Upload using_dataset.py

medlineplus_spanish/using_dataset.py

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+# -*- coding: utf-8 -*-
+"""using_dataset_hugginface.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1soGxkZu4antYbYG23GioJ6zoSt_GhSNT
+"""
+
+"""**Hugginface loggin for push on Hub**"""
+###
+#
+#  Used bibliografy:
+#    https://huggingface.co/learn/nlp-course/chapter5/5
+#
+###
+
+import os
+import time
+import math
+from huggingface_hub import login
+from datasets import load_dataset, concatenate_datasets
+from functools import reduce
+from pathlib import Path
+import pandas as pd
+import numpy as np
+
+
+# Load model directly
+from transformers import AutoTokenizer
+
+HF_TOKEN = ''
+DATASET_TO_LOAD = 'spanish_health_output.json'
+DATASET_TO_UPDATE = 'somosnlp/spanish_medica_llm'
+BAD_CHAIN = [
+   'es como usted puede verificarlo',
+   'Un sitio oficial del Gobierno de Estados Unidos',
+   'lo en sitios web oficiales y seguros.',
+   'forma segura a un sitio web .gov. Comparta informaci', 
+   'Gobierno de Estados Unidos.',
+   'pertenece a una organizaci',
+   '(\r\n              \n ) o ',
+   'Un sitio\r\n'
+]
+#Loggin to Huggin Face
+login(token = HF_TOKEN)
+
+
+
+royalListOfCode = {}
+issues_path = 'dataset'
+tokenizer = AutoTokenizer.from_pretrained("DeepESP/gpt2-spanish-medium")
+DATASET_SOURCE_ID = '2'
+#Read current path
+path = Path(__file__).parent.absolute()
+
+dataset_CODING = pd.read_json(str(path) + os.sep + DATASET_TO_LOAD,  encoding="utf8")
+
+    # raw_text: Texto asociado al documento, pregunta, caso clínico u otro tipo de información.
+
+    # topic: (puede ser healthcare_treatment, healthcare_diagnosis, tema, respuesta a pregunta, o estar vacío p.ej en el texto abierto)
+
+    # speciality: (especialidad médica a la que se relaciona el raw_text p.ej: cardiología, cirugía, otros)
+
+    # raw_text_type: (puede ser caso clínico, open_text, question)
+
+    # topic_type: (puede ser medical_topic, medical_diagnostic,answer,natural_medicine_topic, other, o vacio)
+
+    # source: Identificador de la fuente asociada al documento que aparece en el README y descripción del dataset.
+
+    # 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.).
+cantemistDstDict = {
+  'raw_text': '',
+  'topic': '',
+  'speciallity': '',
+  'raw_text_type': 'open_text',
+  'topic_type': 'other',
+  'source': DATASET_SOURCE_ID,
+  'country': 'es',
+  'document_id': ''
+}
+
+def getExtraTexInformation(item, data_top_columname):
+     optionalTag = ["Healthtopics Name", "titles", "subtitles", "paragraphs"]
+     text = ""
+
+     for key in data_top_columname:
+          if key not in optionalTag:
+              if not np.isnan(item[key]) and len(item[key]) > 1:
+                text += str(item[key]) + '\n'          
+              
+     return text
+               
+totalOfTokens = 0
+corpusToLoad = []
+countCopySeveralDocument = 0
+counteOriginalDocument = 0
+data_top_columname = dataset_CODING.head()
+
+def verifyRepetelyChain(paragraph):
+    return '' if len([ x for x in BAD_CHAIN if paragraph.find(x) != -1]) > 0 else paragraph
+    
+
+for index, item in dataset_CODING.iterrows():      
+
+        if len(item['paragraphs']) > 1:
+           text = reduce(lambda a, b: verifyRepetelyChain(a) + "\n "+ verifyRepetelyChain(b), item['paragraphs'], "")
+        else:
+           text = getExtraTexInformation(item, data_top_columname)
+        #Find topic or diagnosti clasification about the text
+       
+        counteOriginalDocument += 1  
+        newCorpusRow = cantemistDstDict.copy()
+
+          #print('Current text has ', currentSizeOfTokens)
+          #print('Total of tokens is ', totalOfTokens)
+
+        listOfTokens = []
+        try:
+          listOfTokens = tokenizer.tokenize(text)
+        except Exception:
+           raise Exception('Error')
+             
+        currentSizeOfTokens = len(listOfTokens)
+        totalOfTokens += currentSizeOfTokens
+
+        newCorpusRow['topic'] =  item['Healthtopics Name']  if item['Healthtopics Name'] else reduce(lambda a, b: a + "\n "+ b, item['titles'], "")
+        newCorpusRow['raw_text'] = text
+        idFile = counteOriginalDocument
+        newCorpusRow['document_id'] = str(idFile)
+        corpusToLoad.append(newCorpusRow)
+        
+      
+df = pd.DataFrame.from_records(corpusToLoad)
+
+if os.path.exists(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl"):
+  os.remove(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl")
+
+df.to_json(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", orient="records", lines=True)
+print(
+        f"Downloaded all the issues for {DATASET_TO_LOAD}! Dataset stored at {issues_path}/spanish_medical_llms.jsonl"
+)
+
+print(' On dataset there are as document ', counteOriginalDocument)
+print(' On dataset there are as copy document ', countCopySeveralDocument)
+print(' On dataset there are as size of Tokens ', totalOfTokens)
+file = Path(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl")  # or Path('./doc.txt')
+size = file.stat().st_size
+print ('File size on Kilobytes (kB)', size >> 10)  # 5242880 kilobytes (kB)
+print ('File size on Megabytes  (MB)', size >> 20 ) # 5120 megabytes (MB)
+print ('File size on Gigabytes (GB)', size >> 30 ) # 5 gigabytes (GB)
+
+#Once the issues are downloaded we can load them locally using our 
+local_spanish_dataset = load_dataset("json", data_files=f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", split="train")
+
+##Update local dataset with cloud dataset
+try:  
+  spanish_dataset = load_dataset(DATASET_TO_UPDATE, split="train")
+  new_spanish_dataset = concatenate_datasets([spanish_dataset, local_spanish_dataset])
+except Exception:
+  print ('<== Exception ==> ')
+  raise Exception
+  #new_spanish_dataset = local_spanish_dataset
+
+new_spanish_dataset.push_to_hub(DATASET_TO_UPDATE)
+
+print(new_spanish_dataset)
+
+# Augmenting the dataset
+
+#Importan if exist element on DATASET_TO_UPDATE we must to update element 
+# in list, and review if the are repeted elements
+
+#spanish_dataset.push_to_hub(DATASET_TO_UPDATE)
+