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sib_200.py

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+# coding=utf-8
+# Copyright 2024 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.
+
+"""
+SIB-200 is the largest publicly available topic classification dataset based on Flores-200 covering 205 languages and dialects.
+The train/validation/test sets are available for all the 205 languages.
+"""
+
+import os
+from pathlib import Path
+from typing import List, Tuple, Dict
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks, Licenses
+
+_CITATION = """\
+@misc{adelani2023sib200,
+      title={SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects},
+      author={David Ifeoluwa Adelani and Hannah Liu and Xiaoyu Shen and Nikita Vassilyev and Jesujoba O. Alabi and Yanke Mao and Haonan Gao and Annie En-Shiun Lee},
+      year={2023},
+      eprint={2309.07445},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DATASETNAME = "sib_200"
+
+_DESCRIPTION = """\
+SIB-200 is the largest publicly available topic classification dataset based on Flores-200 covering 205 languages and dialects.
+The train/validation/test sets are available for all the 205 languages.
+"""
+
+_HOMEPAGE = "https://github.com/dadelani/sib-200"
+
+_LANGUAGES = [
+    "ace",
+    "ban",
+    "bjn",
+    "bug",
+    "ceb",
+    "ilo",
+    "ind",
+    "jav",
+    "kac",
+    "khm",
+    "lao",
+    "lus",
+    "min",
+    "mya",
+    "pag",
+    "shn",
+    "sun",
+    "tgl",
+    "tha",
+    "vie",
+    "war",
+    "zsm",
+]
+
+_SUPPORTED_LANGUAGE_CODES = [
+    "ace_Arab",
+    "ace_Latn",
+    "ban_Latn",
+    "bjn_Arab",
+    "bjn_Latn",
+    "bug_Latn",
+    "ceb_Latn",
+    "ilo_Latn",
+    "ind_Latn",
+    "jav_Latn",
+    "kac_Latn",
+    "khm_Khmr",
+    "lao_Laoo",
+    "lus_Latn",
+    "min_Arab",
+    "min_Latn",
+    "mya_Mymr",
+    "pag_Latn",
+    "shn_Mymr",
+    "sun_Latn",
+    "tgl_Latn",
+    "tha_Thai",
+    "vie_Latn",
+    "war_Latn",
+    "zsm_Latn",
+]
+
+_LICENSE = Licenses.CC_BY_SA_4_0.value
+
+_LOCAL = False
+
+# This can be an arbitrarily nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "base_url": "https://huggingface.co/datasets/Davlan/sib200/raw/main/data"
+}
+
+_SUPPORTED_TASKS = [Tasks.TOPIC_MODELING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+_SEACROWD_SCHEMA = f"seacrowd_text"
+
+
+def _sib_config_constructor(lang: str, schema: str = _SEACROWD_SCHEMA, version: str = _SEACROWD_VERSION) -> SEACrowdConfig:
+    return SEACrowdConfig(
+        name=f"{_DATASETNAME}_{lang}_{schema}",
+        version=version,
+        description=f"SIB-200 {schema} schema",
+        schema=schema,
+        subset_id=f"SIB-200 {lang}",
+    )
+
+
+class Sib200Dataset(datasets.GeneratorBasedBuilder):
+    """
+    SIB-200 is the largest publicly available topic classification dataset based on Flores-200 covering 205 languages and dialects.
+    The train/validation/test sets are available for all the 205 languages.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    def _populate_configs():
+        configs = [_sib_config_constructor(lang, schema="source", version=_SOURCE_VERSION) for lang in _SUPPORTED_LANGUAGE_CODES] + [_sib_config_constructor(lang, schema=_SEACROWD_SCHEMA, version=_SEACROWD_VERSION) for lang in _SUPPORTED_LANGUAGE_CODES]
+
+        all_lang_source_config = SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=_SOURCE_VERSION,
+            description=f"SIB-200 source schema",
+            schema="source",
+            subset_id=f"SIB-200 SEA",
+        )
+
+        all_lang_t2t_config = SEACrowdConfig(
+            name=f"{_DATASETNAME}_{_SEACROWD_SCHEMA}",
+            version=_SEACROWD_VERSION,
+            description=f"SIB-200 {_SEACROWD_SCHEMA} schema",
+            schema=_SEACROWD_SCHEMA,
+            subset_id=f"SIB-200 SEA",
+        )
+
+        configs.append(all_lang_source_config)
+        configs.append(all_lang_t2t_config)
+        return configs
+
+    BUILDER_CONFIGS = _populate_configs()
+
+    DEFAULT_CONFIG_NAME = "sib_200_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "index_id": datasets.Value("int64"),
+                    "text": datasets.Value("string"),
+                    "category": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_text":
+            features = schemas.text_features(["geography", "science/technology", "health", "travel", "entertainment", "politics", "sports"])
+
+        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."""
+        # dl_manager not used since dataloader uses HF 'load_dataset'
+        lang = self.config.subset_id.split(" ")[-1]
+        if lang in _SUPPORTED_LANGUAGE_CODES:
+            train_paths = [Path(dl_manager.download_and_extract(f"{_URLS['base_url']}/{lang}/train.tsv"))]
+            valid_paths = [Path(dl_manager.download_and_extract(f"{_URLS['base_url']}/{lang}/dev.tsv"))]
+            test_paths = [Path(dl_manager.download_and_extract(f"{_URLS['base_url']}/{lang}/test.tsv"))]
+            lang_codes = [lang]
+        elif lang == "SEA":
+            train_paths, valid_paths, test_paths, lang_codes = [], [], [], []
+            for lang in _SUPPORTED_LANGUAGE_CODES:                
+                train_paths.append(Path(dl_manager.download_and_extract(f"{_URLS['base_url']}/{lang}/train.tsv")))
+                valid_paths.append(Path(dl_manager.download_and_extract(f"{_URLS['base_url']}/{lang}/dev.tsv")))
+                test_paths.append(Path(dl_manager.download_and_extract(f"{_URLS['base_url']}/{lang}/test.tsv")))
+                lang_codes.append(lang)
+        else:
+            raise ValueError(f"Language {lang} not a SEA language in the dataset")
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, 
+                gen_kwargs={"file_paths": train_paths, "lang_codes": lang_codes}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION, 
+                gen_kwargs={"file_paths": valid_paths, "lang_codes": lang_codes}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, 
+                gen_kwargs={"file_paths": test_paths, "lang_codes": lang_codes}
+            )
+        ]
+
+    def _generate_examples(self, file_paths: List[str], lang_codes: List[str]) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        index = 0
+        for file_path, lang_code in zip(file_paths, lang_codes):
+            lang_df = pd.read_csv(file_path, sep='\t')
+            for row in lang_df.itertuples():
+                if self.config.schema == "source":
+                    example = {
+                        "index_id": row.index_id,
+                        "text": row.text,
+                        "category": row.category,
+                    }
+
+                elif self.config.schema == "seacrowd_text":
+                    example = {
+                        "id": f'{lang_code}_{row.index_id}',
+                        "text": row.text,
+                        "label": row.category,
+                    }
+                yield index, example
+                index += 1