Upload using_dataset_hugginface.py

distemist/using_dataset_hugginface.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
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+HF_TOKEN = ''
+DATASET_TO_LOAD = 'bigbio/distemist'
+DATASET_TO_UPDATE = 'somosnlp/spanish_medica_llm'
+DATASET_SOURCE_ID = '9'
+
+#Loggin to Huggin Face
+login(token = HF_TOKEN)
+
+dataset_CODING = load_dataset(DATASET_TO_LOAD)
+royalListOfCode = {}
+issues_path = 'dataset'
+tokenizer = AutoTokenizer.from_pretrained("DeepESP/gpt2-spanish-medium")
+
+#Read current path
+path = Path(__file__).parent.absolute()
+
+#print (dataset_CODING)
+
+# with open( str(path) + os.sep + 'ICD-O-3_valid-codes.txt',encoding='utf8') as file:
+#   """
+#      # Build a dictionary with ICD-O-3 associated with 
+#      # healtcare problems
+#   """
+#   linesInFile = file.readlines()
+#   for iLine in linesInFile:
+#     listOfData = iLine.split('\t')
+
+#     code = listOfData[0]
+#     description = reduce(lambda a, b: a + " "+ b, listOfData[1:2], "")
+#     royalListOfCode[code.strip()] = description.strip()
+
+
+# def getCodeDescription(labels_of_type, royalListOfCode):
+#   """
+#     Search description associated with some code 
+#     in royalListOfCode
+
+#   """
+#   classification = [] 
+
+#   for iValue in labels_of_type:
+#     if iValue in  royalListOfCode.keys():
+#       classification.append(royalListOfCode[iValue])
+#   return classification
+
+
+#     # 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': 'clinic_case',
+  'topic_type': '',
+  'source': DATASET_SOURCE_ID,
+  'country': 'es',
+  'document_id': ''
+}
+
+totalOfTokens = 0
+corpusToLoad = []
+countCopySeveralDocument = 0
+counteOriginalDocument = 0
+
+#print (dataset_CODING['train'][5]['entities'])
+
+# for item in dataset_CODING['train']:
+#     for passage in item['passages']:
+#       print ("Keys " + str( passage.keys()))
+#       print("Clinical case  type " + str(passage['text']))
+
+for iDataset in dataset_CODING:
+      for item in dataset_CODING[iDataset]:
+        for passageItem in item['passages']:        
+            #print ("Element in dataset")
+            idFile = passageItem['id'] + '_' + str(iDataset)
+            text = passageItem['text'][0]
+
+            #Find topic or diagnosti clasification about the text
+            counteOriginalDocument += 1
+
+            listOfTokens = tokenizer.tokenize(text)
+            currentSizeOfTokens = len(listOfTokens)
+            totalOfTokens += currentSizeOfTokens
+            newCorpusRow = cantemistDstDict.copy()
+
+            #print('Current text has ', currentSizeOfTokens)
+            #print('Total of tokens is ', totalOfTokens)
+            newCorpusRow['raw_text'] = text
+            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")
+  spanish_dataset = concatenate_datasets([spanish_dataset, local_spanish_dataset])
+except Exception:
+  spanish_dataset = local_spanish_dataset
+
+spanish_dataset.push_to_hub(DATASET_TO_UPDATE)
+
+print(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
+
+
+