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