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import json |
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from collections import defaultdict |
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from enum import Enum |
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from types import SimpleNamespace |
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from dataclasses import dataclass |
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import datasets |
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from . licenses import License, Licenses |
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BigBioValues = SimpleNamespace(NULL="<BB_NULL_STR>") |
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@dataclass |
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class BigBioConfig(datasets.BuilderConfig): |
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"""BuilderConfig for BigBio.""" |
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name: str = None |
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version: datasets.Version = None |
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description: str = None |
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schema: str = None |
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subset_id: str = None |
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langs_json = json.load(open("languages.json", "r")) |
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langs_dict = {k.replace("-", "_").upper(): v for k, v in langs_json.items()} |
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Lang = Enum("Lang", langs_dict) |
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METADATA: dict = { |
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"_LOCAL": bool, |
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"_LANGUAGES": Lang, |
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"_PUBMED": bool, |
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"_LICENSE": License, |
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"_DISPLAYNAME": str, |
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} |
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class Tasks(Enum): |
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NAMED_ENTITY_RECOGNITION = "NER" |
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NAMED_ENTITY_DISAMBIGUATION = "NED" |
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EVENT_EXTRACTION = "EE" |
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RELATION_EXTRACTION = "RE" |
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COREFERENCE_RESOLUTION = "COREF" |
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QUESTION_ANSWERING = "QA" |
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TEXTUAL_ENTAILMENT = "TE" |
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SEMANTIC_SIMILARITY = "STS" |
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TEXT_PAIRS_CLASSIFICATION = "TXT2CLASS" |
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PARAPHRASING = "PARA" |
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TRANSLATION = "TRANSL" |
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SUMMARIZATION = "SUM" |
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TEXT_CLASSIFICATION = "TXTCLASS" |
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TASK_TO_SCHEMA = { |
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Tasks.NAMED_ENTITY_RECOGNITION: "KB", |
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Tasks.NAMED_ENTITY_DISAMBIGUATION: "KB", |
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Tasks.EVENT_EXTRACTION: "KB", |
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Tasks.RELATION_EXTRACTION: "KB", |
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Tasks.COREFERENCE_RESOLUTION: "KB", |
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Tasks.QUESTION_ANSWERING: "QA", |
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Tasks.TEXTUAL_ENTAILMENT: "TE", |
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Tasks.SEMANTIC_SIMILARITY: "PAIRS", |
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Tasks.TEXT_PAIRS_CLASSIFICATION: "PAIRS", |
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Tasks.PARAPHRASING: "T2T", |
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Tasks.TRANSLATION: "T2T", |
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Tasks.SUMMARIZATION: "T2T", |
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Tasks.TEXT_CLASSIFICATION: "TEXT", |
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} |
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SCHEMA_TO_TASKS = defaultdict(set) |
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for task, schema in TASK_TO_SCHEMA.items(): |
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SCHEMA_TO_TASKS[schema].add(task) |
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SCHEMA_TO_TASKS = dict(SCHEMA_TO_TASKS) |
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VALID_TASKS = set(TASK_TO_SCHEMA.keys()) |
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VALID_SCHEMAS = set(TASK_TO_SCHEMA.values()) |
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entailment_features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"premise": datasets.Value("string"), |
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"hypothesis": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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} |
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) |
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pairs_features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"document_id": datasets.Value("string"), |
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"text_1": datasets.Value("string"), |
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"text_2": datasets.Value("string"), |
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"label": datasets.Value("string"), |
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} |
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) |
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qa_features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"question_id": datasets.Value("string"), |
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"document_id": datasets.Value("string"), |
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"question": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"choices": [datasets.Value("string")], |
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"context": datasets.Value("string"), |
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"answer": datasets.Sequence(datasets.Value("string")), |
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} |
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) |
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text_features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"document_id": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"labels": [datasets.Value("string")], |
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} |
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) |
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text2text_features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"document_id": datasets.Value("string"), |
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"text_1": datasets.Value("string"), |
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"text_2": datasets.Value("string"), |
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"text_1_name": datasets.Value("string"), |
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"text_2_name": datasets.Value("string"), |
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} |
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) |
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kb_features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"document_id": datasets.Value("string"), |
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"passages": [ |
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{ |
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"id": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"text": datasets.Sequence(datasets.Value("string")), |
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"offsets": datasets.Sequence([datasets.Value("int32")]), |
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} |
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], |
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"entities": [ |
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{ |
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"id": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"text": datasets.Sequence(datasets.Value("string")), |
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"offsets": datasets.Sequence([datasets.Value("int32")]), |
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"normalized": [ |
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{ |
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"db_name": datasets.Value("string"), |
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"db_id": datasets.Value("string"), |
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} |
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], |
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} |
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], |
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"events": [ |
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{ |
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"id": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"trigger": { |
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"text": datasets.Sequence(datasets.Value("string")), |
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"offsets": datasets.Sequence([datasets.Value("int32")]), |
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}, |
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"arguments": [ |
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{ |
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"role": datasets.Value("string"), |
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"ref_id": datasets.Value("string"), |
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} |
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], |
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} |
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], |
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"coreferences": [ |
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{ |
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"id": datasets.Value("string"), |
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"entity_ids": datasets.Sequence(datasets.Value("string")), |
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} |
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], |
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"relations": [ |
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{ |
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"id": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"arg1_id": datasets.Value("string"), |
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"arg2_id": datasets.Value("string"), |
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"normalized": [ |
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{ |
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"db_name": datasets.Value("string"), |
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"db_id": datasets.Value("string"), |
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} |
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], |
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} |
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], |
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} |
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) |
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SCHEMA_TO_FEATURES = { |
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"KB": kb_features, |
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"QA": qa_features, |
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"TE": entailment_features, |
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"T2T": text2text_features, |
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"TEXT": text_features, |
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"PAIRS": pairs_features, |
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} |
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