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spaccc/process_dataset.py ADDED
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+ from datasets import load_dataset
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+ import os
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+ import re
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+
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+ from pathlib import Path
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+ from zipfile import ZipFile
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+ import tarfile
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+
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+ FILE_PATH = "BARR2.tar.bz2"
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+
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+ path = Path(__file__).parent.absolute()
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+
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+ from urllib import request
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+ URL = 'https://zenodo.org/api/records/2560316/files-archive'
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+
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+ FILE_ZIP = str(path) + os.sep + "SPACC.zip"
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+ FILE_ZIP_EXTRAC = str(path) + os.sep + "SPACC"
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+
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+ if not os.path.exists( FILE_ZIP ):
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+ response = request.urlretrieve(URL, FILE_ZIP)
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+
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+ # loading the temp.zip and creating a zip object
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+ if os.path.exists( FILE_ZIP_EXTRAC ):
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+ os.remove(FILE_ZIP_EXTRAC)
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+ os.makedirs(FILE_ZIP_EXTRAC)
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+
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+ with ZipFile(FILE_ZIP, 'r') as zObject:
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+
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+ # Extracting specific file in the zip
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+ # into a specific location.
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+ zObject.extractall( FILE_ZIP_EXTRAC)
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+ zObject.close()
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+
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+ #Open Zip
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+
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+ # with open( str(path) + os.sep + 'example.txt', encoding='utf8') as file:
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+ # """
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+ # # Build a dictionary with ICD-O-3 associated with
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+ # # healtcare problems
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+ # """
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+ # linesInFile = file.readlines()
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+
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+ # for index, iLine in enumerate(linesInFile):
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+ # print([linesInFile[index]]) if len(linesInFile[index]) > 1 else print('**************') if linesInFile[index] == '\n' else print ('******* ERROR ********')
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+
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+
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+ # if re.match('^Las dilataciones bronquiales',iLine):
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+ # break
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+
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+
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+ # code = listOfData[0]
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+ # description = reduce(lambda a, b: a + " "+ b, listOfData[1:2], "")
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+ # royalListOfCode[code.strip()] = description.strip()
spaccc/using_dataset_hugginface.py ADDED
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+ # -*- coding: utf-8 -*-
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+ """using_dataset_hugginface.ipynb
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+
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+ Automatically generated by Colaboratory.
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+
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+ Original file is located at
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+ https://colab.research.google.com/drive/1soGxkZu4antYbYG23GioJ6zoSt_GhSNT
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+ """
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+
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+ """**Hugginface loggin for push on Hub**"""
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+ ###
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+ #
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+ # Used bibliografy:
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+ # https://huggingface.co/learn/nlp-course/chapter5/5
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+ #
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+ ###
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+
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+ import os
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+ import time
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+ import math
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+ from huggingface_hub import login
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+ from datasets import load_dataset, concatenate_datasets
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+ from functools import reduce
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+ from pathlib import Path
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+ import pandas as pd
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+ import pathlib
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+ # Load model directly
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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+ HF_TOKEN = ''
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+ DATASET_TO_LOAD = 'bigbio/distemist'
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+ DATASET_TO_UPDATE = 'somosnlp/spanish_medica_llm'
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+ DATASET_SOURCE_ID = '12'
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+ BASE_DIR = "SPACC" + os.sep + "SPACCC" + os.sep + "SPACCC" + os.sep + "corpus"
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+
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+ #Loggin to Huggin Face
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+ login(token = HF_TOKEN)
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+
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+ dataset_CODING = load_dataset(DATASET_TO_LOAD)
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+ royalListOfCode = {}
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+ issues_path = 'dataset'
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+ tokenizer = AutoTokenizer.from_pretrained("DeepESP/gpt2-spanish-medium")
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+
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+ #Read current path
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+ path = Path(__file__).parent.absolute()
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+ MAIN_FILE_ADRESS = str(path) + os.sep + BASE_DIR
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+ #print ( os.listdir(str(path) + os.sep + BASE_DIR))
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+
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+ files = [ str(path) + os.sep + BASE_DIR + os.sep + f for f in os.listdir(MAIN_FILE_ADRESS) if os.path.isfile(str(path) + os.sep + BASE_DIR + os.sep + f) and pathlib.Path(MAIN_FILE_ADRESS + os.sep + f).suffix == ".txt" ]
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+
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+ #print (files)
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+ for iFile in files:
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+ with open( iFile,encoding='utf8') as file:
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+ linesInFile = file.readlines()
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+ text = reduce(lambda a, b: a + " "+ b, linesInFile, "")
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+
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+ #print (dataset_CODING)
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+
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+ # with open( str(path) + os.sep + 'ICD-O-3_valid-codes.txt',encoding='utf8') as file:
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+ # """
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+ # # Build a dictionary with ICD-O-3 associated with
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+ # # healtcare problems
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+ # """
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+ # linesInFile = file.readlines()
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+ # for iLine in linesInFile:
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+ # listOfData = iLine.split('\t')
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+
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+ # code = listOfData[0]
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+ # description = reduce(lambda a, b: a + " "+ b, listOfData[1:2], "")
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+ # royalListOfCode[code.strip()] = description.strip()
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+
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+
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+ # def getCodeDescription(labels_of_type, royalListOfCode):
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+ # """
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+ # Search description associated with some code
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+ # in royalListOfCode
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+
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+ # """
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+ # classification = []
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+
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+ # for iValue in labels_of_type:
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+ # if iValue in royalListOfCode.keys():
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+ # classification.append(royalListOfCode[iValue])
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+ # return classification
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+
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+
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+ # # raw_text: Texto asociado al documento, pregunta, caso clínico u otro tipo de información.
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+
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+ # # topic: (puede ser healthcare_treatment, healthcare_diagnosis, tema, respuesta a pregunta, o estar vacío p.ej en el texto abierto)
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+
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+ # # speciality: (especialidad médica a la que se relaciona el raw_text p.ej: cardiología, cirugía, otros)
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+
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+ # # raw_text_type: (puede ser caso clínico, open_text, question)
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+
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+ # # topic_type: (puede ser medical_topic, medical_diagnostic,answer,natural_medicine_topic, other, o vacio)
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+
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+ # # source: Identificador de la fuente asociada al documento que aparece en el README y descripción del dataset.
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+
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+ # # country: Identificador del país de procedencia de la fuente (p.ej.; ch, es) usando el estándar ISO 3166-1 alfa-2 (Códigos de país de dos letras.).
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+ cantemistDstDict = {
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+ 'raw_text': '',
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+ 'topic': '',
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+ 'speciallity': '',
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+ 'raw_text_type': 'clinic_case',
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+ 'topic_type': '',
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+ 'source': DATASET_SOURCE_ID,
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+ 'country': 'es',
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+ 'document_id': ''
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+ }
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+
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+ totalOfTokens = 0
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+ corpusToLoad = []
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+ countCopySeveralDocument = 0
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+ counteOriginalDocument = 0
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+
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+ #print (dataset_CODING['train'][5]['entities'])
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+
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+ for iFile in files:
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+ with open( iFile,encoding='utf8') as file:
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+ linesInFile = file.readlines()
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+ text = reduce(lambda a, b: a + " "+ b, linesInFile, "")
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+ #print ("Element in dataset")
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+
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+ #Find topic or diagnosti clasification about the text
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+ counteOriginalDocument += 1
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+
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+ listOfTokens = tokenizer.tokenize(text)
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+ currentSizeOfTokens = len(listOfTokens)
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+ totalOfTokens += currentSizeOfTokens
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+ newCorpusRow = cantemistDstDict.copy()
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+
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+
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+ newCorpusRow['raw_text'] = text
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+ newCorpusRow['document_id'] = str(counteOriginalDocument)
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+ corpusToLoad.append(newCorpusRow)
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+
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+
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+ df = pd.DataFrame.from_records(corpusToLoad)
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+
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+ if os.path.exists(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl"):
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+ os.remove(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl")
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+
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+ df.to_json(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", orient="records", lines=True)
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+ print(
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+ f"Downloaded all the issues for {DATASET_TO_LOAD}! Dataset stored at {issues_path}/spanish_medical_llms.jsonl"
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+ )
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+
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+ print(' On dataset there are as document ', counteOriginalDocument)
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+ print(' On dataset there are as copy document ', countCopySeveralDocument)
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+ print(' On dataset there are as size of Tokens ', totalOfTokens)
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+ file = Path(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl") # or Path('./doc.txt')
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+ size = file.stat().st_size
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+ print ('File size on Kilobytes (kB)', size >> 10) # 5242880 kilobytes (kB)
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+ print ('File size on Megabytes (MB)', size >> 20 ) # 5120 megabytes (MB)
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+ print ('File size on Gigabytes (GB)', size >> 30 ) # 5 gigabytes (GB)
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+
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+ #Once the issues are downloaded we can load them locally using our
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+ local_spanish_dataset = load_dataset("json", data_files=f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", split="train")
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+
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+
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+ ##Update local dataset with cloud dataset
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+ try:
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+ spanish_dataset = load_dataset(DATASET_TO_UPDATE, split="train")
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+ print("=== Before ====")
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+ print(spanish_dataset)
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+ spanish_dataset = concatenate_datasets([spanish_dataset, local_spanish_dataset])
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+ except Exception:
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+ spanish_dataset = local_spanish_dataset
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+
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+ spanish_dataset.push_to_hub(DATASET_TO_UPDATE)
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+
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+ print("=== After ====")
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+ print(spanish_dataset)
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+
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+ # Augmenting the dataset
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+
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+ #Importan if exist element on DATASET_TO_UPDATE we must to update element
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+ # in list, and review if the are repeted elements
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+
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+
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+