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sap_wat.py

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+from pathlib import Path
+from typing import List
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_DATASETNAME = "sap_wat"
+
+_LANGUAGES = ["eng", "ind", "zlm", "tha", "vie"] 
+
+_CITATION = """\
+@inproceedings{buschbeck-exel-2020-parallel,
+    title = "A Parallel Evaluation Data Set of Software Documentation with Document Structure Annotation",
+    author = "Buschbeck, Bianka  and
+      Exel, Miriam",
+    editor = "Nakazawa, Toshiaki  and
+      Nakayama, Hideki  and
+      Ding, Chenchen  and
+      Dabre, Raj  and
+      Kunchukuttan, Anoop  and
+      Pa, Win Pa  and
+      Bojar, Ond{\v{r}}ej  and
+      Parida, Shantipriya  and
+      Goto, Isao  and
+      Mino, Hidaya  and
+      Manabe, Hiroshi  and
+      Sudoh, Katsuhito  and
+      Kurohashi, Sadao  and
+      Bhattacharyya, Pushpak",
+    booktitle = "Proceedings of the 7th Workshop on Asian Translation",
+    month = dec,
+    year = "2020",
+    address = "Suzhou, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.wat-1.20",
+    pages = "160--169",
+    abstract = "This paper accompanies the software documentation data set for machine translation, a parallel 
+    evaluation data set of data originating from the SAP Help Portal, that we released to the machine translation 
+    community for research purposes. It offers the possibility to tune and evaluate machine translation systems 
+    in the domain of corporate software documentation and contributes to the availability of a wider range of 
+    evaluation scenarios. The data set comprises of the language pairs English to Hindi, Indonesian, Malay and 
+    Thai, and thus also increases the test coverage for the many low-resource language pairs. Unlike most evaluation 
+    data sets that consist of plain parallel text, the segments in this data set come with additional metadata that 
+    describes structural information of the document context. We provide insights into the origin and creation, the 
+    particularities and characteristics of the data set as well as machine translation results.",
+}
+
+"""
+
+_DESCRIPTION = """The data set originates from the SAP Help Portal that contains documentation for SAP products and user 
+assistance for product-related questions. The data has been processed in a way that makes it suitable as development and 
+test data for machine translation purposes. The current language scope is English to Hindi, Indonesian, Japanese, Korean, 
+Malay, Thai, Vietnamese, Simplified Chinese and Traditional Chinese. For each language pair about 4k segments are available, 
+split into development and test data. The segments are provided in their document context and are annotated with additional 
+metadata from the document."""
+
+_HOMEPAGE = "https://github.com/SAP/software-documentation-data-set-for-machine-translation"
+
+_LICENSE = Licenses.CC_BY_NC_4_0.value
+
+_URLs = {
+    _DATASETNAME: "https://raw.githubusercontent.com/SAP/software-documentation-data-set-for-machine-translation/master/{split}_data/en{lang}/software_documentation.{split}.en{lang}.{appx}"
+}
+
+_SUPPORTED_TASKS = [
+    Tasks.MACHINE_TRANSLATION
+]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+_SUBSET = ["id", "ms", "th", "vi"]
+
+_LOCAL = False
+
+class SapWatDataset(datasets.GeneratorBasedBuilder):
+    """SAP WAT is a software documentation dataset for machine translation. The current language scope is English to Hindi, 
+    Indonesian, Japanese, Korean, Malay, Thai, Vietnamese, Simplified Chinese and Traditional Chinese. Here, we only consider 
+    EN-ID, EN-TH, EN-MS, EN-VI"""
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_en_{lang}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"SAP WAT source schema for EN-{lang.upper()}",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_en_{lang}",
+        ) 
+        for lang in _SUBSET] + [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_en_{lang}_seacrowd_t2t",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"SAP WAT SEACrowd schema for EN-{lang.upper()}",
+            schema="seacrowd_t2t",
+            subset_id=f"{_DATASETNAME}_en_{lang}",
+        ) 
+        for lang in _SUBSET
+    ]
+
+    DEFAULT_CONFIG_NAME = "sap_wat_en_id_source"
+
+    def _info(self):
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "label": 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]:
+        lang = self.config.name.split("_")[3]
+
+        splits = {datasets.Split.VALIDATION: "dev", datasets.Split.TEST: "test"}
+        data_urls = {
+            split: _URLs[_DATASETNAME].format(split=splits[split], lang=lang, appx=lang) for split in splits
+        }
+        dl_paths = dl_manager.download(data_urls)
+
+        en_data_urls = {
+            split: _URLs[_DATASETNAME].format(split=splits[split], lang=lang, appx="en") for split in splits
+        }
+        en_dl_paths = dl_manager.download(en_data_urls)
+        return [
+            datasets.SplitGenerator(
+                name=split,
+                gen_kwargs={"filepath": dl_paths[split], "en_filepath": en_dl_paths[split]},
+            )
+            for split in splits
+        ]
+
+    def _generate_examples(self, filepath: Path, en_filepath: Path):
+        with open(en_filepath, "r") as f:
+            lines_1 = f.readlines()
+        with open(filepath, "r") as f:
+            lines_2 = f.readlines()
+
+        if self.config.schema == "source":
+            for _id, (line_1, line_2) in enumerate(zip(lines_1, lines_2)):
+                ex = {
+                    "id": _id,
+                    "text": line_1.strip(),
+                    "label": line_2.strip()
+                }                
+                yield _id, ex
+
+        elif self.config.schema == "seacrowd_t2t":
+            lang = self.config.name.split("_")[3]
+            lang_name = _LANGUAGES[_SUBSET.index(lang)+1]
+
+            for _id, (line_1, line_2) in enumerate(zip(lines_1, lines_2)):
+                ex = {
+                    "id": _id,
+                    "text_1": line_1.strip(),
+                    "text_2": line_2.strip(),
+                    "text_1_name": 'eng',
+                    "text_2_name": lang_name,
+                }
+                yield _id, ex
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")