Upload dbp15k-fr-en.py

dbp15k-fr-en.py

@@ -0,0 +1,170 @@
+import datasets
+import os
+
+class EntityAlignmentConfig(datasets.BuilderConfig):
+    """BuilderConfig for SuperGLUE."""
+
+    def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
+        """BuilderConfig for SuperGLUE.
+        Args:
+          features: `list[string]`, list of the features that will appear in the
+            feature dict. Should not include "label".
+          data_url: `string`, url to download the zip file from.
+          citation: `string`, citation for the data set.
+          url: `string`, url for information about the data set.
+          label_classes: `list[string]`, the list of classes for the label if the
+            label is present as a string. Non-string labels will be cast to either
+            'False' or 'True'.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        # Version history:
+        # 1.0.3: Fix not including entity position in ReCoRD.
+        # 1.0.2: Fixed non-nondeterminism in ReCoRD.
+        # 1.0.1: Change from the pre-release trial version of SuperGLUE (v1.9) to
+        #        the full release (v2.0).
+        # 1.0.0: S3 (new shuffling, sharding and slicing mechanism).
+        # 0.0.2: Initial version.
+        super(EntityAlignmentConfig, self).__init__(version=datasets.Version("1.0.3"), **kwargs)
+        self.features = features
+        self.label_classes = label_classes
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+
+
+class EntityAlignment(datasets.GeneratorBasedBuilder):
+    """The SuperGLUE benchmark."""
+
+    BUILDER_CONFIGS = [
+        EntityAlignmentConfig(
+            name="source",
+            features=["column1", "column2", "column3"],
+            data_url="https://www.dropbox.com/s/j55ly9i7w7t4tnn/dbp15k-fr-en-src.zip"
+        ),
+        EntityAlignmentConfig(
+            name="target",
+            features=["column1", "column2", "column3"],
+            data_url="https://www.dropbox.com/s/eo2huntzhfti1p1/dbp15k-fr-en-tgt.zip"
+        ),
+        EntityAlignmentConfig(
+            name="pairs",
+            features=["left_id", "right_id"],
+            data_url="https://www.dropbox.com/s/5lhkfka1imuum1o/dbp15k-fr-en-pairs.zip"
+        ),
+    ]
+
+    def _info(self):
+        features = {feature: datasets.Value("string") for feature in self.config.features}
+
+        return datasets.DatasetInfo(
+            features=datasets.Features(features)
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_dir = dl_manager.download_and_extract(self.config.data_url) or ""
+        #task_name = _get_task_name_from_data_url(self.config.data_url)
+        #dl_dir = os.path.join(dl_dir, task_name)
+        if self.config.name == "source":
+            return [
+            datasets.SplitGenerator(
+                name="ent_ids",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "ent_ids_1"),
+                    "split": "ent_ids",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="rel_ids",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "rel_ids_1"),
+                    "split": "rel_ids",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="attr_triples",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "attr_triples_1"),
+                    "split": "attr_triples",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="rel_triples",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "triples_1"),
+                    "split": "rel_triples",
+                },
+            ),
+            ]
+        elif self.config.name == "target":
+            return [
+            datasets.SplitGenerator(
+                name="ent_ids",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "ent_ids_2"),
+                    "split": "ent_ids",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="rel_ids",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "rel_ids_2"),
+                    "split": "rel_ids",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="attr_triples",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "attr_triples_2"),
+                    "split": "attr_triples",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="rel_triples",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "triples_2"),
+                    "split": "rel_triples",
+                },
+            ),
+            ]
+        elif self.config.name == "pairs":
+            return [
+            datasets.SplitGenerator(
+                name="train",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "sup_pairs"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="test",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "ref_pairs"),
+                    "split": "test",
+                },
+            ),
+            ]
+
+
+    def _generate_examples(self, data_file, split):
+        f = open(data_file,"r",encoding='utf-8')
+        data = f.readlines()
+        for i in range(len(data)):
+            row = data[i].strip('\n').split('\t')
+            if self.config.name in ["source", "target"]:
+                if split in ["ent_ids","rel_ids"]:
+                    yield i, {
+                                "column1": row[0],
+                                "column2": row[1],
+                                "column3": None
+                            }
+                else:
+                    yield i, {
+                                "column1": row[0],
+                                "column2": row[1],
+                                "column3": row[2]
+                            }
+            if self.config.name == "pairs":
+                yield i, {
+                            "left_id": row[0],
+                            "right_id": row[1]
+                }