load vector of pre-trained embedded word from pre-trained binary file like google_w2v.bin

load_bin_vec

#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
             for line in collect(1:vocab_size)
                word=[]
                while true 
                           ch=read(f,1)
                           ch=convert(Char,ch[1])
                            if ch == ' '
                                word = join(word,"")
                                break
                            end
                            if ch != '\n';
                                push!(word,ch);
                            end
                    end
                if word in keys(vocab)
                   word_vecs[word]=reinterpret(Float32,read(f,binary_len))
                else
                read(f,binary_len)
                end

            end    
    end;
return word_vecs
end