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Update head_qa based on git version 251a069

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  1. README.md +50 -0
  2. bigbiohub.py +590 -0
  3. head_qa.py +230 -0
README.md ADDED
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1
+ ---
2
+ language:
3
+ - es
4
+ - en
5
+ bigbio_language:
6
+ - English
7
+ - Spanish
8
+ license: mit
9
+ bigbio_license_shortname: MIT
10
+ multilinguality: multilingual
11
+ pretty_name: HEAD-QA
12
+ homepage: https://aghie.github.io/head-qa/
13
+ bigbio_pubmed: false
14
+ bigbio_public: true
15
+ bigbio_tasks:
16
+ - QUESTION_ANSWERING
17
+ ---
18
+
19
+
20
+ # Dataset Card for HEAD-QA
21
+
22
+ ## Dataset Description
23
+
24
+ - **Homepage:** https://aghie.github.io/head-qa/
25
+ - **Pubmed:** False
26
+ - **Public:** True
27
+ - **Tasks:** QA
28
+
29
+ HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the
30
+ Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the
31
+ Ministerio de Sanidad, Consumo y Bienestar Social. The dataset contains questions about following topics: medicine,
32
+ nursing, psychology, chemistry, pharmacology and biology.
33
+
34
+
35
+ ## Citation Information
36
+
37
+ ```
38
+ @inproceedings{vilares-gomez-rodriguez-2019-head,
39
+ title = "{HEAD}-{QA}: A Healthcare Dataset for Complex Reasoning",
40
+ author = "Vilares, David and G{\'o}mez-Rodr{\'i}guez, Carlos",
41
+ booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
42
+ month = jul,
43
+ year = "2019",
44
+ address = "Florence, Italy",
45
+ publisher = "Association for Computational Linguistics",
46
+ url = "https://www.aclweb.org/anthology/P19-1092",
47
+ doi = "10.18653/v1/P19-1092",
48
+ pages = "960--966"
49
+ }
50
+ ```
bigbiohub.py ADDED
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1
+ from collections import defaultdict
2
+ from dataclasses import dataclass
3
+ from enum import Enum
4
+ import logging
5
+ from pathlib import Path
6
+ from types import SimpleNamespace
7
+ from typing import TYPE_CHECKING, Dict, Iterable, List, Tuple
8
+
9
+ import datasets
10
+
11
+ if TYPE_CHECKING:
12
+ import bioc
13
+
14
+ logger = logging.getLogger(__name__)
15
+
16
+
17
+ BigBioValues = SimpleNamespace(NULL="<BB_NULL_STR>")
18
+
19
+
20
+ @dataclass
21
+ class BigBioConfig(datasets.BuilderConfig):
22
+ """BuilderConfig for BigBio."""
23
+
24
+ name: str = None
25
+ version: datasets.Version = None
26
+ description: str = None
27
+ schema: str = None
28
+ subset_id: str = None
29
+
30
+
31
+ class Tasks(Enum):
32
+ NAMED_ENTITY_RECOGNITION = "NER"
33
+ NAMED_ENTITY_DISAMBIGUATION = "NED"
34
+ EVENT_EXTRACTION = "EE"
35
+ RELATION_EXTRACTION = "RE"
36
+ COREFERENCE_RESOLUTION = "COREF"
37
+ QUESTION_ANSWERING = "QA"
38
+ TEXTUAL_ENTAILMENT = "TE"
39
+ SEMANTIC_SIMILARITY = "STS"
40
+ TEXT_PAIRS_CLASSIFICATION = "TXT2CLASS"
41
+ PARAPHRASING = "PARA"
42
+ TRANSLATION = "TRANSL"
43
+ SUMMARIZATION = "SUM"
44
+ TEXT_CLASSIFICATION = "TXTCLASS"
45
+
46
+
47
+ entailment_features = datasets.Features(
48
+ {
49
+ "id": datasets.Value("string"),
50
+ "premise": datasets.Value("string"),
51
+ "hypothesis": datasets.Value("string"),
52
+ "label": datasets.Value("string"),
53
+ }
54
+ )
55
+
56
+ pairs_features = datasets.Features(
57
+ {
58
+ "id": datasets.Value("string"),
59
+ "document_id": datasets.Value("string"),
60
+ "text_1": datasets.Value("string"),
61
+ "text_2": datasets.Value("string"),
62
+ "label": datasets.Value("string"),
63
+ }
64
+ )
65
+
66
+ qa_features = datasets.Features(
67
+ {
68
+ "id": datasets.Value("string"),
69
+ "question_id": datasets.Value("string"),
70
+ "document_id": datasets.Value("string"),
71
+ "question": datasets.Value("string"),
72
+ "type": datasets.Value("string"),
73
+ "choices": [datasets.Value("string")],
74
+ "context": datasets.Value("string"),
75
+ "answer": datasets.Sequence(datasets.Value("string")),
76
+ }
77
+ )
78
+
79
+ text_features = datasets.Features(
80
+ {
81
+ "id": datasets.Value("string"),
82
+ "document_id": datasets.Value("string"),
83
+ "text": datasets.Value("string"),
84
+ "labels": [datasets.Value("string")],
85
+ }
86
+ )
87
+
88
+ text2text_features = datasets.Features(
89
+ {
90
+ "id": datasets.Value("string"),
91
+ "document_id": datasets.Value("string"),
92
+ "text_1": datasets.Value("string"),
93
+ "text_2": datasets.Value("string"),
94
+ "text_1_name": datasets.Value("string"),
95
+ "text_2_name": datasets.Value("string"),
96
+ }
97
+ )
98
+
99
+ kb_features = datasets.Features(
100
+ {
101
+ "id": datasets.Value("string"),
102
+ "document_id": datasets.Value("string"),
103
+ "passages": [
104
+ {
105
+ "id": datasets.Value("string"),
106
+ "type": datasets.Value("string"),
107
+ "text": datasets.Sequence(datasets.Value("string")),
108
+ "offsets": datasets.Sequence([datasets.Value("int32")]),
109
+ }
110
+ ],
111
+ "entities": [
112
+ {
113
+ "id": datasets.Value("string"),
114
+ "type": datasets.Value("string"),
115
+ "text": datasets.Sequence(datasets.Value("string")),
116
+ "offsets": datasets.Sequence([datasets.Value("int32")]),
117
+ "normalized": [
118
+ {
119
+ "db_name": datasets.Value("string"),
120
+ "db_id": datasets.Value("string"),
121
+ }
122
+ ],
123
+ }
124
+ ],
125
+ "events": [
126
+ {
127
+ "id": datasets.Value("string"),
128
+ "type": datasets.Value("string"),
129
+ # refers to the text_bound_annotation of the trigger
130
+ "trigger": {
131
+ "text": datasets.Sequence(datasets.Value("string")),
132
+ "offsets": datasets.Sequence([datasets.Value("int32")]),
133
+ },
134
+ "arguments": [
135
+ {
136
+ "role": datasets.Value("string"),
137
+ "ref_id": datasets.Value("string"),
138
+ }
139
+ ],
140
+ }
141
+ ],
142
+ "coreferences": [
143
+ {
144
+ "id": datasets.Value("string"),
145
+ "entity_ids": datasets.Sequence(datasets.Value("string")),
146
+ }
147
+ ],
148
+ "relations": [
149
+ {
150
+ "id": datasets.Value("string"),
151
+ "type": datasets.Value("string"),
152
+ "arg1_id": datasets.Value("string"),
153
+ "arg2_id": datasets.Value("string"),
154
+ "normalized": [
155
+ {
156
+ "db_name": datasets.Value("string"),
157
+ "db_id": datasets.Value("string"),
158
+ }
159
+ ],
160
+ }
161
+ ],
162
+ }
163
+ )
164
+
165
+
166
+ TASK_TO_SCHEMA = {
167
+ Tasks.NAMED_ENTITY_RECOGNITION.name: "KB",
168
+ Tasks.NAMED_ENTITY_DISAMBIGUATION.name: "KB",
169
+ Tasks.EVENT_EXTRACTION.name: "KB",
170
+ Tasks.RELATION_EXTRACTION.name: "KB",
171
+ Tasks.COREFERENCE_RESOLUTION.name: "KB",
172
+ Tasks.QUESTION_ANSWERING.name: "QA",
173
+ Tasks.TEXTUAL_ENTAILMENT.name: "TE",
174
+ Tasks.SEMANTIC_SIMILARITY.name: "PAIRS",
175
+ Tasks.TEXT_PAIRS_CLASSIFICATION.name: "PAIRS",
176
+ 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 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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