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Update parquet files
Browse files- .gitattributes +0 -54
- bigbiohub.py +0 -556
- chia.py +0 -649
- chia_bigbio_kb/chia-train.parquet +3 -0
- chia_fixed_source/chia-train.parquet +3 -0
- chia_source/chia-train.parquet +3 -0
- chia_without_scope_fixed_source/chia-train.parquet +3 -0
- chia_without_scope_source/chia-train.parquet +3 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ckpt filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pkl filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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# Audio files - uncompressed
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*.pcm filter=lfs diff=lfs merge=lfs -text
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*.sam filter=lfs diff=lfs merge=lfs -text
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*.raw filter=lfs diff=lfs merge=lfs -text
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# Audio files - compressed
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*.aac filter=lfs diff=lfs merge=lfs -text
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# Image files - compressed
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bigbiohub.py
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from collections import defaultdict
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from dataclasses import dataclass
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from enum import Enum
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import logging
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from pathlib import Path
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from types import SimpleNamespace
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from typing import TYPE_CHECKING, Dict, Iterable, List, Tuple
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import datasets
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if TYPE_CHECKING:
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import bioc
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logger = logging.getLogger(__name__)
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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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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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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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# refers to the text_bound_annotation of the trigger
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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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def get_texts_and_offsets_from_bioc_ann(ann: "bioc.BioCAnnotation") -> Tuple:
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offsets = [(loc.offset, loc.offset + loc.length) for loc in ann.locations]
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text = ann.text
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if len(offsets) > 1:
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i = 0
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texts = []
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for start, end in offsets:
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chunk_len = end - start
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texts.append(text[i : chunk_len + i])
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i += chunk_len
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while i < len(text) and text[i] == " ":
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i += 1
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else:
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texts = [text]
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return offsets, texts
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def remove_prefix(a: str, prefix: str) -> str:
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if a.startswith(prefix):
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a = a[len(prefix) :]
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return a
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def parse_brat_file(
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txt_file: Path,
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annotation_file_suffixes: List[str] = None,
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parse_notes: bool = False,
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) -> Dict:
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"""
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Parse a brat file into the schema defined below.
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`txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt'
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Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files,
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e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'.
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Will include annotator notes, when `parse_notes == True`.
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brat_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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"text_bound_annotations": [ # T line in brat, e.g. type or event trigger
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{
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"offsets": datasets.Sequence([datasets.Value("int32")]),
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"text": datasets.Sequence(datasets.Value("string")),
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"type": datasets.Value("string"),
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"id": datasets.Value("string"),
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}
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],
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"events": [ # E line in brat
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{
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"trigger": datasets.Value(
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"string"
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), # refers to the text_bound_annotation of the trigger,
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"id": datasets.Value("string"),
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"type": datasets.Value("string"),
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"arguments": datasets.Sequence(
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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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"relations": [ # R line in brat
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{
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"id": datasets.Value("string"),
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"head": {
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"ref_id": datasets.Value("string"),
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"role": datasets.Value("string"),
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},
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"tail": {
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"ref_id": datasets.Value("string"),
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"role": datasets.Value("string"),
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},
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"type": datasets.Value("string"),
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}
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],
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"equivalences": [ # Equiv line in brat
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{
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"id": datasets.Value("string"),
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"ref_ids": datasets.Sequence(datasets.Value("string")),
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}
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],
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"attributes": [ # M or A lines in brat
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{
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"id": datasets.Value("string"),
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"type": datasets.Value("string"),
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"ref_id": datasets.Value("string"),
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"value": datasets.Value("string"),
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}
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],
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"normalizations": [ # N lines in brat
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{
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"id": datasets.Value("string"),
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"type": datasets.Value("string"),
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"ref_id": datasets.Value("string"),
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"resource_name": datasets.Value(
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"string"
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), # Name of the resource, e.g. "Wikipedia"
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"cuid": datasets.Value(
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"string"
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), # ID in the resource, e.g. 534366
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"text": datasets.Value(
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"string"
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), # Human readable description/name of the entity, e.g. "Barack Obama"
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}
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],
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### OPTIONAL: Only included when `parse_notes == True`
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"notes": [ # # lines in brat
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{
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"id": datasets.Value("string"),
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"type": datasets.Value("string"),
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"ref_id": datasets.Value("string"),
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"text": 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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example = {}
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example["document_id"] = txt_file.with_suffix("").name
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with txt_file.open() as f:
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example["text"] = f.read()
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# If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
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# for event extraction
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if annotation_file_suffixes is None:
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annotation_file_suffixes = [".a1", ".a2", ".ann"]
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if len(annotation_file_suffixes) == 0:
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raise AssertionError(
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"At least one suffix for the to-be-read annotation files should be given!"
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)
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ann_lines = []
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for suffix in annotation_file_suffixes:
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annotation_file = txt_file.with_suffix(suffix)
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if annotation_file.exists():
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with annotation_file.open() as f:
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ann_lines.extend(f.readlines())
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example["text_bound_annotations"] = []
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example["events"] = []
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example["relations"] = []
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example["equivalences"] = []
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example["attributes"] = []
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example["normalizations"] = []
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if parse_notes:
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example["notes"] = []
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for line in ann_lines:
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line = line.strip()
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if not line:
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continue
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if line.startswith("T"): # Text bound
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ann = {}
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fields = line.split("\t")
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330 |
-
ann["id"] = fields[0]
|
331 |
-
ann["type"] = fields[1].split()[0]
|
332 |
-
ann["offsets"] = []
|
333 |
-
span_str = remove_prefix(fields[1], (ann["type"] + " "))
|
334 |
-
text = fields[2]
|
335 |
-
for span in span_str.split(";"):
|
336 |
-
start, end = span.split()
|
337 |
-
ann["offsets"].append([int(start), int(end)])
|
338 |
-
|
339 |
-
# Heuristically split text of discontiguous entities into chunks
|
340 |
-
ann["text"] = []
|
341 |
-
if len(ann["offsets"]) > 1:
|
342 |
-
i = 0
|
343 |
-
for start, end in ann["offsets"]:
|
344 |
-
chunk_len = end - start
|
345 |
-
ann["text"].append(text[i : chunk_len + i])
|
346 |
-
i += chunk_len
|
347 |
-
while i < len(text) and text[i] == " ":
|
348 |
-
i += 1
|
349 |
-
else:
|
350 |
-
ann["text"] = [text]
|
351 |
-
|
352 |
-
example["text_bound_annotations"].append(ann)
|
353 |
-
|
354 |
-
elif line.startswith("E"):
|
355 |
-
ann = {}
|
356 |
-
fields = line.split("\t")
|
357 |
-
|
358 |
-
ann["id"] = fields[0]
|
359 |
-
|
360 |
-
ann["type"], ann["trigger"] = fields[1].split()[0].split(":")
|
361 |
-
|
362 |
-
ann["arguments"] = []
|
363 |
-
for role_ref_id in fields[1].split()[1:]:
|
364 |
-
argument = {
|
365 |
-
"role": (role_ref_id.split(":"))[0],
|
366 |
-
"ref_id": (role_ref_id.split(":"))[1],
|
367 |
-
}
|
368 |
-
ann["arguments"].append(argument)
|
369 |
-
|
370 |
-
example["events"].append(ann)
|
371 |
-
|
372 |
-
elif line.startswith("R"):
|
373 |
-
ann = {}
|
374 |
-
fields = line.split("\t")
|
375 |
-
|
376 |
-
ann["id"] = fields[0]
|
377 |
-
ann["type"] = fields[1].split()[0]
|
378 |
-
|
379 |
-
ann["head"] = {
|
380 |
-
"role": fields[1].split()[1].split(":")[0],
|
381 |
-
"ref_id": fields[1].split()[1].split(":")[1],
|
382 |
-
}
|
383 |
-
ann["tail"] = {
|
384 |
-
"role": fields[1].split()[2].split(":")[0],
|
385 |
-
"ref_id": fields[1].split()[2].split(":")[1],
|
386 |
-
}
|
387 |
-
|
388 |
-
example["relations"].append(ann)
|
389 |
-
|
390 |
-
# '*' seems to be the legacy way to mark equivalences,
|
391 |
-
# but I couldn't find any info on the current way
|
392 |
-
# this might have to be adapted dependent on the brat version
|
393 |
-
# of the annotation
|
394 |
-
elif line.startswith("*"):
|
395 |
-
ann = {}
|
396 |
-
fields = line.split("\t")
|
397 |
-
|
398 |
-
ann["id"] = fields[0]
|
399 |
-
ann["ref_ids"] = fields[1].split()[1:]
|
400 |
-
|
401 |
-
example["equivalences"].append(ann)
|
402 |
-
|
403 |
-
elif line.startswith("A") or line.startswith("M"):
|
404 |
-
ann = {}
|
405 |
-
fields = line.split("\t")
|
406 |
-
|
407 |
-
ann["id"] = fields[0]
|
408 |
-
|
409 |
-
info = fields[1].split()
|
410 |
-
ann["type"] = info[0]
|
411 |
-
ann["ref_id"] = info[1]
|
412 |
-
|
413 |
-
if len(info) > 2:
|
414 |
-
ann["value"] = info[2]
|
415 |
-
else:
|
416 |
-
ann["value"] = ""
|
417 |
-
|
418 |
-
example["attributes"].append(ann)
|
419 |
-
|
420 |
-
elif line.startswith("N"):
|
421 |
-
ann = {}
|
422 |
-
fields = line.split("\t")
|
423 |
-
|
424 |
-
ann["id"] = fields[0]
|
425 |
-
ann["text"] = fields[2]
|
426 |
-
|
427 |
-
info = fields[1].split()
|
428 |
-
|
429 |
-
ann["type"] = info[0]
|
430 |
-
ann["ref_id"] = info[1]
|
431 |
-
ann["resource_name"] = info[2].split(":")[0]
|
432 |
-
ann["cuid"] = info[2].split(":")[1]
|
433 |
-
example["normalizations"].append(ann)
|
434 |
-
|
435 |
-
elif parse_notes and line.startswith("#"):
|
436 |
-
ann = {}
|
437 |
-
fields = line.split("\t")
|
438 |
-
|
439 |
-
ann["id"] = fields[0]
|
440 |
-
ann["text"] = fields[2] if len(fields) == 3 else BigBioValues.NULL
|
441 |
-
|
442 |
-
info = fields[1].split()
|
443 |
-
|
444 |
-
ann["type"] = info[0]
|
445 |
-
ann["ref_id"] = info[1]
|
446 |
-
example["notes"].append(ann)
|
447 |
-
|
448 |
-
return example
|
449 |
-
|
450 |
-
|
451 |
-
def brat_parse_to_bigbio_kb(brat_parse: Dict) -> Dict:
|
452 |
-
"""
|
453 |
-
Transform a brat parse (conforming to the standard brat schema) obtained with
|
454 |
-
`parse_brat_file` into a dictionary conforming to the `bigbio-kb` schema (as defined in ../schemas/kb.py)
|
455 |
-
:param brat_parse:
|
456 |
-
"""
|
457 |
-
|
458 |
-
unified_example = {}
|
459 |
-
|
460 |
-
# Prefix all ids with document id to ensure global uniqueness,
|
461 |
-
# because brat ids are only unique within their document
|
462 |
-
id_prefix = brat_parse["document_id"] + "_"
|
463 |
-
|
464 |
-
# identical
|
465 |
-
unified_example["document_id"] = brat_parse["document_id"]
|
466 |
-
unified_example["passages"] = [
|
467 |
-
{
|
468 |
-
"id": id_prefix + "_text",
|
469 |
-
"type": "abstract",
|
470 |
-
"text": [brat_parse["text"]],
|
471 |
-
"offsets": [[0, len(brat_parse["text"])]],
|
472 |
-
}
|
473 |
-
]
|
474 |
-
|
475 |
-
# get normalizations
|
476 |
-
ref_id_to_normalizations = defaultdict(list)
|
477 |
-
for normalization in brat_parse["normalizations"]:
|
478 |
-
ref_id_to_normalizations[normalization["ref_id"]].append(
|
479 |
-
{
|
480 |
-
"db_name": normalization["resource_name"],
|
481 |
-
"db_id": normalization["cuid"],
|
482 |
-
}
|
483 |
-
)
|
484 |
-
|
485 |
-
# separate entities and event triggers
|
486 |
-
unified_example["events"] = []
|
487 |
-
non_event_ann = brat_parse["text_bound_annotations"].copy()
|
488 |
-
for event in brat_parse["events"]:
|
489 |
-
event = event.copy()
|
490 |
-
event["id"] = id_prefix + event["id"]
|
491 |
-
trigger = next(
|
492 |
-
tr
|
493 |
-
for tr in brat_parse["text_bound_annotations"]
|
494 |
-
if tr["id"] == event["trigger"]
|
495 |
-
)
|
496 |
-
if trigger in non_event_ann:
|
497 |
-
non_event_ann.remove(trigger)
|
498 |
-
event["trigger"] = {
|
499 |
-
"text": trigger["text"].copy(),
|
500 |
-
"offsets": trigger["offsets"].copy(),
|
501 |
-
}
|
502 |
-
for argument in event["arguments"]:
|
503 |
-
argument["ref_id"] = id_prefix + argument["ref_id"]
|
504 |
-
|
505 |
-
unified_example["events"].append(event)
|
506 |
-
|
507 |
-
unified_example["entities"] = []
|
508 |
-
anno_ids = [ref_id["id"] for ref_id in non_event_ann]
|
509 |
-
for ann in non_event_ann:
|
510 |
-
entity_ann = ann.copy()
|
511 |
-
entity_ann["id"] = id_prefix + entity_ann["id"]
|
512 |
-
entity_ann["normalized"] = ref_id_to_normalizations[ann["id"]]
|
513 |
-
unified_example["entities"].append(entity_ann)
|
514 |
-
|
515 |
-
# massage relations
|
516 |
-
unified_example["relations"] = []
|
517 |
-
skipped_relations = set()
|
518 |
-
for ann in brat_parse["relations"]:
|
519 |
-
if (
|
520 |
-
ann["head"]["ref_id"] not in anno_ids
|
521 |
-
or ann["tail"]["ref_id"] not in anno_ids
|
522 |
-
):
|
523 |
-
skipped_relations.add(ann["id"])
|
524 |
-
continue
|
525 |
-
unified_example["relations"].append(
|
526 |
-
{
|
527 |
-
"arg1_id": id_prefix + ann["head"]["ref_id"],
|
528 |
-
"arg2_id": id_prefix + ann["tail"]["ref_id"],
|
529 |
-
"id": id_prefix + ann["id"],
|
530 |
-
"type": ann["type"],
|
531 |
-
"normalized": [],
|
532 |
-
}
|
533 |
-
)
|
534 |
-
if len(skipped_relations) > 0:
|
535 |
-
example_id = brat_parse["document_id"]
|
536 |
-
logger.info(
|
537 |
-
f"Example:{example_id}: The `bigbio_kb` schema allows `relations` only between entities."
|
538 |
-
f" Skip (for now): "
|
539 |
-
f"{list(skipped_relations)}"
|
540 |
-
)
|
541 |
-
|
542 |
-
# get coreferences
|
543 |
-
unified_example["coreferences"] = []
|
544 |
-
for i, ann in enumerate(brat_parse["equivalences"], start=1):
|
545 |
-
is_entity_cluster = True
|
546 |
-
for ref_id in ann["ref_ids"]:
|
547 |
-
if not ref_id.startswith("T"): # not textbound -> no entity
|
548 |
-
is_entity_cluster = False
|
549 |
-
elif ref_id not in anno_ids: # event trigger -> no entity
|
550 |
-
is_entity_cluster = False
|
551 |
-
if is_entity_cluster:
|
552 |
-
entity_ids = [id_prefix + i for i in ann["ref_ids"]]
|
553 |
-
unified_example["coreferences"].append(
|
554 |
-
{"id": id_prefix + str(i), "entity_ids": entity_ids}
|
555 |
-
)
|
556 |
-
return unified_example
|
|
|
|
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|
chia.py
DELETED
@@ -1,649 +0,0 @@
|
|
1 |
-
# coding=utf-8
|
2 |
-
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
|
3 |
-
#
|
4 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
-
# you may not use this file except in compliance with the License.
|
6 |
-
# You may obtain a copy of the License at
|
7 |
-
#
|
8 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
-
#
|
10 |
-
# Unless required by applicable law or agreed to in writing, software
|
11 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
-
# See the License for the specific language governing permissions and
|
14 |
-
# limitations under the License.
|
15 |
-
"""
|
16 |
-
A large annotated corpus of patient eligibility criteria extracted from 1,000
|
17 |
-
interventional, Phase IV clinical trials registered in ClinicalTrials.gov. This
|
18 |
-
dataset includes 12,409 annotated eligibility criteria, represented by 41,487
|
19 |
-
distinctive entities of 15 entity types and 25,017 relationships of 12
|
20 |
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relationship types."""
|
21 |
-
from pathlib import Path
|
22 |
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from typing import Dict, Iterator, List, Tuple
|
23 |
-
|
24 |
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import datasets
|
25 |
-
|
26 |
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from .bigbiohub import kb_features
|
27 |
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from .bigbiohub import BigBioConfig
|
28 |
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from .bigbiohub import Tasks
|
29 |
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from .bigbiohub import remove_prefix
|
30 |
-
|
31 |
-
|
32 |
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_LANGUAGES = ['English']
|
33 |
-
_PUBMED = False
|
34 |
-
_LOCAL = False
|
35 |
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_CITATION = """\
|
36 |
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@article{kury2020chia,
|
37 |
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title = {Chia, a large annotated corpus of clinical trial eligibility criteria},
|
38 |
-
author = {
|
39 |
-
Kury, Fabr{\'\\i}cio and Butler, Alex and Yuan, Chi and Fu, Li-heng and
|
40 |
-
Sun, Yingcheng and Liu, Hao and Sim, Ida and Carini, Simona and Weng,
|
41 |
-
Chunhua
|
42 |
-
},
|
43 |
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year = 2020,
|
44 |
-
journal = {Scientific data},
|
45 |
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publisher = {Nature Publishing Group},
|
46 |
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volume = 7,
|
47 |
-
number = 1,
|
48 |
-
pages = {1--11}
|
49 |
-
}
|
50 |
-
"""
|
51 |
-
|
52 |
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_DATASETNAME = "chia"
|
53 |
-
_DISPLAYNAME = "CHIA"
|
54 |
-
|
55 |
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_DESCRIPTION = """\
|
56 |
-
A large annotated corpus of patient eligibility criteria extracted from 1,000
|
57 |
-
interventional, Phase IV clinical trials registered in ClinicalTrials.gov. This
|
58 |
-
dataset includes 12,409 annotated eligibility criteria, represented by 41,487
|
59 |
-
distinctive entities of 15 entity types and 25,017 relationships of 12
|
60 |
-
relationship types.
|
61 |
-
"""
|
62 |
-
|
63 |
-
_HOMEPAGE = "https://github.com/WengLab-InformaticsResearch/CHIA"
|
64 |
-
|
65 |
-
_LICENSE = 'Creative Commons Attribution 4.0 International'
|
66 |
-
|
67 |
-
_URLS = {
|
68 |
-
_DATASETNAME: "https://figshare.com/ndownloader/files/21728850",
|
69 |
-
_DATASETNAME + "_wo_scope": "https://figshare.com/ndownloader/files/21728853",
|
70 |
-
}
|
71 |
-
|
72 |
-
_SUPPORTED_TASKS = [Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
|
73 |
-
|
74 |
-
_SOURCE_VERSION = "2.0.0"
|
75 |
-
_BIGBIO_VERSION = "1.0.0"
|
76 |
-
|
77 |
-
# For further information see appendix of the publication
|
78 |
-
_DOMAIN_ENTITY_TYPES = [
|
79 |
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"Condition",
|
80 |
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"Device",
|
81 |
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"Drug",
|
82 |
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"Measurement",
|
83 |
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"Observation",
|
84 |
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"Person",
|
85 |
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"Procedure",
|
86 |
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"Visit",
|
87 |
-
]
|
88 |
-
|
89 |
-
# For further information see appendix of the publication
|
90 |
-
_FIELD_ENTITY_TYPES = [
|
91 |
-
"Temporal",
|
92 |
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"Value",
|
93 |
-
]
|
94 |
-
|
95 |
-
# For further information see appendix of the publication
|
96 |
-
_CONSTRUCT_ENTITY_TYPES = [
|
97 |
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"Scope", # Not part of the "without scope" schema / version
|
98 |
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"Negation",
|
99 |
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"Multiplier",
|
100 |
-
"Qualifier",
|
101 |
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"Reference_point",
|
102 |
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"Mood",
|
103 |
-
]
|
104 |
-
|
105 |
-
_ALL_ENTITY_TYPES = _DOMAIN_ENTITY_TYPES + _FIELD_ENTITY_TYPES + _CONSTRUCT_ENTITY_TYPES
|
106 |
-
|
107 |
-
_RELATION_TYPES = [
|
108 |
-
"AND",
|
109 |
-
"OR",
|
110 |
-
"SUBSUMES",
|
111 |
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"HAS_NEGATION",
|
112 |
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"HAS_MULTIPLIER",
|
113 |
-
"HAS_QUALIFIER",
|
114 |
-
"HAS_VALUE",
|
115 |
-
"HAS_TEMPORAL",
|
116 |
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"HAS_INDEX",
|
117 |
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"HAS_MOOD",
|
118 |
-
"HAS_CONTEXT ",
|
119 |
-
"HAS_SCOPE", # Not part of the "without scope" schema / version
|
120 |
-
]
|
121 |
-
|
122 |
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_MAX_OFFSET_CORRECTION = 100
|
123 |
-
|
124 |
-
|
125 |
-
class ChiaDataset(datasets.GeneratorBasedBuilder):
|
126 |
-
"""
|
127 |
-
A large annotated corpus of patient eligibility criteria extracted from 1,000 interventional,
|
128 |
-
Phase IV clinical trials registered in ClinicalTrials.gov.
|
129 |
-
"""
|
130 |
-
|
131 |
-
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
|
132 |
-
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
|
133 |
-
|
134 |
-
BUILDER_CONFIGS = [
|
135 |
-
BigBioConfig(
|
136 |
-
name="chia_source",
|
137 |
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version=SOURCE_VERSION,
|
138 |
-
description="Chia source schema",
|
139 |
-
schema="source",
|
140 |
-
subset_id="chia",
|
141 |
-
),
|
142 |
-
BigBioConfig(
|
143 |
-
name="chia_fixed_source",
|
144 |
-
version=SOURCE_VERSION,
|
145 |
-
description="Chia source schema (with fixed entity offsets)",
|
146 |
-
schema="source",
|
147 |
-
subset_id="chia_fixed",
|
148 |
-
),
|
149 |
-
BigBioConfig(
|
150 |
-
name="chia_without_scope_source",
|
151 |
-
version=SOURCE_VERSION,
|
152 |
-
description="Chia without scope source schema",
|
153 |
-
schema="source",
|
154 |
-
subset_id="chia_without_scope",
|
155 |
-
),
|
156 |
-
BigBioConfig(
|
157 |
-
name="chia_without_scope_fixed_source",
|
158 |
-
version=SOURCE_VERSION,
|
159 |
-
description="Chia without scope source schema (with fixed entity offsets)",
|
160 |
-
schema="source",
|
161 |
-
subset_id="chia_without_scope_fixed",
|
162 |
-
),
|
163 |
-
BigBioConfig(
|
164 |
-
name="chia_bigbio_kb",
|
165 |
-
version=BIGBIO_VERSION,
|
166 |
-
description="Chia BigBio schema",
|
167 |
-
schema="bigbio_kb",
|
168 |
-
subset_id="chia",
|
169 |
-
),
|
170 |
-
]
|
171 |
-
|
172 |
-
DEFAULT_CONFIG_NAME = "chia_source"
|
173 |
-
|
174 |
-
def _info(self):
|
175 |
-
if self.config.schema == "source":
|
176 |
-
features = datasets.Features(
|
177 |
-
{
|
178 |
-
"id": datasets.Value("string"),
|
179 |
-
"document_id": datasets.Value(
|
180 |
-
"string"
|
181 |
-
), # NCT-ID from clinicialtrials.gov
|
182 |
-
"text": datasets.Value("string"),
|
183 |
-
"text_type": datasets.Value(
|
184 |
-
"string"
|
185 |
-
), # inclusion or exclusion (criteria)
|
186 |
-
"entities": [
|
187 |
-
{
|
188 |
-
"id": datasets.Value("string"),
|
189 |
-
"type": datasets.Value("string"),
|
190 |
-
"text": datasets.Sequence(datasets.Value("string")),
|
191 |
-
"offsets": datasets.Sequence([datasets.Value("int32")]),
|
192 |
-
"normalized": [
|
193 |
-
{
|
194 |
-
"db_name": datasets.Value("string"),
|
195 |
-
"db_id": datasets.Value("string"),
|
196 |
-
}
|
197 |
-
],
|
198 |
-
}
|
199 |
-
],
|
200 |
-
"relations": [
|
201 |
-
{
|
202 |
-
"id": datasets.Value("string"),
|
203 |
-
"type": datasets.Value("string"),
|
204 |
-
"arg1_id": datasets.Value("string"),
|
205 |
-
"arg2_id": datasets.Value("string"),
|
206 |
-
"normalized": [
|
207 |
-
{
|
208 |
-
"db_name": datasets.Value("string"),
|
209 |
-
"db_id": datasets.Value("string"),
|
210 |
-
}
|
211 |
-
],
|
212 |
-
}
|
213 |
-
],
|
214 |
-
}
|
215 |
-
)
|
216 |
-
|
217 |
-
elif self.config.schema == "bigbio_kb":
|
218 |
-
features = kb_features
|
219 |
-
|
220 |
-
return datasets.DatasetInfo(
|
221 |
-
description=_DESCRIPTION,
|
222 |
-
features=features,
|
223 |
-
homepage=_HOMEPAGE,
|
224 |
-
license=str(_LICENSE),
|
225 |
-
citation=_CITATION,
|
226 |
-
)
|
227 |
-
|
228 |
-
def _split_generators(self, dl_manager):
|
229 |
-
url_key = _DATASETNAME
|
230 |
-
|
231 |
-
if self.config.subset_id.startswith("chia_without_scope"):
|
232 |
-
url_key += "_wo_scope"
|
233 |
-
|
234 |
-
urls = _URLS[url_key]
|
235 |
-
data_dir = Path(dl_manager.download_and_extract(urls))
|
236 |
-
|
237 |
-
return [
|
238 |
-
datasets.SplitGenerator(
|
239 |
-
name=datasets.Split.TRAIN,
|
240 |
-
gen_kwargs={"data_dir": data_dir},
|
241 |
-
)
|
242 |
-
]
|
243 |
-
|
244 |
-
def _generate_examples(self, data_dir: Path) -> Iterator[Tuple[str, Dict]]:
|
245 |
-
if self.config.schema == "source":
|
246 |
-
fix_offsets = "fixed" in self.config.subset_id
|
247 |
-
|
248 |
-
for file in data_dir.iterdir():
|
249 |
-
if not file.name.endswith(".txt"):
|
250 |
-
continue
|
251 |
-
|
252 |
-
brat_example = parse_brat_file(file, [".ann"])
|
253 |
-
source_example = self._to_source_example(
|
254 |
-
file, brat_example, fix_offsets
|
255 |
-
)
|
256 |
-
yield source_example["id"], source_example
|
257 |
-
|
258 |
-
elif self.config.schema == "bigbio_kb":
|
259 |
-
for file in data_dir.iterdir():
|
260 |
-
if not file.name.endswith(".txt"):
|
261 |
-
continue
|
262 |
-
|
263 |
-
brat_example = parse_brat_file(file, [".ann"])
|
264 |
-
source_example = self._to_source_example(file, brat_example, True)
|
265 |
-
|
266 |
-
bigbio_example = {
|
267 |
-
"id": source_example["id"],
|
268 |
-
"document_id": source_example["document_id"],
|
269 |
-
"passages": [
|
270 |
-
{
|
271 |
-
"id": source_example["id"] + "_text",
|
272 |
-
"type": source_example["text_type"],
|
273 |
-
"text": [source_example["text"]],
|
274 |
-
"offsets": [[0, len(source_example["text"])]],
|
275 |
-
}
|
276 |
-
],
|
277 |
-
"entities": source_example["entities"],
|
278 |
-
"relations": source_example["relations"],
|
279 |
-
"events": [],
|
280 |
-
"coreferences": [],
|
281 |
-
}
|
282 |
-
|
283 |
-
yield bigbio_example["id"], bigbio_example
|
284 |
-
|
285 |
-
def _to_source_example(
|
286 |
-
self, input_file: Path, brat_example: Dict, fix_offsets: bool
|
287 |
-
) -> Dict:
|
288 |
-
"""
|
289 |
-
Converts the generic brat example to the source schema format.
|
290 |
-
"""
|
291 |
-
example_id = str(input_file.stem)
|
292 |
-
document_id = example_id.split("_")[0]
|
293 |
-
criteria_type = "inclusion" if "_inc" in input_file.stem else "exclusion"
|
294 |
-
|
295 |
-
text = brat_example["text"]
|
296 |
-
|
297 |
-
source_example = {
|
298 |
-
"id": example_id,
|
299 |
-
"document_id": document_id,
|
300 |
-
"text_type": criteria_type,
|
301 |
-
"text": text,
|
302 |
-
"entities": [],
|
303 |
-
"relations": [],
|
304 |
-
}
|
305 |
-
|
306 |
-
example_prefix = example_id + "_"
|
307 |
-
entity_ids = {}
|
308 |
-
|
309 |
-
for tb_annotation in brat_example["text_bound_annotations"]:
|
310 |
-
if tb_annotation["type"].capitalize() not in _ALL_ENTITY_TYPES:
|
311 |
-
continue
|
312 |
-
|
313 |
-
entity_ann = tb_annotation.copy()
|
314 |
-
entity_ann["id"] = example_prefix + entity_ann["id"]
|
315 |
-
entity_ids[entity_ann["id"]] = True
|
316 |
-
|
317 |
-
if fix_offsets:
|
318 |
-
if len(entity_ann["offsets"]) > 1:
|
319 |
-
entity_ann["text"] = self._get_texts_for_multiple_offsets(
|
320 |
-
text, entity_ann["offsets"]
|
321 |
-
)
|
322 |
-
|
323 |
-
fixed_offsets = []
|
324 |
-
fixed_texts = []
|
325 |
-
for entity_text, offsets in zip(
|
326 |
-
entity_ann["text"], entity_ann["offsets"]
|
327 |
-
):
|
328 |
-
fixed_offset = self._fix_entity_offsets(text, entity_text, offsets)
|
329 |
-
fixed_offsets.append(fixed_offset)
|
330 |
-
fixed_texts.append(text[fixed_offset[0] : fixed_offset[1]])
|
331 |
-
|
332 |
-
entity_ann["offsets"] = fixed_offsets
|
333 |
-
entity_ann["text"] = fixed_texts
|
334 |
-
|
335 |
-
entity_ann["normalized"] = []
|
336 |
-
source_example["entities"].append(entity_ann)
|
337 |
-
|
338 |
-
for base_rel_annotation in brat_example["relations"]:
|
339 |
-
if base_rel_annotation["type"].upper() not in _RELATION_TYPES:
|
340 |
-
continue
|
341 |
-
|
342 |
-
head_id = example_prefix + base_rel_annotation["head"]["ref_id"]
|
343 |
-
tail_id = example_prefix + base_rel_annotation["tail"]["ref_id"]
|
344 |
-
|
345 |
-
if head_id not in entity_ids or tail_id not in entity_ids:
|
346 |
-
continue
|
347 |
-
|
348 |
-
relation = {
|
349 |
-
"id": example_prefix + base_rel_annotation["id"],
|
350 |
-
"type": base_rel_annotation["type"],
|
351 |
-
"arg1_id": head_id,
|
352 |
-
"arg2_id": tail_id,
|
353 |
-
"normalized": [],
|
354 |
-
}
|
355 |
-
|
356 |
-
source_example["relations"].append(relation)
|
357 |
-
|
358 |
-
relation_id = len(brat_example["relations"]) + 10
|
359 |
-
for base_co_reference in brat_example["equivalences"]:
|
360 |
-
ref_ids = base_co_reference["ref_ids"]
|
361 |
-
for i, arg1 in enumerate(ref_ids[:-1]):
|
362 |
-
for arg2 in ref_ids[i + 1 :]:
|
363 |
-
if arg1 not in entity_ids or arg2 not in entity_ids:
|
364 |
-
continue
|
365 |
-
|
366 |
-
or_relation = {
|
367 |
-
"id": example_prefix + f"R{relation_id}",
|
368 |
-
"type": "OR",
|
369 |
-
"arg1_id": example_prefix + arg1,
|
370 |
-
"arg2_id": example_prefix + arg2,
|
371 |
-
"normalized": [],
|
372 |
-
}
|
373 |
-
|
374 |
-
source_example["relations"].append(or_relation)
|
375 |
-
relation_id += 1
|
376 |
-
|
377 |
-
return source_example
|
378 |
-
|
379 |
-
def _fix_entity_offsets(
|
380 |
-
self, doc_text: str, entity_text: str, given_offsets: List[int]
|
381 |
-
) -> List[int]:
|
382 |
-
"""
|
383 |
-
Fixes incorrect mention offsets by checking whether the given entity mention text can be
|
384 |
-
found to the left or right of the given offsets by considering incrementally larger shifts.
|
385 |
-
"""
|
386 |
-
left = given_offsets[0]
|
387 |
-
right = given_offsets[1]
|
388 |
-
|
389 |
-
# Some annotations contain whitespaces - we ignore them
|
390 |
-
clean_entity_text = entity_text.strip()
|
391 |
-
|
392 |
-
i = 0
|
393 |
-
while i <= _MAX_OFFSET_CORRECTION:
|
394 |
-
# Move mention window to the left
|
395 |
-
if doc_text[left - i : right - i].strip() == clean_entity_text:
|
396 |
-
return [left - i, left - i + len(clean_entity_text)]
|
397 |
-
|
398 |
-
# Move mention window to the right
|
399 |
-
elif doc_text[left + i : right + i].strip() == clean_entity_text:
|
400 |
-
return [left + i, left + i + len(clean_entity_text)]
|
401 |
-
|
402 |
-
i += 1
|
403 |
-
|
404 |
-
# We can't find any better offsets
|
405 |
-
return given_offsets
|
406 |
-
|
407 |
-
def _get_texts_for_multiple_offsets(
|
408 |
-
self, document_text: str, offsets: List[List[int]]
|
409 |
-
) -> List[str]:
|
410 |
-
"""
|
411 |
-
Extracts the single text span for a given list of offsets.
|
412 |
-
"""
|
413 |
-
texts = []
|
414 |
-
for offset in offsets:
|
415 |
-
texts.append(document_text[offset[0] : offset[1]])
|
416 |
-
return texts
|
417 |
-
|
418 |
-
|
419 |
-
def parse_brat_file(txt_file: Path, annotation_file_suffixes: List[str] = None) -> Dict:
|
420 |
-
"""
|
421 |
-
Parse a brat file into the schema defined below.
|
422 |
-
`txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt'
|
423 |
-
Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files,
|
424 |
-
e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'.
|
425 |
-
|
426 |
-
Schema of the parse:
|
427 |
-
features = datasets.Features(
|
428 |
-
{
|
429 |
-
"id": datasets.Value("string"),
|
430 |
-
"document_id": datasets.Value("string"),
|
431 |
-
"text": datasets.Value("string"),
|
432 |
-
"text_bound_annotations": [ # T line in brat, e.g. type or event trigger
|
433 |
-
{
|
434 |
-
"offsets": datasets.Sequence([datasets.Value("int32")]),
|
435 |
-
"text": datasets.Sequence(datasets.Value("string")),
|
436 |
-
"type": datasets.Value("string"),
|
437 |
-
"id": datasets.Value("string"),
|
438 |
-
}
|
439 |
-
],
|
440 |
-
"events": [ # E line in brat
|
441 |
-
{
|
442 |
-
"trigger": datasets.Value(
|
443 |
-
"string"
|
444 |
-
), # refers to the text_bound_annotation of the trigger,
|
445 |
-
"id": datasets.Value("string"),
|
446 |
-
"type": datasets.Value("string"),
|
447 |
-
"arguments": datasets.Sequence(
|
448 |
-
{
|
449 |
-
"role": datasets.Value("string"),
|
450 |
-
"ref_id": datasets.Value("string"),
|
451 |
-
}
|
452 |
-
),
|
453 |
-
}
|
454 |
-
],
|
455 |
-
"relations": [ # R line in brat
|
456 |
-
{
|
457 |
-
"id": datasets.Value("string"),
|
458 |
-
"head": {
|
459 |
-
"ref_id": datasets.Value("string"),
|
460 |
-
"role": datasets.Value("string"),
|
461 |
-
},
|
462 |
-
"tail": {
|
463 |
-
"ref_id": datasets.Value("string"),
|
464 |
-
"role": datasets.Value("string"),
|
465 |
-
},
|
466 |
-
"type": datasets.Value("string"),
|
467 |
-
}
|
468 |
-
],
|
469 |
-
"equivalences": [ # Equiv line in brat
|
470 |
-
{
|
471 |
-
"id": datasets.Value("string"),
|
472 |
-
"ref_ids": datasets.Sequence(datasets.Value("string")),
|
473 |
-
}
|
474 |
-
],
|
475 |
-
"attributes": [ # M or A lines in brat
|
476 |
-
{
|
477 |
-
"id": datasets.Value("string"),
|
478 |
-
"type": datasets.Value("string"),
|
479 |
-
"ref_id": datasets.Value("string"),
|
480 |
-
"value": datasets.Value("string"),
|
481 |
-
}
|
482 |
-
],
|
483 |
-
"normalizations": [ # N lines in brat
|
484 |
-
{
|
485 |
-
"id": datasets.Value("string"),
|
486 |
-
"type": datasets.Value("string"),
|
487 |
-
"ref_id": datasets.Value("string"),
|
488 |
-
"resource_name": datasets.Value(
|
489 |
-
"string"
|
490 |
-
), # Name of the resource, e.g. "Wikipedia"
|
491 |
-
"cuid": datasets.Value(
|
492 |
-
"string"
|
493 |
-
), # ID in the resource, e.g. 534366
|
494 |
-
"text": datasets.Value(
|
495 |
-
"string"
|
496 |
-
), # Human readable description/name of the entity, e.g. "Barack Obama"
|
497 |
-
}
|
498 |
-
],
|
499 |
-
},
|
500 |
-
)
|
501 |
-
"""
|
502 |
-
|
503 |
-
example = {}
|
504 |
-
example["document_id"] = txt_file.with_suffix("").name
|
505 |
-
with txt_file.open() as f:
|
506 |
-
example["text"] = f.read()
|
507 |
-
|
508 |
-
# If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
|
509 |
-
# for event extraction
|
510 |
-
if annotation_file_suffixes is None:
|
511 |
-
annotation_file_suffixes = [".a1", ".a2", ".ann"]
|
512 |
-
|
513 |
-
if len(annotation_file_suffixes) == 0:
|
514 |
-
raise AssertionError(
|
515 |
-
"At least one suffix for the to-be-read annotation files should be given!"
|
516 |
-
)
|
517 |
-
|
518 |
-
ann_lines = []
|
519 |
-
for suffix in annotation_file_suffixes:
|
520 |
-
annotation_file = txt_file.with_suffix(suffix)
|
521 |
-
if annotation_file.exists():
|
522 |
-
with annotation_file.open() as f:
|
523 |
-
ann_lines.extend(f.readlines())
|
524 |
-
|
525 |
-
example["text_bound_annotations"] = []
|
526 |
-
example["events"] = []
|
527 |
-
example["relations"] = []
|
528 |
-
example["equivalences"] = []
|
529 |
-
example["attributes"] = []
|
530 |
-
example["normalizations"] = []
|
531 |
-
|
532 |
-
prev_tb_annotation = None
|
533 |
-
|
534 |
-
for line in ann_lines:
|
535 |
-
orig_line = line
|
536 |
-
line = line.strip()
|
537 |
-
if not line:
|
538 |
-
continue
|
539 |
-
|
540 |
-
# If an (entity) annotation spans multiple lines, this will result in multiple
|
541 |
-
# lines also in the annotation file
|
542 |
-
if "\t" not in line and prev_tb_annotation is not None:
|
543 |
-
prev_tb_annotation["text"][0] += "\n" + orig_line[:-1]
|
544 |
-
continue
|
545 |
-
|
546 |
-
if line.startswith("T"): # Text bound
|
547 |
-
ann = {}
|
548 |
-
fields = line.split("\t")
|
549 |
-
|
550 |
-
ann["id"] = fields[0]
|
551 |
-
ann["text"] = [fields[2]]
|
552 |
-
ann["type"] = fields[1].split()[0]
|
553 |
-
ann["offsets"] = []
|
554 |
-
span_str = remove_prefix(fields[1], (ann["type"] + " "))
|
555 |
-
for span in span_str.split(";"):
|
556 |
-
start, end = span.split()
|
557 |
-
ann["offsets"].append([int(start), int(end)])
|
558 |
-
|
559 |
-
example["text_bound_annotations"].append(ann)
|
560 |
-
prev_tb_annotation = ann
|
561 |
-
|
562 |
-
elif line.startswith("E"):
|
563 |
-
ann = {}
|
564 |
-
fields = line.split("\t")
|
565 |
-
|
566 |
-
ann["id"] = fields[0]
|
567 |
-
|
568 |
-
ann["type"], ann["trigger"] = fields[1].split()[0].split(":")
|
569 |
-
|
570 |
-
ann["arguments"] = []
|
571 |
-
for role_ref_id in fields[1].split()[1:]:
|
572 |
-
argument = {
|
573 |
-
"role": (role_ref_id.split(":"))[0],
|
574 |
-
"ref_id": (role_ref_id.split(":"))[1],
|
575 |
-
}
|
576 |
-
ann["arguments"].append(argument)
|
577 |
-
|
578 |
-
example["events"].append(ann)
|
579 |
-
prev_tb_annotation = None
|
580 |
-
|
581 |
-
elif line.startswith("R"):
|
582 |
-
ann = {}
|
583 |
-
fields = line.split("\t")
|
584 |
-
|
585 |
-
ann["id"] = fields[0]
|
586 |
-
ann["type"] = fields[1].split()[0]
|
587 |
-
|
588 |
-
ann["head"] = {
|
589 |
-
"role": fields[1].split()[1].split(":")[0],
|
590 |
-
"ref_id": fields[1].split()[1].split(":")[1],
|
591 |
-
}
|
592 |
-
ann["tail"] = {
|
593 |
-
"role": fields[1].split()[2].split(":")[0],
|
594 |
-
"ref_id": fields[1].split()[2].split(":")[1],
|
595 |
-
}
|
596 |
-
|
597 |
-
example["relations"].append(ann)
|
598 |
-
prev_tb_annotation = None
|
599 |
-
|
600 |
-
# '*' seems to be the legacy way to mark equivalences,
|
601 |
-
# but I couldn't find any info on the current way
|
602 |
-
# this might have to be adapted dependent on the brat version
|
603 |
-
# of the annotation
|
604 |
-
elif line.startswith("*"):
|
605 |
-
ann = {}
|
606 |
-
fields = line.split("\t")
|
607 |
-
|
608 |
-
ann["id"] = fields[0]
|
609 |
-
ann["ref_ids"] = fields[1].split()[1:]
|
610 |
-
|
611 |
-
example["equivalences"].append(ann)
|
612 |
-
prev_tb_annotation = None
|
613 |
-
|
614 |
-
elif line.startswith("A") or line.startswith("M"):
|
615 |
-
ann = {}
|
616 |
-
fields = line.split("\t")
|
617 |
-
|
618 |
-
ann["id"] = fields[0]
|
619 |
-
|
620 |
-
info = fields[1].split()
|
621 |
-
ann["type"] = info[0]
|
622 |
-
ann["ref_id"] = info[1]
|
623 |
-
|
624 |
-
if len(info) > 2:
|
625 |
-
ann["value"] = info[2]
|
626 |
-
else:
|
627 |
-
ann["value"] = ""
|
628 |
-
|
629 |
-
example["attributes"].append(ann)
|
630 |
-
prev_tb_annotation = None
|
631 |
-
|
632 |
-
elif line.startswith("N"):
|
633 |
-
ann = {}
|
634 |
-
fields = line.split("\t")
|
635 |
-
|
636 |
-
ann["id"] = fields[0]
|
637 |
-
ann["text"] = fields[2]
|
638 |
-
|
639 |
-
info = fields[1].split()
|
640 |
-
|
641 |
-
ann["type"] = info[0]
|
642 |
-
ann["ref_id"] = info[1]
|
643 |
-
ann["resource_name"] = info[2].split(":")[0]
|
644 |
-
ann["cuid"] = info[2].split(":")[1]
|
645 |
-
|
646 |
-
example["normalizations"].append(ann)
|
647 |
-
prev_tb_annotation = None
|
648 |
-
|
649 |
-
return example
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
chia_bigbio_kb/chia-train.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3a417dc3d8812440f53bd17eed23f77ee74583520a8da760830e721c3635259b
|
3 |
+
size 2083711
|
chia_fixed_source/chia-train.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:c232d9360ef03b97c6bfb24d64ef355cfbec46483b3db30b91ac18fa71dc260b
|
3 |
+
size 2049327
|
chia_source/chia-train.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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chia_without_scope_fixed_source/chia-train.parquet
ADDED
@@ -0,0 +1,3 @@
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|
|
|
|
|
|
|
|
1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:74d9425fc0d1be2c931b06c909089107de02f52890d18cee4c34a8415634a254
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size 1905007
|
chia_without_scope_source/chia-train.parquet
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:5070d9d8eb2aa1838e213e98595a2d4e6e3dbba93b06cb9a7f6df94c864d95e2
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3 |
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size 1893793
|