Create damt.py

damt.py

@@ -0,0 +1,119 @@
+# coding=utf-8
+"""Multi-domain German-English parallel dataset for Domain Adapted Machine Translation."""
+
+import datasets
+
+_CITATION = """\
+@inproceedings{koehn-knowles-2017-six,
+    title = "Six Challenges for Neural Machine Translation",
+    author = "Koehn, Philipp  and
+      Knowles, Rebecca",
+    booktitle = "Proceedings of the First Workshop on Neural Machine Translation",
+    month = aug,
+    year = "2017",
+    address = "Vancouver",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W17-3204",
+    doi = "10.18653/v1/W17-3204",
+    pages = "28--39",
+}
+@inproceedings{aharoni2020unsupervised,
+  title={Unsupervised domain clusters in pretrained language models},
+  author={Aharoni, Roee and Goldberg, Yoav},
+  booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+  year={2020},
+  url={https://arxiv.org/abs/2004.02105},
+  publisher = "Association for Computational Linguistics"
+}
+"""
+
+_URL = "https://drive.google.com/file/d/1yvB-pvlojtT2UpOX1JvwtD6rw9joQ49A/view"
+
+_HOMEPAGE = "https://github.com/roeeaharoni/unsupervised-domain-clusters"
+
+_DOMAIN = ["it", "koran", "law", "medical", "subtitles"]
+
+
+class DAMTConfig(datasets.BuilderConfig):
+    """BuilderConfig for DAMT Dataset"""
+
+    def __init__(self, domain=None, **kwargs):
+        """
+        Args:
+            domain: domain name.
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super(DAMTConfig, self).__init__(
+            name=domain,
+            description="multi-domain German-English parallel dataset for Domain Adapted Machine Translation.",
+            version=datasets.Version("1.0.0", ""),
+            **kwargs,
+        )
+
+        # Validate domain name.
+        assert domain in _DOMAIN
+
+        self.domain = domain
+
+
+class DAMT(datasets.GeneratorBasedBuilder):
+    """Multi-domain German-English parallel dataset for Domain Adapted Machine Translation."""
+
+    BUILDER_CONFIGS = [DAMTConfig(domain=d) for d in _DOMAIN]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description="multi-domain German-English parallel dataset for Domain Adapted Machine Translation",
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {"translation": datasets.features.Translation(languages=("en", "de"))}
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        domain = self.config.domain
+
+        def _get_drive_url(url):
+            return f"https://drive.google.com/uc?id={url.split('/')[5]}"
+
+        dl_dir = dl_manager.download_and_extract(_get_drive_url(_URL))
+        files = {
+            "train": {
+                "en_file": f"{dl_dir}/{domain}/train.en",
+                "de_file": f"{dl_dir}/{domain}/train.de",
+            },
+            "validation": {
+                "en_file": f"{dl_dir}/{domain}/dev.en",
+                "de_file": f"{dl_dir}/{domain}/dev.de",
+            },
+            "test": {
+                "en_file": f"{dl_dir}/{domain}/test.en",
+                "de_file": f"{dl_dir}/{domain}/test.de",
+            },
+        }
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=files["train"]),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=files["validation"]),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=files["test"]),
+        ]
+
+    def _generate_examples(self, en_file, de_file):
+        """Yields examples."""
+
+        id_ = 0
+        with open(en_file, "r", encoding="utf-8") as en_f:
+            with open(de_file, "r", encoding="utf-8") as de_f:
+                for en, de in zip(en_f, de_f):
+                    yield id_, {"translation": {"en": en.strip(), "de": de.strip()}}
+                    id_ += 1