Update head_qa based on git version 251a069
Browse files- README.md +50 -0
- bigbiohub.py +590 -0
- head_qa.py +230 -0
README.md
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---
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language:
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- es
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- en
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bigbio_language:
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- English
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- Spanish
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license: mit
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bigbio_license_shortname: MIT
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multilinguality: multilingual
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pretty_name: HEAD-QA
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homepage: https://aghie.github.io/head-qa/
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bigbio_pubmed: false
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bigbio_public: true
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bigbio_tasks:
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- QUESTION_ANSWERING
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---
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# Dataset Card for HEAD-QA
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## Dataset Description
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- **Homepage:** https://aghie.github.io/head-qa/
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- **Pubmed:** False
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- **Public:** True
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- **Tasks:** QA
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HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the
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Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the
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Ministerio de Sanidad, Consumo y Bienestar Social. The dataset contains questions about following topics: medicine,
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nursing, psychology, chemistry, pharmacology and biology.
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## Citation Information
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```
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@inproceedings{vilares-gomez-rodriguez-2019-head,
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title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
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author = "Vilares, David and G{\'o}mez-Rodr{\'i}guez, Carlos",
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booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
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month = jul,
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year = "2019",
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address = "Florence, Italy",
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publisher = "Association for Computational Linguistics",
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url = "https://www.aclweb.org/anthology/P19-1092",
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doi = "10.18653/v1/P19-1092",
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pages = "960--966"
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}
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```
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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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4 |
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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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+
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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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+
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kb_features = datasets.Features(
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{
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101 |
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"id": datasets.Value("string"),
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"document_id": datasets.Value("string"),
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103 |
+
"passages": [
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104 |
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{
|
105 |
+
"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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108 |
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"offsets": datasets.Sequence([datasets.Value("int32")]),
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109 |
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}
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],
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111 |
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"entities": [
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112 |
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{
|
113 |
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"id": datasets.Value("string"),
|
114 |
+
"type": datasets.Value("string"),
|
115 |
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"text": datasets.Sequence(datasets.Value("string")),
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116 |
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"offsets": datasets.Sequence([datasets.Value("int32")]),
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117 |
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"normalized": [
|
118 |
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{
|
119 |
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"db_name": datasets.Value("string"),
|
120 |
+
"db_id": datasets.Value("string"),
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121 |
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}
|
122 |
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],
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123 |
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}
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124 |
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],
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125 |
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"events": [
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126 |
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{
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127 |
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"id": datasets.Value("string"),
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128 |
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"type": datasets.Value("string"),
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129 |
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# refers to the text_bound_annotation of the trigger
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130 |
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"trigger": {
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131 |
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"text": datasets.Sequence(datasets.Value("string")),
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132 |
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"offsets": datasets.Sequence([datasets.Value("int32")]),
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133 |
+
},
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134 |
+
"arguments": [
|
135 |
+
{
|
136 |
+
"role": datasets.Value("string"),
|
137 |
+
"ref_id": datasets.Value("string"),
|
138 |
+
}
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139 |
+
],
|
140 |
+
}
|
141 |
+
],
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142 |
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"coreferences": [
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143 |
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{
|
144 |
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"id": datasets.Value("string"),
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145 |
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"entity_ids": datasets.Sequence(datasets.Value("string")),
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146 |
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}
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147 |
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],
|
148 |
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"relations": [
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149 |
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{
|
150 |
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"id": datasets.Value("string"),
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151 |
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"type": datasets.Value("string"),
|
152 |
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"arg1_id": datasets.Value("string"),
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153 |
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"arg2_id": datasets.Value("string"),
|
154 |
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"normalized": [
|
155 |
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{
|
156 |
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"db_name": datasets.Value("string"),
|
157 |
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"db_id": datasets.Value("string"),
|
158 |
+
}
|
159 |
+
],
|
160 |
+
}
|
161 |
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],
|
162 |
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}
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)
|
164 |
+
|
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+
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TASK_TO_SCHEMA = {
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Tasks.NAMED_ENTITY_RECOGNITION.name: "KB",
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Tasks.NAMED_ENTITY_DISAMBIGUATION.name: "KB",
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Tasks.EVENT_EXTRACTION.name: "KB",
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Tasks.RELATION_EXTRACTION.name: "KB",
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Tasks.COREFERENCE_RESOLUTION.name: "KB",
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Tasks.QUESTION_ANSWERING.name: "QA",
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Tasks.TEXTUAL_ENTAILMENT.name: "TE",
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Tasks.SEMANTIC_SIMILARITY.name: "PAIRS",
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Tasks.TEXT_PAIRS_CLASSIFICATION.name: "PAIRS",
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+
Tasks.PARAPHRASING.name: "T2T",
|
177 |
+
Tasks.TRANSLATION.name: "T2T",
|
178 |
+
Tasks.SUMMARIZATION.name: "T2T",
|
179 |
+
Tasks.TEXT_CLASSIFICATION.name: "TEXT",
|
180 |
+
}
|
181 |
+
|
182 |
+
SCHEMA_TO_TASKS = defaultdict(set)
|
183 |
+
for task, schema in TASK_TO_SCHEMA.items():
|
184 |
+
SCHEMA_TO_TASKS[schema].add(task)
|
185 |
+
SCHEMA_TO_TASKS = dict(SCHEMA_TO_TASKS)
|
186 |
+
|
187 |
+
VALID_TASKS = set(TASK_TO_SCHEMA.keys())
|
188 |
+
VALID_SCHEMAS = set(TASK_TO_SCHEMA.values())
|
189 |
+
|
190 |
+
SCHEMA_TO_FEATURES = {
|
191 |
+
"KB": kb_features,
|
192 |
+
"QA": qa_features,
|
193 |
+
"TE": entailment_features,
|
194 |
+
"T2T": text2text_features,
|
195 |
+
"TEXT": text_features,
|
196 |
+
"PAIRS": pairs_features,
|
197 |
+
}
|
198 |
+
|
199 |
+
|
200 |
+
def get_texts_and_offsets_from_bioc_ann(ann: "bioc.BioCAnnotation") -> Tuple:
|
201 |
+
|
202 |
+
offsets = [(loc.offset, loc.offset + loc.length) for loc in ann.locations]
|
203 |
+
|
204 |
+
text = ann.text
|
205 |
+
|
206 |
+
if len(offsets) > 1:
|
207 |
+
i = 0
|
208 |
+
texts = []
|
209 |
+
for start, end in offsets:
|
210 |
+
chunk_len = end - start
|
211 |
+
texts.append(text[i : chunk_len + i])
|
212 |
+
i += chunk_len
|
213 |
+
while i < len(text) and text[i] == " ":
|
214 |
+
i += 1
|
215 |
+
else:
|
216 |
+
texts = [text]
|
217 |
+
|
218 |
+
return offsets, texts
|
219 |
+
|
220 |
+
|
221 |
+
def remove_prefix(a: str, prefix: str) -> str:
|
222 |
+
if a.startswith(prefix):
|
223 |
+
a = a[len(prefix) :]
|
224 |
+
return a
|
225 |
+
|
226 |
+
|
227 |
+
def parse_brat_file(
|
228 |
+
txt_file: Path,
|
229 |
+
annotation_file_suffixes: List[str] = None,
|
230 |
+
parse_notes: bool = False,
|
231 |
+
) -> Dict:
|
232 |
+
"""
|
233 |
+
Parse a brat file into the schema defined below.
|
234 |
+
`txt_file` should be the path to the brat '.txt' file you want to parse, e.g. 'data/1234.txt'
|
235 |
+
Assumes that the annotations are contained in one or more of the corresponding '.a1', '.a2' or '.ann' files,
|
236 |
+
e.g. 'data/1234.ann' or 'data/1234.a1' and 'data/1234.a2'.
|
237 |
+
Will include annotator notes, when `parse_notes == True`.
|
238 |
+
brat_features = datasets.Features(
|
239 |
+
{
|
240 |
+
"id": datasets.Value("string"),
|
241 |
+
"document_id": datasets.Value("string"),
|
242 |
+
"text": datasets.Value("string"),
|
243 |
+
"text_bound_annotations": [ # T line in brat, e.g. type or event trigger
|
244 |
+
{
|
245 |
+
"offsets": datasets.Sequence([datasets.Value("int32")]),
|
246 |
+
"text": datasets.Sequence(datasets.Value("string")),
|
247 |
+
"type": datasets.Value("string"),
|
248 |
+
"id": datasets.Value("string"),
|
249 |
+
}
|
250 |
+
],
|
251 |
+
"events": [ # E line in brat
|
252 |
+
{
|
253 |
+
"trigger": datasets.Value(
|
254 |
+
"string"
|
255 |
+
), # refers to the text_bound_annotation of the trigger,
|
256 |
+
"id": datasets.Value("string"),
|
257 |
+
"type": datasets.Value("string"),
|
258 |
+
"arguments": datasets.Sequence(
|
259 |
+
{
|
260 |
+
"role": datasets.Value("string"),
|
261 |
+
"ref_id": datasets.Value("string"),
|
262 |
+
}
|
263 |
+
),
|
264 |
+
}
|
265 |
+
],
|
266 |
+
"relations": [ # R line in brat
|
267 |
+
{
|
268 |
+
"id": datasets.Value("string"),
|
269 |
+
"head": {
|
270 |
+
"ref_id": datasets.Value("string"),
|
271 |
+
"role": datasets.Value("string"),
|
272 |
+
},
|
273 |
+
"tail": {
|
274 |
+
"ref_id": datasets.Value("string"),
|
275 |
+
"role": datasets.Value("string"),
|
276 |
+
},
|
277 |
+
"type": datasets.Value("string"),
|
278 |
+
}
|
279 |
+
],
|
280 |
+
"equivalences": [ # Equiv line in brat
|
281 |
+
{
|
282 |
+
"id": datasets.Value("string"),
|
283 |
+
"ref_ids": datasets.Sequence(datasets.Value("string")),
|
284 |
+
}
|
285 |
+
],
|
286 |
+
"attributes": [ # M or A lines in brat
|
287 |
+
{
|
288 |
+
"id": datasets.Value("string"),
|
289 |
+
"type": datasets.Value("string"),
|
290 |
+
"ref_id": datasets.Value("string"),
|
291 |
+
"value": datasets.Value("string"),
|
292 |
+
}
|
293 |
+
],
|
294 |
+
"normalizations": [ # N lines in brat
|
295 |
+
{
|
296 |
+
"id": datasets.Value("string"),
|
297 |
+
"type": datasets.Value("string"),
|
298 |
+
"ref_id": datasets.Value("string"),
|
299 |
+
"resource_name": datasets.Value(
|
300 |
+
"string"
|
301 |
+
), # Name of the resource, e.g. "Wikipedia"
|
302 |
+
"cuid": datasets.Value(
|
303 |
+
"string"
|
304 |
+
), # ID in the resource, e.g. 534366
|
305 |
+
"text": datasets.Value(
|
306 |
+
"string"
|
307 |
+
), # Human readable description/name of the entity, e.g. "Barack Obama"
|
308 |
+
}
|
309 |
+
],
|
310 |
+
### OPTIONAL: Only included when `parse_notes == True`
|
311 |
+
"notes": [ # # lines in brat
|
312 |
+
{
|
313 |
+
"id": datasets.Value("string"),
|
314 |
+
"type": datasets.Value("string"),
|
315 |
+
"ref_id": datasets.Value("string"),
|
316 |
+
"text": datasets.Value("string"),
|
317 |
+
}
|
318 |
+
],
|
319 |
+
},
|
320 |
+
)
|
321 |
+
"""
|
322 |
+
|
323 |
+
example = {}
|
324 |
+
example["document_id"] = txt_file.with_suffix("").name
|
325 |
+
with txt_file.open() as f:
|
326 |
+
example["text"] = f.read()
|
327 |
+
|
328 |
+
# If no specific suffixes of the to-be-read annotation files are given - take standard suffixes
|
329 |
+
# for event extraction
|
330 |
+
if annotation_file_suffixes is None:
|
331 |
+
annotation_file_suffixes = [".a1", ".a2", ".ann"]
|
332 |
+
|
333 |
+
if len(annotation_file_suffixes) == 0:
|
334 |
+
raise AssertionError(
|
335 |
+
"At least one suffix for the to-be-read annotation files should be given!"
|
336 |
+
)
|
337 |
+
|
338 |
+
ann_lines = []
|
339 |
+
for suffix in annotation_file_suffixes:
|
340 |
+
annotation_file = txt_file.with_suffix(suffix)
|
341 |
+
if annotation_file.exists():
|
342 |
+
with annotation_file.open() as f:
|
343 |
+
ann_lines.extend(f.readlines())
|
344 |
+
|
345 |
+
example["text_bound_annotations"] = []
|
346 |
+
example["events"] = []
|
347 |
+
example["relations"] = []
|
348 |
+
example["equivalences"] = []
|
349 |
+
example["attributes"] = []
|
350 |
+
example["normalizations"] = []
|
351 |
+
|
352 |
+
if parse_notes:
|
353 |
+
example["notes"] = []
|
354 |
+
|
355 |
+
for line in ann_lines:
|
356 |
+
line = line.strip()
|
357 |
+
if not line:
|
358 |
+
continue
|
359 |
+
|
360 |
+
if line.startswith("T"): # Text bound
|
361 |
+
ann = {}
|
362 |
+
fields = line.split("\t")
|
363 |
+
|
364 |
+
ann["id"] = fields[0]
|
365 |
+
ann["type"] = fields[1].split()[0]
|
366 |
+
ann["offsets"] = []
|
367 |
+
span_str = remove_prefix(fields[1], (ann["type"] + " "))
|
368 |
+
text = fields[2]
|
369 |
+
for span in span_str.split(";"):
|
370 |
+
start, end = span.split()
|
371 |
+
ann["offsets"].append([int(start), int(end)])
|
372 |
+
|
373 |
+
# Heuristically split text of discontiguous entities into chunks
|
374 |
+
ann["text"] = []
|
375 |
+
if len(ann["offsets"]) > 1:
|
376 |
+
i = 0
|
377 |
+
for start, end in ann["offsets"]:
|
378 |
+
chunk_len = end - start
|
379 |
+
ann["text"].append(text[i : chunk_len + i])
|
380 |
+
i += chunk_len
|
381 |
+
while i < len(text) and text[i] == " ":
|
382 |
+
i += 1
|
383 |
+
else:
|
384 |
+
ann["text"] = [text]
|
385 |
+
|
386 |
+
example["text_bound_annotations"].append(ann)
|
387 |
+
|
388 |
+
elif line.startswith("E"):
|
389 |
+
ann = {}
|
390 |
+
fields = line.split("\t")
|
391 |
+
|
392 |
+
ann["id"] = fields[0]
|
393 |
+
|
394 |
+
ann["type"], ann["trigger"] = fields[1].split()[0].split(":")
|
395 |
+
|
396 |
+
ann["arguments"] = []
|
397 |
+
for role_ref_id in fields[1].split()[1:]:
|
398 |
+
argument = {
|
399 |
+
"role": (role_ref_id.split(":"))[0],
|
400 |
+
"ref_id": (role_ref_id.split(":"))[1],
|
401 |
+
}
|
402 |
+
ann["arguments"].append(argument)
|
403 |
+
|
404 |
+
example["events"].append(ann)
|
405 |
+
|
406 |
+
elif line.startswith("R"):
|
407 |
+
ann = {}
|
408 |
+
fields = line.split("\t")
|
409 |
+
|
410 |
+
ann["id"] = fields[0]
|
411 |
+
ann["type"] = fields[1].split()[0]
|
412 |
+
|
413 |
+
ann["head"] = {
|
414 |
+
"role": fields[1].split()[1].split(":")[0],
|
415 |
+
"ref_id": fields[1].split()[1].split(":")[1],
|
416 |
+
}
|
417 |
+
ann["tail"] = {
|
418 |
+
"role": fields[1].split()[2].split(":")[0],
|
419 |
+
"ref_id": fields[1].split()[2].split(":")[1],
|
420 |
+
}
|
421 |
+
|
422 |
+
example["relations"].append(ann)
|
423 |
+
|
424 |
+
# '*' seems to be the legacy way to mark equivalences,
|
425 |
+
# but I couldn't find any info on the current way
|
426 |
+
# this might have to be adapted dependent on the brat version
|
427 |
+
# of the annotation
|
428 |
+
elif line.startswith("*"):
|
429 |
+
ann = {}
|
430 |
+
fields = line.split("\t")
|
431 |
+
|
432 |
+
ann["id"] = fields[0]
|
433 |
+
ann["ref_ids"] = fields[1].split()[1:]
|
434 |
+
|
435 |
+
example["equivalences"].append(ann)
|
436 |
+
|
437 |
+
elif line.startswith("A") or line.startswith("M"):
|
438 |
+
ann = {}
|
439 |
+
fields = line.split("\t")
|
440 |
+
|
441 |
+
ann["id"] = fields[0]
|
442 |
+
|
443 |
+
info = fields[1].split()
|
444 |
+
ann["type"] = info[0]
|
445 |
+
ann["ref_id"] = info[1]
|
446 |
+
|
447 |
+
if len(info) > 2:
|
448 |
+
ann["value"] = info[2]
|
449 |
+
else:
|
450 |
+
ann["value"] = ""
|
451 |
+
|
452 |
+
example["attributes"].append(ann)
|
453 |
+
|
454 |
+
elif line.startswith("N"):
|
455 |
+
ann = {}
|
456 |
+
fields = line.split("\t")
|
457 |
+
|
458 |
+
ann["id"] = fields[0]
|
459 |
+
ann["text"] = fields[2]
|
460 |
+
|
461 |
+
info = fields[1].split()
|
462 |
+
|
463 |
+
ann["type"] = info[0]
|
464 |
+
ann["ref_id"] = info[1]
|
465 |
+
ann["resource_name"] = info[2].split(":")[0]
|
466 |
+
ann["cuid"] = info[2].split(":")[1]
|
467 |
+
example["normalizations"].append(ann)
|
468 |
+
|
469 |
+
elif parse_notes and line.startswith("#"):
|
470 |
+
ann = {}
|
471 |
+
fields = line.split("\t")
|
472 |
+
|
473 |
+
ann["id"] = fields[0]
|
474 |
+
ann["text"] = fields[2] if len(fields) == 3 else BigBioValues.NULL
|
475 |
+
|
476 |
+
info = fields[1].split()
|
477 |
+
|
478 |
+
ann["type"] = info[0]
|
479 |
+
ann["ref_id"] = info[1]
|
480 |
+
example["notes"].append(ann)
|
481 |
+
|
482 |
+
return example
|
483 |
+
|
484 |
+
|
485 |
+
def brat_parse_to_bigbio_kb(brat_parse: Dict) -> Dict:
|
486 |
+
"""
|
487 |
+
Transform a brat parse (conforming to the standard brat schema) obtained with
|
488 |
+
`parse_brat_file` into a dictionary conforming to the `bigbio-kb` schema (as defined in ../schemas/kb.py)
|
489 |
+
:param brat_parse:
|
490 |
+
"""
|
491 |
+
|
492 |
+
unified_example = {}
|
493 |
+
|
494 |
+
# Prefix all ids with document id to ensure global uniqueness,
|
495 |
+
# because brat ids are only unique within their document
|
496 |
+
id_prefix = brat_parse["document_id"] + "_"
|
497 |
+
|
498 |
+
# identical
|
499 |
+
unified_example["document_id"] = brat_parse["document_id"]
|
500 |
+
unified_example["passages"] = [
|
501 |
+
{
|
502 |
+
"id": id_prefix + "_text",
|
503 |
+
"type": "abstract",
|
504 |
+
"text": [brat_parse["text"]],
|
505 |
+
"offsets": [[0, len(brat_parse["text"])]],
|
506 |
+
}
|
507 |
+
]
|
508 |
+
|
509 |
+
# get normalizations
|
510 |
+
ref_id_to_normalizations = defaultdict(list)
|
511 |
+
for normalization in brat_parse["normalizations"]:
|
512 |
+
ref_id_to_normalizations[normalization["ref_id"]].append(
|
513 |
+
{
|
514 |
+
"db_name": normalization["resource_name"],
|
515 |
+
"db_id": normalization["cuid"],
|
516 |
+
}
|
517 |
+
)
|
518 |
+
|
519 |
+
# separate entities and event triggers
|
520 |
+
unified_example["events"] = []
|
521 |
+
non_event_ann = brat_parse["text_bound_annotations"].copy()
|
522 |
+
for event in brat_parse["events"]:
|
523 |
+
event = event.copy()
|
524 |
+
event["id"] = id_prefix + event["id"]
|
525 |
+
trigger = next(
|
526 |
+
tr
|
527 |
+
for tr in brat_parse["text_bound_annotations"]
|
528 |
+
if tr["id"] == event["trigger"]
|
529 |
+
)
|
530 |
+
if trigger in non_event_ann:
|
531 |
+
non_event_ann.remove(trigger)
|
532 |
+
event["trigger"] = {
|
533 |
+
"text": trigger["text"].copy(),
|
534 |
+
"offsets": trigger["offsets"].copy(),
|
535 |
+
}
|
536 |
+
for argument in event["arguments"]:
|
537 |
+
argument["ref_id"] = id_prefix + argument["ref_id"]
|
538 |
+
|
539 |
+
unified_example["events"].append(event)
|
540 |
+
|
541 |
+
unified_example["entities"] = []
|
542 |
+
anno_ids = [ref_id["id"] for ref_id in non_event_ann]
|
543 |
+
for ann in non_event_ann:
|
544 |
+
entity_ann = ann.copy()
|
545 |
+
entity_ann["id"] = id_prefix + entity_ann["id"]
|
546 |
+
entity_ann["normalized"] = ref_id_to_normalizations[ann["id"]]
|
547 |
+
unified_example["entities"].append(entity_ann)
|
548 |
+
|
549 |
+
# massage relations
|
550 |
+
unified_example["relations"] = []
|
551 |
+
skipped_relations = set()
|
552 |
+
for ann in brat_parse["relations"]:
|
553 |
+
if (
|
554 |
+
ann["head"]["ref_id"] not in anno_ids
|
555 |
+
or ann["tail"]["ref_id"] not in anno_ids
|
556 |
+
):
|
557 |
+
skipped_relations.add(ann["id"])
|
558 |
+
continue
|
559 |
+
unified_example["relations"].append(
|
560 |
+
{
|
561 |
+
"arg1_id": id_prefix + ann["head"]["ref_id"],
|
562 |
+
"arg2_id": id_prefix + ann["tail"]["ref_id"],
|
563 |
+
"id": id_prefix + ann["id"],
|
564 |
+
"type": ann["type"],
|
565 |
+
"normalized": [],
|
566 |
+
}
|
567 |
+
)
|
568 |
+
if len(skipped_relations) > 0:
|
569 |
+
example_id = brat_parse["document_id"]
|
570 |
+
logger.info(
|
571 |
+
f"Example:{example_id}: The `bigbio_kb` schema allows `relations` only between entities."
|
572 |
+
f" Skip (for now): "
|
573 |
+
f"{list(skipped_relations)}"
|
574 |
+
)
|
575 |
+
|
576 |
+
# get coreferences
|
577 |
+
unified_example["coreferences"] = []
|
578 |
+
for i, ann in enumerate(brat_parse["equivalences"], start=1):
|
579 |
+
is_entity_cluster = True
|
580 |
+
for ref_id in ann["ref_ids"]:
|
581 |
+
if not ref_id.startswith("T"): # not textbound -> no entity
|
582 |
+
is_entity_cluster = False
|
583 |
+
elif ref_id not in anno_ids: # event trigger -> no entity
|
584 |
+
is_entity_cluster = False
|
585 |
+
if is_entity_cluster:
|
586 |
+
entity_ids = [id_prefix + i for i in ann["ref_ids"]]
|
587 |
+
unified_example["coreferences"].append(
|
588 |
+
{"id": id_prefix + str(i), "entity_ids": entity_ids}
|
589 |
+
)
|
590 |
+
return unified_example
|
head_qa.py
ADDED
@@ -0,0 +1,230 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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 |
+
import json
|
17 |
+
from pathlib import Path
|
18 |
+
from typing import Dict, List, Tuple
|
19 |
+
|
20 |
+
import datasets
|
21 |
+
|
22 |
+
from .bigbiohub import BigBioConfig, Tasks, qa_features
|
23 |
+
|
24 |
+
_LANGUAGES = ["English", "Spanish"]
|
25 |
+
_LICENSE = "MIT"
|
26 |
+
_LOCAL = False
|
27 |
+
_PUBMED = False
|
28 |
+
|
29 |
+
_CITATION = """\
|
30 |
+
@inproceedings{vilares-gomez-rodriguez-2019-head,
|
31 |
+
title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
|
32 |
+
author = "Vilares, David and G{\'o}mez-Rodr{\'i}guez, Carlos",
|
33 |
+
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
|
34 |
+
month = jul,
|
35 |
+
year = "2019",
|
36 |
+
address = "Florence, Italy",
|
37 |
+
publisher = "Association for Computational Linguistics",
|
38 |
+
url = "https://www.aclweb.org/anthology/P19-1092",
|
39 |
+
doi = "10.18653/v1/P19-1092",
|
40 |
+
pages = "960--966"
|
41 |
+
}
|
42 |
+
"""
|
43 |
+
|
44 |
+
_DATASETNAME = "head_qa"
|
45 |
+
_DISPLAYNAME = "HEAD-QA"
|
46 |
+
|
47 |
+
_DESCRIPTION = """\
|
48 |
+
HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the \
|
49 |
+
Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the \
|
50 |
+
Ministerio de Sanidad, Consumo y Bienestar Social.The dataset contains questions about following topics: medicine, \
|
51 |
+
nursing, psychology, chemistry, pharmacology and biology.
|
52 |
+
"""
|
53 |
+
|
54 |
+
_HOMEPAGE = "https://aghie.github.io/head-qa/"
|
55 |
+
|
56 |
+
_URLS = {
|
57 |
+
"HEAD": "https://drive.usercontent.google.com/u/0/uc?id=1dUIqVwvoZAtbX_-z5axCoe97XNcFo1No&export=download",
|
58 |
+
"HEAD_EN": "https://drive.usercontent.google.com/u/0/uc?id=1phryJg4FjCFkn0mSCqIOP2-FscAeKGV0&export=download",
|
59 |
+
}
|
60 |
+
|
61 |
+
_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
|
62 |
+
|
63 |
+
_SOURCE_VERSION = "1.0.0"
|
64 |
+
_BIGBIO_VERSION = "1.0.0"
|
65 |
+
|
66 |
+
|
67 |
+
class HeadQADataset(datasets.GeneratorBasedBuilder):
|
68 |
+
"""HEAD-QA: A Healthcare Dataset for Complex Reasoning"""
|
69 |
+
|
70 |
+
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
|
71 |
+
BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
|
72 |
+
|
73 |
+
BUILDER_CONFIGS = [
|
74 |
+
BigBioConfig(
|
75 |
+
name="head_qa_en_source",
|
76 |
+
version=SOURCE_VERSION,
|
77 |
+
description="HeadQA English source schema",
|
78 |
+
schema="source",
|
79 |
+
subset_id="head_qa_en",
|
80 |
+
),
|
81 |
+
BigBioConfig(
|
82 |
+
name="head_qa_es_source",
|
83 |
+
version=SOURCE_VERSION,
|
84 |
+
description="HeadQA Spanish source schema",
|
85 |
+
schema="source",
|
86 |
+
subset_id="head_qa_es",
|
87 |
+
),
|
88 |
+
BigBioConfig(
|
89 |
+
name="head_qa_en_bigbio_qa",
|
90 |
+
version=BIGBIO_VERSION,
|
91 |
+
description="HeadQA English Question Answering BigBio schema",
|
92 |
+
schema="bigbio_qa",
|
93 |
+
subset_id="head_qa_en",
|
94 |
+
),
|
95 |
+
BigBioConfig(
|
96 |
+
name="head_qa_es_bigbio_qa",
|
97 |
+
version=BIGBIO_VERSION,
|
98 |
+
description="HeadQA Spanish Question Answering BigBio schema",
|
99 |
+
schema="bigbio_qa",
|
100 |
+
subset_id="head_qa_es",
|
101 |
+
),
|
102 |
+
]
|
103 |
+
|
104 |
+
DEFAULT_CONFIG_NAME = "head_qa_en_source"
|
105 |
+
|
106 |
+
def _info(self) -> datasets.DatasetInfo:
|
107 |
+
if self.config.schema == "source":
|
108 |
+
features = datasets.Features(
|
109 |
+
{
|
110 |
+
"name": datasets.Value("string"),
|
111 |
+
"year": datasets.Value("string"),
|
112 |
+
"category": datasets.Value("string"),
|
113 |
+
"qid": datasets.Value("int32"),
|
114 |
+
"qtext": datasets.Value("string"),
|
115 |
+
"ra": datasets.Value("int32"),
|
116 |
+
"answers": [
|
117 |
+
{
|
118 |
+
"aid": datasets.Value("int32"),
|
119 |
+
"atext": datasets.Value("string"),
|
120 |
+
}
|
121 |
+
],
|
122 |
+
}
|
123 |
+
)
|
124 |
+
elif self.config.schema == "bigbio_qa":
|
125 |
+
features = qa_features
|
126 |
+
else:
|
127 |
+
raise NotImplementedError(f"Schema {self.config.schema} is not supported")
|
128 |
+
|
129 |
+
return datasets.DatasetInfo(
|
130 |
+
description=_DESCRIPTION,
|
131 |
+
features=features,
|
132 |
+
homepage=_HOMEPAGE,
|
133 |
+
license=_LICENSE,
|
134 |
+
citation=_CITATION,
|
135 |
+
)
|
136 |
+
|
137 |
+
def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
|
138 |
+
"""Returns SplitGenerators."""
|
139 |
+
if self.config.subset_id == "head_qa_en":
|
140 |
+
data_dir = Path(dl_manager.download_and_extract(_URLS["HEAD_EN"])) / "HEAD_EN"
|
141 |
+
subset_name = "HEAD_EN"
|
142 |
+
|
143 |
+
elif self.config.subset_id == "head_qa_es":
|
144 |
+
data_dir = Path(dl_manager.download_and_extract(_URLS["HEAD"])) / "HEAD"
|
145 |
+
subset_name = "HEAD"
|
146 |
+
|
147 |
+
else:
|
148 |
+
raise NotImplementedError(f"Subset {self.config.subset_id} is not supported")
|
149 |
+
|
150 |
+
return [
|
151 |
+
datasets.SplitGenerator(
|
152 |
+
name=datasets.Split.TRAIN,
|
153 |
+
gen_kwargs={
|
154 |
+
"input_json_file": data_dir / f"train_{subset_name}.json",
|
155 |
+
},
|
156 |
+
),
|
157 |
+
datasets.SplitGenerator(
|
158 |
+
name=datasets.Split.VALIDATION,
|
159 |
+
gen_kwargs={
|
160 |
+
"input_json_file": data_dir / f"dev_{subset_name}.json",
|
161 |
+
},
|
162 |
+
),
|
163 |
+
datasets.SplitGenerator(
|
164 |
+
name=datasets.Split.TEST,
|
165 |
+
gen_kwargs={
|
166 |
+
"input_json_file": data_dir / f"test_{subset_name}.json",
|
167 |
+
},
|
168 |
+
),
|
169 |
+
]
|
170 |
+
|
171 |
+
def _generate_examples(self, input_json_file: Path) -> Tuple[int, Dict]:
|
172 |
+
"""Yields examples as (key, example) tuples."""
|
173 |
+
|
174 |
+
if self.config.schema == "source":
|
175 |
+
for key, example in self._generate_source_documents(input_json_file):
|
176 |
+
yield key, example
|
177 |
+
|
178 |
+
elif self.config.schema == "bigbio_qa":
|
179 |
+
for key, example in self._generate_source_documents(input_json_file):
|
180 |
+
yield self._source_to_qa(example)
|
181 |
+
|
182 |
+
def _generate_source_documents(self, input_json_file: Path) -> Tuple[str, Dict]:
|
183 |
+
"""Generates source instances."""
|
184 |
+
|
185 |
+
with input_json_file.open("r", encoding="utf8") as file_stream:
|
186 |
+
head_qa = json.load(file_stream)
|
187 |
+
|
188 |
+
for exam_id, exam in enumerate(head_qa["exams"]):
|
189 |
+
content = head_qa["exams"][exam]
|
190 |
+
name = content["name"].strip()
|
191 |
+
year = content["year"].strip()
|
192 |
+
category = content["category"].strip()
|
193 |
+
|
194 |
+
for question in content["data"]:
|
195 |
+
qid = int(question["qid"].strip())
|
196 |
+
qtext = question["qtext"].strip()
|
197 |
+
ra = int(question["ra"].strip())
|
198 |
+
|
199 |
+
aids = [answer["aid"] for answer in question["answers"]]
|
200 |
+
atexts = [answer["atext"].strip() for answer in question["answers"]]
|
201 |
+
answers = [{"aid": aid, "atext": atext} for aid, atext in zip(aids, atexts)]
|
202 |
+
|
203 |
+
instance_id = f"{exam_id}_{qid}"
|
204 |
+
instance = {
|
205 |
+
"name": name,
|
206 |
+
"year": year,
|
207 |
+
"category": category,
|
208 |
+
"qid": qid,
|
209 |
+
"qtext": qtext,
|
210 |
+
"ra": ra,
|
211 |
+
"answers": answers,
|
212 |
+
}
|
213 |
+
|
214 |
+
yield instance_id, instance
|
215 |
+
|
216 |
+
def _source_to_qa(self, example: Dict) -> Tuple[str, Dict]:
|
217 |
+
"""Converts a source example to BigBio example."""
|
218 |
+
|
219 |
+
instance = {
|
220 |
+
"id": example["name"] + "_qid_" + str(example["qid"]),
|
221 |
+
"question_id": example["qid"],
|
222 |
+
"document_id": None,
|
223 |
+
"question": example["qtext"],
|
224 |
+
"type": "multiple_choice",
|
225 |
+
"choices": [answer["atext"] for answer in example["answers"]],
|
226 |
+
"context": None,
|
227 |
+
"answer": [next(filter(lambda answer: answer["aid"] == example["ra"], example["answers"]))["atext"]],
|
228 |
+
}
|
229 |
+
|
230 |
+
return instance["id"], instance
|