import datasets import os class Dbp15kFrEnConfig(datasets.BuilderConfig): 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(Dbp15kFrEnConfig, 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 Dbp15kFrEn(datasets.GeneratorBasedBuilder): BUILDER_CONFIGS = [ Dbp15kFrEnConfig( name="source", features=["column1", "column2", "column3"], citation="TODO", url="TODO", data_url="https://huggingface.co/datasets/matchbench/dbp15k-fr-en/resolve/main/dbp15k-fr-en-src.zip" ), Dbp15kFrEnConfig( name="target", features=["column1", "column2", "column3"], citation="TODO", url="TODO", data_url="https://huggingface.co/datasets/matchbench/dbp15k-fr-en/resolve/main/dbp15k-fr-en-tgt.zip" ), Dbp15kFrEnConfig( name="pairs", features=["left_id", "right_id"], citation="TODO", url="TODO", data_url="https://huggingface.co/datasets/matchbench/dbp15k-fr-en/resolve/main/dbp15k-fr-en-pairs.zip" ), ] def _info(self): if self.config.name=="source": features = {feature: datasets.Value("string") for feature in self.config.features} elif self.config.name=="target": features = {feature: datasets.Value("string") for feature in self.config.features} elif self.config.name=="pairs": features = {feature: datasets.Value("int32") 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, "att_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, "att_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)): #print(row) if self.config.name in ["source", "target"]: if split in ["ent_ids","rel_ids"]: row = data[i].strip('\n').split('\t') yield i, { "column1": row[0], "column2": row[1], "column3": None } elif split in ["rel_triples"]: row = data[i].strip('\n').split('\t') yield i, { "column1": row[0], "column2": row[1], "column3": row[2] } else: row = data[i].strip('\n').split(' ') yield i, { "column1": row[0], "column2": row[1], "column3": row[2] } if self.config.name == "pairs": row = data[i].strip('\n').split('\t') yield i, { "left_id": row[0], "right_id": row[1] }