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aya_collection_translated.py

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+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 = """
+@misc{singh2024aya,
+      title={Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning},
+      author={Shivalika Singh and Freddie Vargus and Daniel Dsouza and Börje F. Karlsson and
+      Abinaya Mahendiran and Wei-Yin Ko and Herumb Shandilya and Jay Patel and Deividas
+      Mataciunas and Laura OMahony and Mike Zhang and Ramith Hettiarachchi and Joseph
+      Wilson and Marina Machado and Luisa Souza Moura and Dominik Krzemiński and Hakimeh
+      Fadaei and Irem Ergün and Ifeoma Okoh and Aisha Alaagib and Oshan Mudannayake and
+      Zaid Alyafeai and Vu Minh Chien and Sebastian Ruder and Surya Guthikonda and Emad A.
+      Alghamdi and Sebastian Gehrmann and Niklas Muennighoff and Max Bartolo and Julia Kreutzer
+      and Ahmet Üstün and Marzieh Fadaee and Sara Hooker},
+      year={2024},
+      eprint={2402.06619},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DATASETNAME = "aya_collection_translated"
+
+_DESCRIPTION = """
+The Aya Collection is a massive multilingual collection consisting of 513 million instances of prompts and
+completions covering a wide range of tasks. This dataset covers the translated prompts of the Aya Collection.
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split"
+
+_LANGUAGES = ["ceb", "tha", "mya", "zsm", "jav", "ind", "vie", "sun", "ace", "bjn", "khm", "lao", "min"]
+
+_LICENSE = Licenses.APACHE_2_0.value
+
+_LOCAL = False
+
+_URLS = {
+    "ceb": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/cebuano",
+    "tha": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/thai",
+    "mya": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/burmese",
+    "zsm": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/malayalam",
+    "jav": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/javanese",
+    "ind": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/indonesian",
+    "vie": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/vietnamese",
+    "sun": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/sundanese",
+    "ace": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/achinese",
+    "bjn": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/banjar",
+    "khm": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/central_khmer",
+    "lao": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/lao",
+    "min": "https://huggingface.co/datasets/CohereForAI/aya_collection_language_split/resolve/main/minangkabau",
+}
+
+_SUPPORTED_TASKS = [Tasks.INSTRUCTION_TUNING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class AyaCollectionTranslatedDataset(datasets.GeneratorBasedBuilder):
+    """
+    The Aya Collection is a massive multilingual collection consisting of 513 million instances of prompts and
+    completions covering a wide range of tasks. This dataset covers the translated prompts of the Aya Collection.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{LANG}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{_DATASETNAME} {LANG} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_{LANG}",
+        )
+        for LANG in _LANGUAGES
+    ] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{LANG}_seacrowd_t2t",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} {LANG} SEACrowd schema",
+            schema="seacrowd_t2t",
+            subset_id=f"{_DATASETNAME}_{LANG}",
+        )
+        for LANG in _LANGUAGES
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_ind_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("int64"),
+                    "inputs": datasets.Value("string"),
+                    "targets": datasets.Value("string"),
+                    "dataset_name": datasets.Value("string"),
+                    "sub_dataset_name": datasets.Value("string"),
+                    "task_type": datasets.Value("string"),
+                    "template_id": datasets.Value("int64"),
+                    "language": datasets.Value("string"),
+                    "script": datasets.Value("string"),
+                    "split": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_t2t":
+            features = schemas.text2text_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."""
+
+        language = self.config.name.split("_")[3]
+
+        if language in _LANGUAGES:
+            data_train_paths = []
+            for version in [0, 1, 2]:
+                for all in [1, 2, 3]:
+                    if version >= all:
+                        continue
+                    else:
+                        try:
+                            data_train_path = Path(dl_manager.download_and_extract(f"{_URLS[language]}/train-0000{version}-of-0000{all}.parquet?download=true"))
+                            data_train_paths.append(data_train_path)
+                        except Exception:
+                            continue
+
+            data_validation_path = Path(dl_manager.download_and_extract(f"{_URLS[language]}/validation-00000-of-00001.parquet?download=true"))
+            data_test_path = Path(dl_manager.download_and_extract(f"{_URLS[language]}/test-00000-of-00001.parquet?download=true"))
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_train_paths,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_test_path,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_validation_path,
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        if isinstance(filepath, Path):
+            dfs = [pd.read_parquet(filepath)]
+        else:
+            dfs = [pd.read_parquet(path) for path in filepath]
+
+        df = pd.concat(dfs, ignore_index=True)
+
+        for index, row in df.iterrows():
+            if self.config.schema == "source":
+                example = row.to_dict()
+
+            elif self.config.schema == "seacrowd_t2t":
+                example = {
+                    "id": str(index),
+                    "text_1": row["inputs"],
+                    "text_2": row["targets"],
+                    "text_1_name": "inputs",
+                    "text_2_name": "targets",
+                }
+
+            yield index, example