UNKNOWN.value
+_URL = "http://www.cs.toronto.edu/~ella/code-switch.reddit.tar.gz"
+
+_SUPPORTED_TASKS = [Tasks.CODE_SWITCHING_IDENTIFICATION, Tasks.SELF_SUPERVISED_PRETRAINING]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class CodeSwitchRedditDataset(datasets.GeneratorBasedBuilder):
+    """Dataset of monolingual English and multilingual comments from country-specific subreddits."""
+
+    SUBSETS = ["cs", "eng_monolingual"]
+    INCLUDED_SUBREDDITS = ["indonesia", "Philippines", "singapore"]
+    INCLUDED_LANGUAGES = {"English": "eng", "Indonesian": "ind", "Tagalog": "tgl"}
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} source schema for {subset} subset",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_{subset}",
+        )
+        for subset in SUBSETS
+    ] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_eng_monolingual_seacrowd_ssp",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd ssp schema for eng_monolingual subset",
+            schema="seacrowd_ssp",
+            subset_id=f"{_DATASETNAME}_eng_monolingual",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_cs_seacrowd_text_multi",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd text multilabel schema for cs subset",
+            schema="seacrowd_text_multi",
+            subset_id=f"{_DATASETNAME}_cs",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_cs_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            if "cs" in self.config.subset_id:
+                features = datasets.Features(
+                    {
+                        "author": datasets.Value("string"),
+                        "subreddit": datasets.Value("string"),
+                        "country": datasets.Value("string"),
+                        "date": datasets.Value("int32"),
+                        "confidence": datasets.Value("int32"),
+                        "lang1": datasets.Value("string"),
+                        "lang2": datasets.Value("string"),
+                        "text": datasets.Value("string"),
+                        "id": datasets.Value("string"),
+                        "link_id": datasets.Value("string"),
+                        "parent_id": datasets.Value("string"),
+                    }
+                )
+            elif "eng_monolingual" in self.config.subset_id:
+                features = datasets.Features(
+                    {
+                        "author": datasets.Value("string"),
+                        "subreddit": datasets.Value("string"),
+                        "country": datasets.Value("string"),
+                        "date": datasets.Value("int32"),
+                        "confidence": datasets.Value("int32"),
+                        "lang": datasets.Value("string"),
+                        "text": datasets.Value("string"),
+                    }
+                )
+
+        elif self.config.schema == "seacrowd_ssp":
+            features = schemas.ssp_features
+        elif self.config.schema == "seacrowd_text_multi":
+            features = schemas.text_multi_features(label_names=list(self.INCLUDED_LANGUAGES.values()))
+
+        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_dir = dl_manager.download_and_extract(_URL)
+        if "cs" in self.config.subset_id:
+            filepath = os.path.join(data_dir, "cs_main_reddit_corpus.csv")
+        elif "eng_monolingual" in self.config.subset_id:
+            filepath = os.path.join(data_dir, "eng_monolingual_reddit_corpus.csv")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": filepath,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        df = pd.read_csv(filepath, index_col=None, header="infer", encoding="utf-8")
+        df = df[df["Subreddit"].isin(self.INCLUDED_SUBREDDITS)]
+
+        if self.config.subset_id.split("_")[-1] == "cs":
+            df = df[(df["Lang1"].isin(self.INCLUDED_LANGUAGES)) & (df["Lang2"].isin(self.INCLUDED_LANGUAGES))]
+            df.reset_index(drop=True, inplace=True)
+
+            for index, row in df.iterrows():
+                parsed_text = html.unescape(row["Text"])
+                if self.config.schema == "source":
+                    example = {
+                        "author": row["Author"],
+                        "subreddit": row["Subreddit"],
+                        "country": row["Country"],
+                        "date": row["Date"],
+                        "confidence": row["confidence"],
+                        "lang1": row["Lang1"],
+                        "lang2": row["Lang2"],
+                        "text": parsed_text,
+                        "id": row["id"],
+                        "link_id": row["link_id"],
+                        "parent_id": row["parent_id"],
+                    }
+
+                elif self.config.schema == "seacrowd_text_multi":
+                    lang_one, lang_two = self.INCLUDED_LANGUAGES[row["Lang1"]], self.INCLUDED_LANGUAGES[row["Lang2"]]
+                    example = {
+                        "id": str(index),
+                        "text": parsed_text,
+                        "labels": list(sorted([lang_one, lang_two])),  # Language order doesn't matter in original dataset; just arrange alphabetically for consistency
+                    }
+                yield index, example
+
+        else:
+            df.reset_index(drop=True, inplace=True)
+            for index, row in df.iterrows():
+                parsed_text = html.unescape(row["Text"])
+                if self.config.schema == "source":
+                    example = {
+                        "author": row["Author"],
+                        "subreddit": row["Subreddit"],
+                        "country": row["Country"],
+                        "date": row["Date"],
+                        "confidence": row["confidence"],
+                        "lang": row["Lang"],
+                        "text": parsed_text,
+                    }
+                elif self.config.schema == "seacrowd_ssp":
+                    example = {
+                        "id": str(index),
+                        "text": parsed_text,
+                    }
+                yield index, example