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cc_aligned_doc.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
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@inproceedings{elkishky_ccaligned_2020,
+    author = {El-Kishky, Ahmed and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Koehn, Philipp},
+    booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)},
+    month = {November},
+    title = {{CCAligned}: A Massive Collection of Cross-lingual Web-Document Pairs},
+    year = {2020}
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.emnlp-main.480",
+    doi = "10.18653/v1/2020.emnlp-main.480",
+    pages = "5960--5969"
+}
+"""
+
+_DATASETNAME = "cc_aligned_doc"
+
+_DESCRIPTION = """\
+CCAligned consists of parallel or comparable web-document pairs in 137 languages aligned with English\
+(10 languages are from Southeast Asia; Burmese has two document collection with different scripts).\
+These web-document pairs were constructed by performing language identification on raw web-documents, \
+and ensuring corresponding language codes were corresponding in the URLs of web documents. This pattern \
+matching approach yielded more than 100 million aligned documents paired with English.
+"""
+
+_HOMEPAGE = "https://www2.statmt.org/cc-aligned/"
+
+_LANGUAGES = ["ind", "sun", "tha", "vie", "zlm", "lao", "khm", "mya", "ceb", "war"]
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_LOCAL = False
+_SUBSETS = {"id_ID": "ind", "su_ID": "sun", "th_TH": "tha", "vi_VN": "vie", "ms_MY": "zlm", "lo_LA": "lao", "km_KH": "khm", "my_MM": "mya", "my_MM_zaw": "mya", "cx_PH": "ceb", "wy_PH": "war"}
+_URLS = {_DATASETNAME: "https://data.statmt.org/cc-aligned/en_XX-{subset}.tsv.xz"}
+
+_SUPPORTED_TASKS = [Tasks.MACHINE_TRANSLATION]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class CCAlignedDocDataset(datasets.GeneratorBasedBuilder):
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    SEACROWD_SCHEMA_NAME = "t2t"
+
+    BUILDER_CONFIGS = [SEACrowdConfig(name=f"{_DATASETNAME}_{subset}_source", version=datasets.Version(_SOURCE_VERSION), description=f"{_DATASETNAME} source schema", schema="source", subset_id=f"{_DATASETNAME}",) for subset in _SUBSETS.keys()] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_{subset}_seacrowd_{schema_name}",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{schema_name}",
+            subset_id=f"{_DATASETNAME}",
+        )
+        for subset, schema_name in zip(_SUBSETS.keys(), len(_SUBSETS.keys()) * [SEACROWD_SCHEMA_NAME])
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_id_ID_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "Domain": datasets.Value("string"),
+                    "Source_URL": datasets.Value("string"),
+                    "Source_Content": datasets.Value("string"),
+                    "Target_URL": datasets.Value("string"),
+                    "Target_Content": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            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."""
+        subset = "_".join([self.config.name.split("_")[3], self.config.name.split("_")[4]])
+        urls = _URLS[_DATASETNAME].format(subset=subset)
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "train",
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        subset = "_".join([self.config.name.split("_")[3], self.config.name.split("_")[4]])
+        lines = open(filepath, "r").readlines()
+        if self.config.schema == "source":
+            idx = 0
+            for line in lines:
+                content = line.split("\t")
+                example = {
+                    "Domain": content[0],
+                    "Source_URL": content[1],
+                    "Source_Content": content[2],
+                    "Target_URL": content[3],
+                    "Target_Content": content[4],
+                }
+                yield idx, example
+                idx += 1
+        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
+            idx = 0
+            for line in lines:
+                content = line.split("\t")
+                example = {
+                    "id": str(idx),
+                    "text_1": content[2],
+                    "text_2": content[4],
+                    "text_1_name": "en",
+                    "text_2_name": _SUBSETS[subset],
+                }
+                yield idx, example
+                idx += 1