Upload huggingface.co_datasets_Dr-BERT_ESSAI_raw_main_ESSAI.py

huggingface.co_datasets_Dr-BERT_ESSAI_raw_main_ESSAI.py

@@ -0,0 +1,364 @@
+import os
+import random
+
+import datasets
+import numpy as np
+
+_CITATION = """\
+ @misc{
+    dalloux,
+    title={Datasets – Clément Dalloux},
+    url={http://clementdalloux.fr/?page_id=28},
+    journal={Clément Dalloux},
+    author={Dalloux, Clément}
+} 
+"""
+
+_DESCRIPTION = """\
+We manually annotated two corpora from the biomedical field. The ESSAI corpus \
+contains clinical trial protocols in French. They were mainly obtained from the \
+National Cancer Institute The typical protocol consists of two parts: the \
+summary of the trial, which indicates the purpose of the trial and the methods \
+applied; and a detailed description of the trial with the inclusion and \
+exclusion criteria. The CAS corpus contains clinical cases published in \
+scientific literature and training material. They are published in different \
+journals from French-speaking countries (France, Belgium, Switzerland, Canada, \
+African countries, tropical countries) and are related to various medical \
+specialties (cardiology, urology, oncology, obstetrics, pulmonology, \
+gastro-enterology). The purpose of clinical cases is to describe clinical \
+situations of patients. Hence, their content is close to the content of clinical \
+narratives (description of diagnoses, treatments or procedures, evolution, \
+family history, expected audience, etc.). In clinical cases, the negation is \
+frequently used for describing the patient signs, symptoms, and diagnosis. \
+Speculation is present as well but less frequently.
+
+This version only contain the annotated ESSAI corpus
+"""
+
+_HOMEPAGE = "https://clementdalloux.fr/?page_id=28"
+
+_LICENSE = 'Data User Agreement'
+
+class ESSAI(datasets.GeneratorBasedBuilder):
+
+    DEFAULT_CONFIG_NAME = "pos_spec"
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="pos", version="1.0.0", description="The ESSAI corpora - POS Speculation task"),
+
+        datasets.BuilderConfig(name="cls", version="1.0.0", description="The ESSAI corpora - CLS Negation / Speculation task"),
+
+        datasets.BuilderConfig(name="ner_spec", version="1.0.0", description="The ESSAI corpora - NER Speculation task"),
+        datasets.BuilderConfig(name="ner_neg", version="1.0.0", description="The ESSAI corpora - NER Negation task"),
+    ]
+
+    def _info(self):
+
+        if self.config.name.find("pos") != -1:
+
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "tokens": [datasets.Value("string")],
+                    "lemmas": [datasets.Value("string")],
+                    "pos_tags": [datasets.features.ClassLabel(
+                        names = ['B-INT', 'B-PRO:POS', 'B-PRP', 'B-SENT', 'B-PRO', 'B-ABR', 'B-VER:pres', 'B-KON', 'B-SYM', 'B-DET:POS', 'B-VER:', 'B-PRO:IND', 'B-NAM', 'B-ADV', 'B-PRO:DEM', 'B-NN', 'B-PRO:PER', 'B-VER:pper', 'B-VER:ppre', 'B-PUN', 'B-VER:simp', 'B-PREF', 'B-NUM', 'B-VER:futu', 'B-NOM', 'B-VER:impf', 'B-VER:subp', 'B-VER:infi', 'B-DET:ART', 'B-PUN:cit', 'B-ADJ', 'B-PRP:det', 'B-PRO:REL', 'B-VER:cond', 'B-VER:subi'],
+                    )],
+                }
+            )
+        
+        elif self.config.name.find("cls") != -1:
+
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "tokens": [datasets.Value("string")],
+                    "label": datasets.features.ClassLabel(
+                        names = ['negation_speculation', 'negation', 'neutral', 'speculation'],
+                    ),
+                }
+            )
+        
+        elif self.config.name.find("ner") != -1:
+
+            if self.config.name.find("_spec") != -1:
+                names = ['O', 'B_cue_spec', 'B_scope_spec', 'I_scope_spec']
+            elif self.config.name.find("_neg") != -1:
+                names = ['O', 'B_cue_neg', 'B_scope_neg', 'I_scope_neg']
+
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "tokens": [datasets.Value("string")],
+                    "lemmas": [datasets.Value("string")],
+                    "ner_tags": [datasets.features.ClassLabel(
+                        names = names,
+                    )],
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        if self.config.data_dir is None:
+            raise ValueError("This is a local dataset. Please pass the data_dir kwarg to load_dataset.")
+        
+        else:
+            data_dir = self.config.data_dir
+            
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "datadir": data_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "datadir": data_dir,
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "datadir": data_dir,
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, datadir, split):
+
+        all_res = []
+
+        key = 0
+
+        subset = self.config.name.split("_")[-1]
+
+        unique_id_doc = []
+
+        if self.config.name.find("ner") != -1:
+            docs = [f"ESSAI_{subset}.txt"]
+        else:
+            docs = ["ESSAI_neg.txt", "ESSAI_spec.txt"]
+
+        for file in docs:
+
+            filename = os.path.join(datadir, file)
+
+            if self.config.name.find("pos") != -1:
+
+                id_docs = []
+                id_words = []
+                words = []
+                lemmas = []
+                POS_tags = []
+
+                with open(filename) as f:
+
+                    for line in f.readlines():
+
+                        splitted = line.split("\t")
+
+                        if len(splitted) < 5:
+                            continue
+
+                        id_doc, id_word, word, lemma, tag = splitted[0:5]
+                        if len(splitted) >= 8:
+                            tag = splitted[6]
+                        
+                        if tag == "@card@":
+                            print(splitted)
+
+                        if word == "@card@":
+                            print(splitted)
+
+                        if lemma == "000" and tag == "@card@":
+                            tag = "NUM"
+                            word = "100 000"
+                            lemma = "100 000"
+                        elif lemma == "45" and tag == "@card@":
+                            tag = "NUM"
+
+                        # if id_doc in id_docs:
+                        #     continue
+
+                        id_docs.append(id_doc)
+                        id_words.append(id_word)
+                        words.append(word)
+                        lemmas.append(lemma)
+                        POS_tags.append('B-'+tag)
+
+                dic = {
+                    "id_docs":  np.array(list(map(int, id_docs))),
+                    "id_words": id_words,
+                    "words":    words,
+                    "lemmas":   lemmas,
+                    "POS_tags": POS_tags,
+                }
+
+                for doc_id in set(dic["id_docs"]):
+
+                    indexes = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
+                    tokens = [dic["words"][id] for id in indexes]
+                    text_lemmas = [dic["lemmas"][id] for id in indexes]
+                    pos_tags = [dic["POS_tags"][id] for id in indexes]
+
+                    if doc_id not in unique_id_doc:
+
+                        all_res.append({
+                            "id": str(doc_id),
+                            "document_id": doc_id,
+                            "tokens": tokens,
+                            "lemmas": text_lemmas,
+                            "pos_tags": pos_tags,
+                        })
+                        unique_id_doc.append(doc_id)
+
+                        # key += 1
+
+            elif self.config.name.find("ner") != -1:
+
+                id_docs = []
+                id_words = []
+                words = []
+                lemmas = []
+                ner_tags = []
+
+                with open(filename) as f:
+
+                    for line in f.readlines():
+
+                        if len(line.split("\t")) < 5:
+                            continue
+
+                        id_doc, id_word, word, lemma, _ = line.split("\t")[0:5]
+                        tag = line.replace("\n","").split("\t")[-1]
+
+                        if tag == "***" or tag == "_":
+                            tag = "O"
+                        elif tag == "v":
+                            tag = "I_scope_spec"
+                        elif tag == "z":
+                            tag = "O"
+                        elif tag == "I_scope_spec_":
+                            tag = "I_scope_spec"
+
+                        id_docs.append(id_doc)
+                        id_words.append(id_word)
+                        words.append(word)
+                        lemmas.append(lemma)
+                        ner_tags.append(tag)
+
+                dic = {
+                    "id_docs":  np.array(list(map(int, id_docs))),
+                    "id_words": id_words,
+                    "words":    words,
+                    "lemmas":   lemmas,
+                    "ner_tags": ner_tags,
+                }
+
+                for doc_id in set(dic["id_docs"]):
+
+                    indexes = np.argwhere(dic["id_docs"] == doc_id)[:, 0]
+                    tokens = [dic["words"][id] for id in indexes]
+                    text_lemmas = [dic["lemmas"][id] for id in indexes]
+                    ner_tags = [dic["ner_tags"][id] for id in indexes]
+
+                    all_res.append({
+                        "id": key,
+                        "document_id": doc_id,
+                        "tokens": tokens,
+                        "lemmas": text_lemmas,
+                        "ner_tags": ner_tags,
+                    })
+
+                    key += 1
+
+            elif self.config.name.find("cls") != -1:
+
+                f_in = open(filename, "r")
+                conll = [
+                    [b.split("\t") for b in a.split("\n")]
+                    for a in f_in.read().split("\n\n")
+                ]
+                f_in.close()
+
+                classe = "negation" if filename.find("_neg") != -1 else "speculation"
+
+                for document in conll:
+
+                    if document == [""]:
+                        continue
+
+                    identifier = document[0][0]
+
+                    unique = list(set([w[-1] for w in document]))
+                    tokens = [sent[2] for sent in document if len(sent) > 1]
+
+                    if "***" in unique:
+                        l = "neutral"
+                    elif "_" in unique:
+                        l = classe
+
+                    if identifier in unique_id_doc and l == 'neutral':
+                        continue
+
+                    elif identifier in unique_id_doc and l != 'neutral':
+
+                        index_l = unique_id_doc.index(identifier)
+
+                        if all_res[index_l]["label"] != "neutral":
+                            l = "negation_speculation"
+
+                        all_res[index_l] = {
+                            "id": str(identifier),
+                            "document_id": identifier,
+                            "tokens": tokens,
+                            "label": l,
+                        }
+
+                    else:
+
+                        all_res.append({
+                            "id": str(identifier),
+                            "document_id": identifier,
+                            "tokens": tokens,
+                            "label": l,
+                        })
+
+                        unique_id_doc.append(identifier)
+
+        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