ricardocarvalhods

from subprocess import call
call(['python', '-m', 'nbconvert', 'My_Notebook.ipynb'])

ricardocarvalhods

# Do not include azure-functions-worker as it may conflict with the Azure Functions platform

azure-functions
transformers
requests
-f https://download.pytorch.org/whl/torch_stable.html
torch==1.9.0+cpu

ricardocarvalhods

import logging
import json
import azure.functions as func
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
import torch

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
nlp = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)

ricardocarvalhods

import numpy as np
from numpy import random
from sklearn import base

class PUWrapper(object):
    def __init__(self,trad_clf,n_fold=5):
        self._trad_clf=trad_clf
        self._n_fold=n_fold

    def fit(self,X,s):

ricardocarvalhods

makeglm <- function(formula, ..., family, data=NULL) {
    dots <- list(...)
    out<-list()
    tt <- terms(formula, data=data)
    if(!is.null(data)) {
        mf <- model.frame(tt, data)
        vn <- sapply(attr(tt, "variables")[-1], deparse)
 
        if((yvar <- attr(tt, "response"))>0)
            vn <- vn[-yvar]