Upload using_dataset_hugginface.py
Browse files
pharmaconer/using_dataset_hugginface.py
ADDED
@@ -0,0 +1,183 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# -*- coding: utf-8 -*-
|
2 |
+
"""using_dataset_hugginface.ipynb
|
3 |
+
|
4 |
+
Automatically generated by Colaboratory.
|
5 |
+
|
6 |
+
Original file is located at
|
7 |
+
https://colab.research.google.com/drive/1soGxkZu4antYbYG23GioJ6zoSt_GhSNT
|
8 |
+
"""
|
9 |
+
|
10 |
+
"""**Hugginface loggin for push on Hub**"""
|
11 |
+
###
|
12 |
+
#
|
13 |
+
# Used bibliografy:
|
14 |
+
# https://huggingface.co/learn/nlp-course/chapter5/5
|
15 |
+
#
|
16 |
+
###
|
17 |
+
|
18 |
+
import os
|
19 |
+
import time
|
20 |
+
import math
|
21 |
+
from huggingface_hub import login
|
22 |
+
from datasets import load_dataset, concatenate_datasets
|
23 |
+
from functools import reduce
|
24 |
+
from pathlib import Path
|
25 |
+
import pandas as pd
|
26 |
+
import mysql.connector
|
27 |
+
|
28 |
+
# Load model directly
|
29 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
30 |
+
|
31 |
+
HF_TOKEN = ''
|
32 |
+
DATASET_TO_LOAD = 'PlanTL-GOB-ES/pharmaconer'
|
33 |
+
DATASET_TO_UPDATE = 'somosnlp/spanish_medica_llm'
|
34 |
+
|
35 |
+
#Loggin to Huggin Face
|
36 |
+
login(token = HF_TOKEN)
|
37 |
+
|
38 |
+
dataset_CODING = load_dataset(DATASET_TO_LOAD)
|
39 |
+
dataset_CODING
|
40 |
+
royalListOfCode = {}
|
41 |
+
issues_path = 'dataset'
|
42 |
+
tokenizer = AutoTokenizer.from_pretrained("DeepESP/gpt2-spanish-medium")
|
43 |
+
DATASET_SOURCE_ID = '3'
|
44 |
+
#Read current path
|
45 |
+
path = Path(__file__).parent.absolute()
|
46 |
+
|
47 |
+
'''
|
48 |
+
Bibliografy:
|
49 |
+
https://www.w3schools.com/python/python_mysql_getstarted.asp
|
50 |
+
https://www.w3schools.com/python/python_mysql_select.as
|
51 |
+
|
52 |
+
'''
|
53 |
+
mydb = mysql.connector.connect(
|
54 |
+
host="localhost",
|
55 |
+
user="root",
|
56 |
+
password="",
|
57 |
+
database="icd10_dx_hackatonnlp"
|
58 |
+
)
|
59 |
+
|
60 |
+
|
61 |
+
|
62 |
+
def getCodeDescription(labels_of_type):
|
63 |
+
"""
|
64 |
+
Search description associated with some code
|
65 |
+
in royalListOfCode
|
66 |
+
|
67 |
+
"""
|
68 |
+
icd10CodeDict = {}
|
69 |
+
mycursor = mydb.cursor()
|
70 |
+
codeIcd10 = ''
|
71 |
+
|
72 |
+
for iValue in labels_of_type:
|
73 |
+
codeIcd10 = iValue
|
74 |
+
|
75 |
+
if codeIcd10.find('.') == -1:
|
76 |
+
codeIcd10 += '.0'
|
77 |
+
|
78 |
+
mycursor.execute(f"SELECT dx_code, long_desc FROM `icd10_dx_order_code` WHERE dx_code = '{codeIcd10}' LIMIT 1;")
|
79 |
+
|
80 |
+
myresult = mycursor.fetchall()
|
81 |
+
|
82 |
+
for x in myresult:
|
83 |
+
code, description = x
|
84 |
+
icd10CodeDict[code] = description
|
85 |
+
|
86 |
+
return icd10CodeDict
|
87 |
+
|
88 |
+
|
89 |
+
# raw_text: Texto asociado al documento, pregunta, caso clínico u otro tipo de información.
|
90 |
+
|
91 |
+
# topic: (puede ser healthcare_treatment, healthcare_diagnosis, tema, respuesta a pregunta, o estar vacío p.ej en el texto abierto)
|
92 |
+
|
93 |
+
# speciality: (especialidad médica a la que se relaciona el raw_text p.ej: cardiología, cirugía, otros)
|
94 |
+
|
95 |
+
# raw_text_type: (puede ser caso clínico, open_text, question)
|
96 |
+
|
97 |
+
# topic_type: (puede ser medical_topic, medical_diagnostic,answer,natural_medicine_topic, other, o vacio)
|
98 |
+
|
99 |
+
# source: Identificador de la fuente asociada al documento que aparece en el README y descripción del dataset.
|
100 |
+
|
101 |
+
# 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.).
|
102 |
+
cantemistDstDict = {
|
103 |
+
'raw_text': '',
|
104 |
+
'topic': '',
|
105 |
+
'speciallity': '',
|
106 |
+
'raw_text_type': 'clinic_case',
|
107 |
+
'topic_type': '',
|
108 |
+
'source': DATASET_SOURCE_ID,
|
109 |
+
'country': 'es',
|
110 |
+
'document_id': ''
|
111 |
+
}
|
112 |
+
|
113 |
+
totalOfTokens = 0
|
114 |
+
corpusToLoad = []
|
115 |
+
countCopySeveralDocument = 0
|
116 |
+
counteOriginalDocument = 0
|
117 |
+
|
118 |
+
for iDataset in dataset_CODING:
|
119 |
+
if iDataset == 'train':
|
120 |
+
for item in dataset_CODING[iDataset]:
|
121 |
+
#print ("Element in dataset")
|
122 |
+
idFile = str(item['id'])
|
123 |
+
text = '' if len(item['tokens']) == 0 else reduce(lambda a, b: a + " "+ b, item['tokens'], "")
|
124 |
+
|
125 |
+
#Find topic or diagnosti clasification about the text
|
126 |
+
|
127 |
+
counteOriginalDocument += 1
|
128 |
+
newCorpusRow = cantemistDstDict.copy()
|
129 |
+
|
130 |
+
#print('Current text has ', currentSizeOfTokens)
|
131 |
+
#print('Total of tokens is ', totalOfTokens)
|
132 |
+
|
133 |
+
listOfTokens = tokenizer.tokenize(text)
|
134 |
+
currentSizeOfTokens = len(listOfTokens)
|
135 |
+
totalOfTokens += currentSizeOfTokens
|
136 |
+
|
137 |
+
newCorpusRow['raw_text'] = text
|
138 |
+
newCorpusRow['document_id'] = idFile
|
139 |
+
corpusToLoad.append(newCorpusRow)
|
140 |
+
|
141 |
+
df = pd.DataFrame.from_records(corpusToLoad)
|
142 |
+
|
143 |
+
if os.path.exists(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl"):
|
144 |
+
os.remove(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl")
|
145 |
+
|
146 |
+
|
147 |
+
df.to_json(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", orient="records", lines=True)
|
148 |
+
print(
|
149 |
+
f"Downloaded all the issues for {DATASET_TO_LOAD}! Dataset stored at {issues_path}/spanish_medical_llms.jsonl"
|
150 |
+
)
|
151 |
+
|
152 |
+
print(' On dataset there are as document ', counteOriginalDocument)
|
153 |
+
print(' On dataset there are as copy document ', countCopySeveralDocument)
|
154 |
+
print(' On dataset there are as size of Tokens ', totalOfTokens)
|
155 |
+
file = Path(f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl") # or Path('./doc.txt')
|
156 |
+
size = file.stat().st_size
|
157 |
+
print ('File size on Kilobytes (kB)', size >> 10) # 5242880 kilobytes (kB)
|
158 |
+
print ('File size on Megabytes (MB)', size >> 20 ) # 5120 megabytes (MB)
|
159 |
+
print ('File size on Gigabytes (GB)', size >> 30 ) # 5 gigabytes (GB)
|
160 |
+
|
161 |
+
##Update local dataset with cloud dataset
|
162 |
+
local_spanish_dataset = load_dataset("json", data_files=f"{str(path)}/{issues_path}/spanish_medical_llms.jsonl", split="train")
|
163 |
+
|
164 |
+
print (' Local Dataset ==> ')
|
165 |
+
print(local_spanish_dataset)
|
166 |
+
|
167 |
+
try:
|
168 |
+
spanish_dataset = load_dataset(DATASET_TO_UPDATE, split="train")
|
169 |
+
spanish_dataset = concatenate_datasets([spanish_dataset, local_spanish_dataset])
|
170 |
+
except Exception:
|
171 |
+
spanish_dataset = local_spanish_dataset
|
172 |
+
|
173 |
+
spanish_dataset.push_to_hub(DATASET_TO_UPDATE)
|
174 |
+
|
175 |
+
print(spanish_dataset)
|
176 |
+
|
177 |
+
# Augmenting the dataset
|
178 |
+
|
179 |
+
#Importan if exist element on DATASET_TO_UPDATE we must to update element
|
180 |
+
# in list, and review if the are repeted elements
|
181 |
+
|
182 |
+
#spanish_dataset.push_to_hub(DATASET_TO_UPDATE)
|
183 |
+
|