| |
|
|
| import os |
| import random |
| import requests |
|
|
| import datasets |
|
|
| import numpy as np |
| from bs4 import BeautifulSoup, ResultSet |
| from syntok.tokenizer import Tokenizer |
|
|
| tokenizer = Tokenizer() |
|
|
| _CITATION = """\ |
| @InProceedings{Kocabiyikoglu2022, |
| author = "Alican Kocabiyikoglu and Fran{\c c}ois Portet and Prudence Gibert and Hervé Blanchon and Jean-Marc Babouchkine and Gaëtan Gavazzi", |
| title = "A Spoken Drug Prescription Dataset in French for Spoken Language Understanding", |
| booktitle = "13th Language Resources and Evaluation Conference (LREC 2022)", |
| year = "2022", |
| location = "Marseille, France" |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| PxSLU is to the best of our knowledge, the first spoken medical drug prescriptions corpus to be distributed. It contains 4 hours of transcribed |
| and annotated dialogues of drug prescriptions in French acquired through an experiment with 55 participants experts and non-experts in drug prescriptions. |
| |
| The automatic transcriptions were verified by human effort and aligned with semantic labels to allow training of NLP models. The data acquisition |
| protocol was reviewed by medical experts and permit free distribution without breach of privacy and regulation. |
| |
| Overview of the Corpus |
| |
| The experiment has been performed in wild conditions with naive participants and medical experts. In total, the dataset includes 1981 recordings |
| of 55 participants (38% non-experts, 25% doctors, 36% medical practitioners), manually transcribed and semantically annotated. |
| """ |
|
|
| _URL = "https://zenodo.org/record/6524162/files/pxslu.zip?download=1" |
|
|
| class StringIndex: |
|
|
| def __init__(self, vocab): |
|
|
| self.vocab_struct = {} |
|
|
| print("Start building the index!") |
| for t in vocab: |
|
|
| if len(t) == 0: |
| continue |
|
|
| |
| key = (t[0], len(t)) |
|
|
| if (key in self.vocab_struct) == False: |
| self.vocab_struct[key] = [] |
| |
| self.vocab_struct[key].append(t) |
|
|
| print("Finished building the index!") |
|
|
| def find(self, t): |
| |
| key = (t[0], len(t)) |
| |
| if (key in self.vocab_struct) == False: |
| return "is_oov" |
| |
| return "is_not_oov" if t in self.vocab_struct[key] else "is_oov" |
|
|
| _VOCAB = StringIndex(vocab=requests.get("https://huggingface.co/datasets/BioMedTok/vocabulary_nachos_lowercased/resolve/main/vocabulary_nachos_lowercased.txt").text.split("\n")) |
|
|
| class PxCorpus(datasets.GeneratorBasedBuilder): |
|
|
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig(name=f"default", version="1.0.0", description=f"PxCorpus data"), |
| ] |
| |
| DEFAULT_CONFIG_NAME = "default" |
|
|
| def _info(self): |
| |
| features = datasets.Features( |
| { |
| "id": datasets.Value("string"), |
| "text": datasets.Value("string"), |
| "label": datasets.features.ClassLabel( |
| names=["medical_prescription", "negate", "none", "replace"], |
| ), |
| "tokens": datasets.Sequence(datasets.Value("string")), |
| "ner_tags": datasets.Sequence( |
| datasets.features.ClassLabel( |
| names=['O', 'B-A', 'B-cma_event', 'B-d_dos_form', 'B-d_dos_form_ext', 'B-d_dos_up', 'B-d_dos_val', 'B-dos_cond', 'B-dos_uf', 'B-dos_val', 'B-drug', 'B-dur_ut', 'B-dur_val', 'B-fasting', 'B-freq_days', 'B-freq_int_v1', 'B-freq_int_v1_ut', 'B-freq_int_v2', 'B-freq_int_v2_ut', 'B-freq_startday', 'B-freq_ut', 'B-freq_val', 'B-inn', 'B-max_unit_uf', 'B-max_unit_ut', 'B-max_unit_val', 'B-min_gap_ut', 'B-min_gap_val', 'B-qsp_ut', 'B-qsp_val', 'B-re_ut', 'B-re_val', 'B-rhythm_hour', 'B-rhythm_perday', 'B-rhythm_rec_ut', 'B-rhythm_rec_val', 'B-rhythm_tdte', 'B-roa', 'I-cma_event', 'I-d_dos_form', 'I-d_dos_form_ext', 'I-d_dos_up', 'I-d_dos_val', 'I-dos_cond', 'I-dos_uf', 'I-dos_val', 'I-drug', 'I-fasting', 'I-freq_startday', 'I-inn', 'I-rhythm_tdte', 'I-roa'], |
| ), |
| ), |
| "is_oov": datasets.Sequence( |
| datasets.features.ClassLabel( |
| names=['is_not_oov', 'is_oov'], |
| ), |
| ), |
| } |
| ) |
| |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=features, |
| citation=_CITATION, |
| supervised_keys=None, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
|
|
| data_dir = dl_manager.download_and_extract(_URL) |
|
|
| print(data_dir) |
| |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath_1": os.path.join(data_dir, "seq.in"), |
| "filepath_2": os.path.join(data_dir, "seq.label"), |
| "filepath_3": os.path.join(data_dir, "PxSLU_conll.txt"), |
| "split": "train", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "filepath_1": os.path.join(data_dir, "seq.in"), |
| "filepath_2": os.path.join(data_dir, "seq.label"), |
| "filepath_3": os.path.join(data_dir, "PxSLU_conll.txt"), |
| "split": "validation", |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "filepath_1": os.path.join(data_dir, "seq.in"), |
| "filepath_2": os.path.join(data_dir, "seq.label"), |
| "filepath_3": os.path.join(data_dir, "PxSLU_conll.txt"), |
| "split": "test", |
| }, |
| ), |
| ] |
|
|
| def getTokenTags(self, document): |
|
|
| tokens = [] |
| ner_tags = [] |
| is_oov = [] |
|
|
| for pair in document.split("\n"): |
|
|
| if len(pair) <= 0: |
| continue |
|
|
| text, label = pair.split("\t") |
| tokens.append(text.lower()) |
| ner_tags.append(label) |
| is_oov.append(_VOCAB.find(text.lower())) |
|
|
| return tokens, ner_tags, is_oov |
|
|
| def _generate_examples(self, filepath_1, filepath_2, filepath_3, split): |
|
|
| key = 0 |
| all_res = [] |
| |
| f_seq_in = open(filepath_1, "r") |
| seq_in = f_seq_in.read().split("\n") |
| f_seq_in.close() |
|
|
| f_seq_label = open(filepath_2, "r") |
| seq_label = f_seq_label.read().split("\n") |
| f_seq_label.close() |
|
|
| f_in_ner = open(filepath_3, "r") |
| docs = f_in_ner.read().split("\n\n") |
| f_in_ner.close() |
|
|
| for idx, doc in enumerate(docs): |
|
|
| text = seq_in[idx] |
| label = seq_label[idx] |
|
|
| tokens, ner_tags, is_oov = self.getTokenTags(docs[idx]) |
|
|
| if len(text) <= 0 or len(label) <= 0: |
| continue |
|
|
| all_res.append({ |
| "id": key, |
| "text": text, |
| "label": label, |
| "tokens": tokens, |
| "ner_tags": ner_tags, |
| "is_oov": is_oov, |
| }) |
| |
| key += 1 |
|
|
| ids = [r["id"] for r in all_res] |
|
|
| random.seed(4) |
| random.shuffle(ids) |
| random.shuffle(ids) |
| random.shuffle(ids) |
| |
| train, validation, test = np.split(ids, [int(len(ids)*0.70), int(len(ids)*0.80)]) |
|
|
| if split == "train": |
| allowed_ids = list(train) |
| elif split == "validation": |
| allowed_ids = list(validation) |
| elif split == "test": |
| allowed_ids = list(test) |
| |
| for r in all_res: |
| if r["id"] in allowed_ids: |
| yield r["id"], r |
