from dataclasses import dataclass from enum import Enum import datasets from datasets.utils.metadata import known_language_codes from types import SimpleNamespace BigBioValues = SimpleNamespace(NULL="") @dataclass class BigBioConfig(datasets.BuilderConfig): """BuilderConfig for BigBio.""" name: str = None version: datasets.Version = None description: str = None schema: str = None subset_id: str = None langs_dict = {k.replace("-", "_").upper(): v for k, v in known_language_codes.items()} Lang = Enum("Lang", langs_dict) class Tasks(Enum): NAMED_ENTITY_RECOGNITION = "NER" NAMED_ENTITY_DISAMBIGUATION = "NED" EVENT_EXTRACTION = "EE" RELATION_EXTRACTION = "RE" COREFERENCE_RESOLUTION = "COREF" QUESTION_ANSWERING = "QA" TEXTUAL_ENTAILMENT = "TE" SEMANTIC_SIMILARITY = "STS" TEXT_PAIRS_CLASSIFICATION = "TXT2CLASS" PARAPHRASING = "PARA" TRANSLATION = "TRANSL" SUMMARIZATION = "SUM" TEXT_CLASSIFICATION = "TXTCLASS" entailment_features = datasets.Features( { "id": datasets.Value("string"), "premise": datasets.Value("string"), "hypothesis": datasets.Value("string"), "label": datasets.Value("string"), } ) pairs_features = datasets.Features( { "id": datasets.Value("string"), "document_id": datasets.Value("string"), "text_1": datasets.Value("string"), "text_2": datasets.Value("string"), "label": datasets.Value("string"), } ) qa_features = datasets.Features( { "id": datasets.Value("string"), "question_id": datasets.Value("string"), "document_id": datasets.Value("string"), "question": datasets.Value("string"), "type": datasets.Value("string"), "choices": [datasets.Value("string")], "context": datasets.Value("string"), "answer": datasets.Sequence(datasets.Value("string")), } ) text_features = datasets.Features( { "id": datasets.Value("string"), "document_id": datasets.Value("string"), "text": datasets.Value("string"), "labels": [datasets.Value("string")], } ) text2text_features = datasets.Features( { "id": datasets.Value("string"), "document_id": datasets.Value("string"), "text_1": datasets.Value("string"), "text_2": datasets.Value("string"), "text_1_name": datasets.Value("string"), "text_2_name": datasets.Value("string"), } ) kb_features = datasets.Features( { "id": datasets.Value("string"), "document_id": datasets.Value("string"), "passages": [ { "id": datasets.Value("string"), "type": datasets.Value("string"), "text": datasets.Sequence(datasets.Value("string")), "offsets": datasets.Sequence([datasets.Value("int32")]), } ], "entities": [ { "id": datasets.Value("string"), "type": datasets.Value("string"), "text": datasets.Sequence(datasets.Value("string")), "offsets": datasets.Sequence([datasets.Value("int32")]), "normalized": [ { "db_name": datasets.Value("string"), "db_id": datasets.Value("string"), } ], } ], "events": [ { "id": datasets.Value("string"), "type": datasets.Value("string"), # refers to the text_bound_annotation of the trigger "trigger": { "text": datasets.Sequence(datasets.Value("string")), "offsets": datasets.Sequence([datasets.Value("int32")]), }, "arguments": [ { "role": datasets.Value("string"), "ref_id": datasets.Value("string"), } ], } ], "coreferences": [ { "id": datasets.Value("string"), "entity_ids": datasets.Sequence(datasets.Value("string")), } ], "relations": [ { "id": datasets.Value("string"), "type": datasets.Value("string"), "arg1_id": datasets.Value("string"), "arg2_id": datasets.Value("string"), "normalized": [ { "db_name": datasets.Value("string"), "db_id": datasets.Value("string"), } ], } ], } )