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import os |
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import datasets |
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import numpy as np |
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import pandas as pd |
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from .bigbiohub import text_features |
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from .bigbiohub import kb_features |
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from .bigbiohub import BigBioConfig |
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from .bigbiohub import Tasks |
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_LANGUAGES = ['French'] |
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_PUBMED = False |
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_LOCAL = True |
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_CITATION = """\ |
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@misc{dalloux, title={Datasets – Clément Dalloux}, url={http://clementdalloux.fr/?page_id=28}, journal={Clément Dalloux}, author={Dalloux, Clément}} |
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""" |
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_DATASETNAME = "essai" |
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_DISPLAYNAME = "ESSAI" |
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_DESCRIPTION = """\ |
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We manually annotated two corpora from the biomedical field. The ESSAI corpus \ |
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contains clinical trial protocols in French. They were mainly obtained from the \ |
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National Cancer Institute The typical protocol consists of two parts: the \ |
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summary of the trial, which indicates the purpose of the trial and the methods \ |
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applied; and a detailed description of the trial with the inclusion and \ |
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exclusion criteria. The CAS corpus contains clinical cases published in \ |
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scientific literature and training material. They are published in different \ |
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journals from French-speaking countries (France, Belgium, Switzerland, Canada, \ |
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African countries, tropical countries) and are related to various medical \ |
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specialties (cardiology, urology, oncology, obstetrics, pulmonology, \ |
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gastro-enterology). The purpose of clinical cases is to describe clinical \ |
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situations of patients. Hence, their content is close to the content of clinical \ |
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narratives (description of diagnoses, treatments or procedures, evolution, \ |
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family history, expected audience, etc.). In clinical cases, the negation is \ |
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frequently used for describing the patient signs, symptoms, and diagnosis. \ |
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Speculation is present as well but less frequently. |
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This version only contain the annotated ESSAI corpus |
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""" |
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_HOMEPAGE = "https://clementdalloux.fr/?page_id=28" |
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_LICENSE = 'Data User Agreement' |
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_URLS = { |
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"essai_source": "", |
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"essai_bigbio_text": "", |
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"essai_bigbio_kb": "", |
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} |
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_SOURCE_VERSION = "1.0.0" |
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_BIGBIO_VERSION = "1.0.0" |
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_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION] |
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class ESSAI(datasets.GeneratorBasedBuilder): |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) |
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DEFAULT_CONFIG_NAME = "essai_source" |
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BUILDER_CONFIGS = [ |
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BigBioConfig( |
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name="essai_source", |
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version=SOURCE_VERSION, |
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description="ESSAI source schema", |
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schema="source", |
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subset_id="essai", |
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), |
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BigBioConfig( |
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name="essai_bigbio_text", |
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version=BIGBIO_VERSION, |
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description="ESSAI simplified BigBio schema for negation/speculation classification", |
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schema="bigbio_text", |
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subset_id="essai", |
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), |
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BigBioConfig( |
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name="essai_bigbio_kb", |
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version=BIGBIO_VERSION, |
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description="ESSAI simplified BigBio schema for part-of-speech-tagging", |
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schema="bigbio_kb", |
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subset_id="essai", |
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), |
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] |
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def _info(self): |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"document_id": datasets.Value("string"), |
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"text": [datasets.Value("string")], |
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"lemmas": [datasets.Value("string")], |
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"POS_tags": [datasets.Value("string")], |
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"labels": [datasets.Value("string")], |
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} |
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) |
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elif self.config.schema == "bigbio_text": |
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features = text_features |
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elif self.config.schema == "bigbio_kb": |
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features = kb_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=str(_LICENSE), |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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if self.config.data_dir is None: |
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raise ValueError( |
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"This is a local dataset. Please pass the data_dir kwarg to load_dataset." |
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) |
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else: |
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data_dir = self.config.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={"datadir": data_dir}, |
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), |
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] |
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def _generate_examples(self, datadir): |
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key = 0 |
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filepath = os.path.join(datadir, "ESSAI_neg.txt") |
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filepath2 = os.path.join(datadir, 'ESSAI_spec.txt') |
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id_docs = [] |
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id_docs_2 = [] |
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id_words = [] |
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words = [] |
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lemmas = [] |
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POS_tags = [] |
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NER_tags = [] |
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NER_tags_2 = [] |
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with open(filepath) as f: |
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for line in f.readlines(): |
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line_content = line.split("\t") |
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if len(line_content) > 1: |
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id_docs.append(line_content[0]) |
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id_words.append(line_content[1]) |
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words.append(line_content[2]) |
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lemmas.append(line_content[3]) |
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POS_tags.append(line_content[4]) |
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NER_tags.append(line_content[5].strip()) |
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with open(filepath2) as f: |
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for line in f.readlines(): |
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line_content = line.split("\t") |
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if len(line_content) > 1: |
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id_docs_2.append(line_content[0]) |
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NER_tags_2.append(line_content[5].strip()) |
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dic = { |
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"id_docs": np.array(list(map(int, id_docs))), |
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"id_words": id_words, |
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"words": words, |
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"lemmas": lemmas, |
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"POS_tags": POS_tags, |
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"NER_tags": NER_tags |
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} |
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dic2 = { |
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"id_docs": np.array(list(map(int, id_docs_2))), |
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"NER_tags": NER_tags_2 |
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} |
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if self.config.schema == "source": |
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for doc_id in set(dic["id_docs"]): |
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idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0] |
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text = [dic["words"][id] for id in idces] |
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text_lemmas = [dic["lemmas"][id] for id in idces] |
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POS_tags_ = [dic["POS_tags"][id] for id in idces] |
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yield key, { |
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"id": key, |
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"document_id": doc_id, |
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"text": text, |
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"lemmas": text_lemmas, |
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"POS_tags": POS_tags_, |
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"labels": [], |
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} |
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key += 1 |
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elif self.config.schema == "bigbio_text": |
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for doc_id in set(dic["id_docs"]): |
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idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0] |
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idces_2 = np.argwhere(dic2["id_docs"] == doc_id)[:, 0] |
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text = " ".join([dic["words"][id] for id in idces]) |
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label_tokens = [dic["NER_tags"][id] for id in idces] |
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label2_tokens = [dic2["NER_tags"][id] for id in idces_2] |
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label_ = [] |
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if not all(l == '***' for l in label_tokens): |
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label_.append("negation") |
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if not all(l == '***' for l in label2_tokens): |
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label_.append("speculation") |
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yield key, { |
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"id": key, |
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"document_id": doc_id, |
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"text": text, |
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"labels": label_, |
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} |
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key += 1 |
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elif self.config.schema == "bigbio_kb": |
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for doc_id in set(dic["id_docs"]): |
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idces = np.argwhere(dic["id_docs"] == doc_id)[:, 0] |
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text = [dic["words"][id] for id in idces] |
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POS_tags_ = [dic["POS_tags"][id] for id in idces] |
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data = { |
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"id": str(key), |
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"document_id": doc_id, |
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"passages": [], |
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"entities": [], |
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"relations": [], |
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"events": [], |
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"coreferences": [], |
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} |
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key += 1 |
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data["passages"] = [ |
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{ |
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"id": str(key + i), |
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"type": "sentence", |
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"text": [text[i]], |
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"offsets": [[i, i + 1]], |
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} |
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for i in range(len(text)) |
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] |
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key += len(text) |
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for i in range(len(text)): |
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entity = { |
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"id": key, |
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"type": "POS_tag", |
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"text": [POS_tags_[i]], |
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"offsets": [[i, i + 1]], |
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"normalized": [], |
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} |
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data["entities"].append(entity) |
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key += 1 |
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yield key, data |
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