alabrashJr

def predict(text):
    print("text -> ",text)
    text_s=[stemmer.stem(w) for w in text.split()]
    print("text stemmed-> ",text_s)
    X=vectorizer.transform([text_s])
    X_pred=lp_classifier.predict(X).toarray()
    return [data.columns[i] for i,x in enumerate(X_pred[0]) if x==1]

text="araçım servise son getirmediğimde düzgün tamir edilmedi memnun değil"

alabrashJr

Çoklu Etiket Metin Sınıflandırma

Sınıflandırma Yöntemleri

köpek midir?	hangi etkit ?	hangi etiketler?
Evet/Hayırikili	köpek/doğa/çimençok sınıflı	köpek,doğa,çimen****çoklu etiket

alabrashJr

#this script was written by Cigil and edit by [email protected]
import pandas as pd
from lxml import etree
import re
import os, sys, io, traceback, codecs
from bs4 import BeautifulSoup, Comment
UNESCAPE = True
import html as h
import glob

alabrashJr

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests \n",
    "from bs4 import BeautifulSoup\n",

alabrashJr

#fname: the file name of binary file <google_w2v.bin>
#vocab: vocabulary dictionary

function load_bin_vec(fname, vocab)
pc(s)=return convert(Char,s[1])
word_vecs = Dict()
    open(fname, "r") do f
                @show header = readline(f)
                vocab_size, layer1_size = map(pf, split(header))
                @show binary_len = sizeof(Float32) * layer1_size