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cantemist/README.md

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+
+
+https://temu.bsc.es/cantemist/?cat=2
+
+Codigos validos
+
+ ==> https://temu.bsc.es/cantemist/wp-content/uploads/2020/07/valid-codes.txt
+
+ On HugginFace Database
+   https://huggingface.co/datasets/bigbio/cantemist/viewer/cantemist_bigbio_kb
+
+  bigbio/cantemist

cantemist/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/cantemist'
+DATASET_TO_UPDATE = 'somosnlp/spanish_medica_llm'
+DATASET_SOURCE_ID = '1'
+
+#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()
+
+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': 'medical_diagnostic',
+  'source': DATASET_SOURCE_ID,
+  'country': 'es',
+  'document_id': ''
+}
+
+totalOfTokens = 0
+corpusToLoad = []
+countCopySeveralDocument = 0
+counteOriginalDocument = 0
+
+for iDataset in dataset_CODING:
+    if iDataset == 'test':
+      for item in dataset_CODING[iDataset]:
+        #print ("Element in dataset")
+        idFile = item['id']
+        text = item['text']
+        list_of_type = item['text_bound_annotations']
+        labels_of_type = item['labels']
+
+        #Find topic or diagnosti clasification about the text
+        diagnostyc_types = getCodeDescription( labels_of_type, royalListOfCode)
+        counteOriginalDocument += 1
+        classFileSize = len(diagnostyc_types)
+
+        #If there are more clasification about the file
+        
+        if classFileSize > 1:
+         countCopySeveralDocument += classFileSize - 1
+
+        listOfTokens = tokenizer.tokenize(text)
+        currentSizeOfTokens = len(listOfTokens)
+        totalOfTokens += currentSizeOfTokens
+        
+        for iTypes in diagnostyc_types:
+          #print(iTypes)
+          newCorpusRow = cantemistDstDict.copy()
+
+          #print('Current text has ', currentSizeOfTokens)
+          #print('Total of tokens is ', totalOfTokens)
+          newCorpusRow['raw_text'] = text
+          newCorpusRow['document_id'] = str(idFile)
+          newCorpusRow['topic'] = iTypes
+          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
+
+
+