from transformers import TFBertModel
model = TFBertModel.from_pretrained("./rubert-base-cased-pt", from_pt=True)

model.save("./rubert-base-cased") # this adds a TF model file (tf_model.h5) to your directory

# find longest common string
def find_lcs(s1, s2):
    m = [[0 for i in range(len(s2)+1)] for j in range(len(s1)+1)]
    mmax = 0
    p = 0
    for i in range(len(s1)):
        for j in range(len(s2)):
            if s1[i] == s2[j]:
                m[i+1 …

git clone https://github.com/GrahamDumpleton/mod_wsgi

apt-get install apache2-dev

apt-get install python-dev

cd mod_wsgi/

./configure

make

make install

import math
import torch
import torch.nn as nn
from torch.nn import TransformerEncoder, TransformerEncoderLayer, TransformerDecoder, TransformerDecoderLayer
from layers.encoder import CustomEmbedding
import layers


class PositionalEncoding(nn.Module):
    """
    Args:
        d_model: the number of expected features in the input (required).
        dropout: the dropout value (default …

def to_var(x, on_cpu=False, gpu_id=None):
    """Tensor => Variable"""
    if torch.cuda.is_available() and not on_cpu:
        x = x.cuda(gpu_id, non_blocking=True)
        # x = Variable(x)
    return x

def normal_kl_div(mu1, var1 …