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from collections import defaultdict
from dataclasses import dataclass
from enum import Enum
import datasets
from datasets.utils.download_manager import DownloadManager
from datasets.utils.metadata import known_language_codes
from types import SimpleNamespace
from .licenses import Licenses
BigBioValues = SimpleNamespace(NULL="<BB_NULL_STR>")
@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
# shamelessly compied from:
# https://github.com/huggingface/datasets/blob/master/src/datasets/utils/metadata.py
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"),
}
],
}
],
}
)
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