# Some code referenced from https://huggingface.co/datasets/Babelscape/SREDFM/blob/main/SREDFM.py from pathlib import Path from typing import Dict, List, Tuple import datasets import jsonlines from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks _CITATION = """\ @inproceedings{huguet-cabot-et-al-2023-redfm-dataset, title = "RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset", author = "Huguet Cabot, Pere-LluĂ­s and Tedeschi, Simone and Ngonga Ngomo, Axel-Cyrille and Navigli, Roberto", booktitle = "Proc. of the 61st Annual Meeting of the Association for Computational Linguistics: ACL 2023", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2306.09802", } """ _DATASETNAME = "sredfm" _DESCRIPTION = """\ SREDFM is an automatically annotated dataset for relation extraction task covering 18 languages, 400 relation types, 13 entity types, totaling more than 40 million triplet instances. SREDFM includes Vietnamnese. """ _HOMEPAGE = "https://github.com/babelscape/rebel" _LANGUAGES = ["vie"] _LICENSE = Licenses.CC_BY_SA_4_0.value _LOCAL = False _URLS = { "train": "https://huggingface.co/datasets/Babelscape/SREDFM/resolve/main/data/train.vi.jsonl", "dev": "https://huggingface.co/datasets/Babelscape/SREDFM/resolve/main/data/dev.vi.jsonl", "test": "https://huggingface.co/datasets/Babelscape/SREDFM/resolve/main/data/test.vi.jsonl", "relations_url": "https://huggingface.co/datasets/Babelscape/SREDFM/raw/main/relations.tsv", } _SUPPORTED_TASKS = [Tasks.RELATION_EXTRACTION] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class SREDFMDataset(datasets.GeneratorBasedBuilder): """SREDFM is an automatically annotated dataset for relation extraction task. Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured knowledge. SREDFM covers 400 relation types, 13 entity types, totaling more than 40 million triplet instances.""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) BUILDER_CONFIGS = [ SEACrowdConfig( name=f"{_DATASETNAME}_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} source schema", schema="source", subset_id=f"{_DATASETNAME}", ), SEACrowdConfig( name=f"{_DATASETNAME}_seacrowd_kb", version=SEACROWD_VERSION, description=f"{_DATASETNAME} SEACrowd schema", schema="seacrowd_kb", subset_id=f"{_DATASETNAME}", ), ] DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "docid": datasets.Value("string"), "title": datasets.Value("string"), "uri": datasets.Value("string"), "text": datasets.Value("string"), "entities": [ { "uri": datasets.Value(dtype="string"), "surfaceform": datasets.Value(dtype="string"), "type": datasets.Value(dtype="string"), "start": datasets.Value(dtype="int32"), "end": datasets.Value(dtype="int32"), } ], "relations": [ { "subject": datasets.Value(dtype="int32"), "predicate": datasets.Value(dtype="string"), "object": datasets.Value(dtype="int32"), } ], } ) elif self.config.schema == "seacrowd_kb": features = schemas.kb_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.""" data_dir = dl_manager.download_and_extract(_URLS) relation_names = dict() relation_path = data_dir["relations_url"] with open(relation_path, encoding="utf-8") as f: for row in f: rel_code, rel_name, _, _ = row.strip().split("\t") relation_names[rel_code] = rel_name return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir["train"], "relation_names": relation_names}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir["test"], "relation_names": relation_names}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir["dev"], "relation_names": relation_names}, ), ] def _generate_examples(self, filepath: Path, relation_names: dict) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" if self.config.schema == "source": with jsonlines.open(filepath) as f: skip = set() for example in f.iter(): if example["docid"] in skip: continue skip.add(example["docid"]) entities = [] for entity in example["entities"]: entities.append( { "uri": entity["uri"], "surfaceform": entity["surfaceform"], "start": entity["boundaries"][0], "end": entity["boundaries"][1], "type": entity["type"], } ) relations = [] for relation in example["relations"]: if relation["predicate"]["uri"] not in relation_names or relation["confidence"] <= 0.75: continue relations.append( { "subject": entities.index( { "uri": relation["subject"]["uri"], "surfaceform": relation["subject"]["surfaceform"], "start": relation["subject"]["boundaries"][0], "end": relation["subject"]["boundaries"][1], "type": relation["subject"]["type"], } ), "predicate": relation_names[relation["predicate"]["uri"]], "object": entities.index( { "uri": relation["object"]["uri"], "surfaceform": relation["object"]["surfaceform"], "start": relation["object"]["boundaries"][0], "end": relation["object"]["boundaries"][1], "type": relation["object"]["type"], } ), } ) if len(relations) == 0: continue yield example["docid"], { "docid": example["docid"], "title": example["title"], "uri": example["uri"], "text": example["text"], "entities": entities, "relations": relations, } elif self.config.schema == "seacrowd_kb": with jsonlines.open(filepath) as f: skip = set() i = 0 for example in f.iter(): if example["docid"] in skip: continue skip.add(example["docid"]) i += 1 processed_text = example["text"].replace("\n", " ") passages = [ { "id": f"{i}-{example['uri']}", "type": "text", "text": [processed_text], "offsets": [[0, len(processed_text)]], } ] entities = [] for entity in example["entities"]: entities.append( { "id": entity["uri"], "type": entity["type"], "text": [entity["surfaceform"]], "offsets": [entity["boundaries"]], "normalized": {"db_name": "", "db_id": ""}, } ) relations = [] for relation in example["relations"]: if relation["predicate"]["uri"] not in relation_names or relation["confidence"] <= 0.75: continue i += 1 sub = relation["subject"] pred = relation["predicate"] obj = relation["object"] relations.append( { "id": f"{i}-{sub['uri']}-{pred['uri']}-{obj['uri']}", "type": relation_names[pred["uri"]], "arg1_id": str( entities.index( { "id": sub["uri"], "type": sub["type"], "text": [sub["surfaceform"]], "offsets": [sub["boundaries"]], "normalized": {"db_name": "", "db_id": ""}, } ) ), "arg2_id": str( entities.index( { "id": obj["uri"], "type": obj["type"], "text": [obj["surfaceform"]], "offsets": [obj["boundaries"]], "normalized": {"db_name": "", "db_id": ""}, } ) ), "normalized": {"db_name": "", "db_id": ""}, } ) for entity in entities: i += 1 entity["id"] = f"{i}-{entity['id']}" if len(relations) == 0: continue yield example["docid"], { "id": example["docid"], "passages": passages, "entities": entities, "relations": relations, "events": [], "coreferences": [], }