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