bricksdont

#! /bin/env python
import os
import logging
import mxnet as mx
import numpy as np

logging.getLogger().setLevel(logging.INFO)


#######################################

bricksdont

#! /bin/env python
import os
import logging
import mxnet as mx
import numpy as np

logging.getLogger().setLevel(logging.INFO)

############################################
# Parameter sharing between BucketingModules

bricksdont

# import embeddings

!ls shared/MUSE

import musevecs

mdls = musevecs.MUSE('shared/MUSE/wiki.multi.{0}.vecfull.txt', {'en', 'de', 'fr'}, nmax=200000)

enmdl = mdls.vecmap['en']

bricksdont

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import lxml.etree as et

xml="""
<text:doc xmlns:text="www.doc.com">
   <text:span text:style-name="Kursiv_5f_KeiinRecht">
      <text:span text:style-name="T25">madhuparká</text:span>

bricksdont

<?xml version="1.0" encoding="UTF-8"?>
<xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0" xmlns:text="www.doc.com">
  <xsl:output method="xml" indent="yes"/>
  <xsl:strip-space elements="*"/>

  <xsl:template match="@*|node()">
    <xsl:copy>
      <xsl:apply-templates select="@*|node()"/>
    </xsl:copy>
   </xsl:template>

bricksdont

#! /bin/bash

chunk_input_dir=$1
chunk_output_dir=$2
chunk_prefix=$3
chunk_index=$4

model_paths=$5
batch_size=$6

bricksdont

#!/usr/bin/env bash

# author:  Mathias Mueller / mathias.mueller@uzh.ch
# purpose: train word alignment models with fast_align

# usage information
if [ $# -lt 4 ]
    then
        echo "[ERROR] Too few arguments. Expected 4 command line arguments." 1>&2
        echo "Usage: $0 <language 1> <language 2> <path to training set without language suffix> <output directory for trained model>" 1>&2

bricksdont

#!/usr/bin/env bash

# author:  Mathias Mueller / mathias.mueller@uzh.ch
# purpose: apply word alignment models trained with fast_align

# usage information
if [ $# -lt 6 ]
    then
        echo "[ERROR] Too few arguments. Expected 6 command line arguments." 1>&2
        echo "Usage: $0 <language 1> <language 2> <source txt file> <target txt file> <directory of trained model> <output file path>" 1>&2

bricksdont

# Author: Mathias Müller / mmueller@cl.uzh.ch

import mxnet as mx
import numpy as np

from typing import Optional, List

SOFTMAX_NAME = "softmax"
WEIGHTED_CROSS_ENTROPY_NAME = "weighted_cross_entropy"

bricksdont

import mxnet as mx
import numpy as np
import random

from collections import namedtuple


def run(seed: int, init_method: str):

    mx.random.seed(seed)