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culturay.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 io
+import json
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import zstandard as zstd
+from huggingface_hub import HfFileSystem
+
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import (SCHEMA_TO_FEATURES, TASK_TO_SCHEMA,
+                                      Licenses, Tasks)
+
+_CITATION = """\
+@misc{nguyen2024culturay,
+      title={CulturaY: A Large Cleaned Multilingual Dataset of 75 Languages}, 
+      author={Thuat Nguyen, Huu Nguyen and Thien Nguyen},
+      year={2024},
+}
+"""
+
+_DATASETNAME = "culturay"
+
+_DESCRIPTION = """\
+CulturaY: A Large Cleaned Multilingual Dataset of 75 Languages From the team
+that brought you CulutraX, we present CulturaY, another substantial multilingual
+dataset of 15TB (uncompressed)/3TB (zstd-compressed) that applies the same
+dataset cleaning methodology to the HPLT v1.1 dataset. Please note that HPLT
+v1.2 has also been released and is an alternative verison with different
+cleaning methodolgies. This data was used in part to train our SOTA Vietnamese
+model: Vistral-7B-Chat.
+
+Before using this dataloader, please accept the acknowledgement at
+https://huggingface.co/datasets/ontocord/CulturaY and use huggingface-cli login
+for authentication.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/ontocord/CulturaY"
+
+_LANGUAGES = ["mya", "fil", "zlm", "vie", "ind", "tha"]
+
+_LICENSE = Licenses.CC_BY_4_0.value
+
+_LOCAL = False
+
+_BASE_URL = "https://huggingface.co/datasets/ontocord/CulturaY/resolve/main/{lang}/"
+
+_SUPPORTED_TASKS = [Tasks.SELF_SUPERVISED_PRETRAINING]
+_SEACROWD_SCHEMA = f"seacrowd_{TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]].lower()}"  # ssp
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class CulturaYDataset(datasets.GeneratorBasedBuilder):
+    """Substantial multilingual dataset by cleaning HPLT v1.1 (Internet Archive) data."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = []
+    for subset in _LANGUAGES:
+        BUILDER_CONFIGS += [
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{subset}_source",
+                version=SOURCE_VERSION,
+                description=f"{_DATASETNAME} {subset} source schema",
+                schema="source",
+                subset_id=subset,
+            ),
+            SEACrowdConfig(
+                name=f"{_DATASETNAME}_{subset}_{_SEACROWD_SCHEMA}",
+                version=SEACROWD_VERSION,
+                description=f"{_DATASETNAME} {subset} SEACrowd schema",
+                schema=_SEACROWD_SCHEMA,
+                subset_id=subset,
+            ),
+        ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_my_source"  # smallest wrt n_doc
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("int64"),
+                    "document_lang": datasets.Value("string"),
+                    "scores": datasets.Sequence(datasets.Value("float64")),
+                    "langs": datasets.Sequence(datasets.Value("string")),
+                    "text": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                    "collection": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == _SEACROWD_SCHEMA:
+            features = SCHEMA_TO_FEATURES[
+                TASK_TO_SCHEMA[_SUPPORTED_TASKS[0]]
+            ]  # ssp_features
+
+        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 is not yet extracted for efficient generation."""
+        lang_dict = {"mya": "my", "fil": "tl", "zlm": "ms", "vie": "vi", "ind": "id", "tha": "th"}
+        subset = lang_dict[self.config.subset_id]
+        base_path = _BASE_URL.format(lang=subset)
+
+        fs = HfFileSystem(token=dl_manager.download_config.token)
+        file_list = fs.ls(f"datasets/ontocord/CulturaY/{subset}", detail=False)
+
+        data_urls = [
+            f"{base_path}{filename.split('/')[-1]}"
+            for filename in file_list
+            if filename.endswith(".jsonl.zst")
+        ]
+
+        data_paths = list(map(Path, dl_manager.download(data_urls)))
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_paths": data_paths,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_paths: Path) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        key = 0
+        for data_path in data_paths:
+            with open(data_path, "rb") as f:
+                # Zstandard decompression
+                dctx = zstd.ZstdDecompressor()
+                reader = dctx.stream_reader(f)
+                text_io = io.TextIOWrapper(reader, encoding="utf-8")
+
+                # read jsonl file by line and yield
+                for line in text_io:
+                    data = json.loads(line)
+                    if self.config.schema == "source":
+                        yield key, {
+                            "id": data["id"],
+                            "document_lang": data["document_lang"],
+                            "scores": data["scores"],
+                            "langs": data["langs"],
+                            "text": data["text"],
+                            "url": data["url"],
+                            "collection": data["collection"],
+                        }
+                    elif self.config.schema == _SEACROWD_SCHEMA:
+                        yield key, {
+                            "id": str(data["id"]),
+                            "text": data["text"],
+                        }
+                    key += 1