Delete loading script

PxCorpus.py

@@ -1,178 +0,0 @@
-# pip install bs4 syntok
-
-import os
-import random
-
-import datasets
-
-import numpy as np
-from bs4 import BeautifulSoup, ResultSet
-from syntok.tokenizer import Tokenizer
-
-tokenizer = Tokenizer()
-
-_CITATION = r"""\
-@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 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'],
-                    ),
-                ),
-            }
-        )
-
-        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)
-
-        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 = []
-
-        for pair in document.split("\n"):
-
-            if len(pair) <= 0:
-                continue
-
-            text, label = pair.split("\t")
-            tokens.append(text)
-            ner_tags.append(label)
-
-        return tokens, ner_tags
-
-    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 = 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,
-            })
-
-            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) * .7), int(len(ids) * .8)])
-
-        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