alvations

$ make -j $(nproc)
Scanning dependencies of target nccl_install
Scanning dependencies of target marian_version
Scanning dependencies of target pathie-cpp
Scanning dependencies of target SQLiteCpp
Scanning dependencies of target libyaml-cpp
Scanning dependencies of target zlib
[  0%] Running cpp protocol buffer compiler on sentencepiece_model.proto
[  1%] Running cpp protocol buffer compiler on sentencepiece.proto
[  2%] Running cpp protocol buffer compiler on sentencepiece_model.proto

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[2019-08-15 08:31:02] [marian] Marian v1.7.8 c65c26d6 2019-08-11 18:27:00 +0100
[2019-08-15 08:31:02] [marian] Running on walle3 as process 24138 with command line:
[2019-08-15 08:31:02] [marian] /home/xyz/marian-dev/build/marian --model /disk2/models/xx-yy-r0/model.npz --type transformer --train-sets /disk2/data/xx-yy/train.sk /disk2/data/xx-yy/train.en --vocabs /disk2/models/xx-yy-r0/vocab.src.spm /disk2/models/xx-yy-r0/vocab.trg.spm --dim-vocabs 32000 32000 --mini-batch-fit --mini-batch 1000 --maxi-batch 1000 --valid-freq 10000 --save-freq 10000 --disp-freq 500 --valid-metrics ce-mean-words perplexity bleu-detok --valid-sets /disk2/data/xx-yy/valid.sk /disk2/data/xx-yy/valid.en --quiet-translation --beam-size 6 --normalize=0.6 --valid-mini-batch 16 --early-stopping 5 --cost-type=ce-mean-words --log /disk2/models/xx-yy-r0/train.log --valid-log /disk2/models/xx-yy-r0/valid.log --enc-depth 6 --dec-depth 6 --transformer-preprocess n --transformer-postprocess da --tied-embeddings-all --dim-emb 1024 --transforme

alvations

The Project Gutenberg EBook of The Adventures of Sherlock Holmes
by Sir Arthur Conan Doyle
(#15 in our series by Sir Arthur Conan Doyle)

Copyright laws are changing all over the world. Be sure to check the
copyright laws for your country before downloading or redistributing
this or any other Project Gutenberg eBook.

This header should be the first thing seen when viewing this Project
Gutenberg file.  Please do not remove it.  Do not change or edit the

alvations

from keras.models import Sequential
from keras.layers import Dense, Activation

model = Sequential([
    Dense(32, input_shape=(784,)),
    Activation('relu'),
    Dense(10),
    Activation('softmax'),
])

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$ th

  ______             __   |  Torch7
 /_  __/__  ________/ /   |  Scientific computing for Lua.
  / / / _ \/ __/ __/ _ \  |
 /_/  \___/_/  \__/_//_/  |  https://github.com/torch
                          |  http://torch.ch

th> torch.Tensor{1,2,3}
 1

alvations

class ToxicDataset(Dataset):
    def __init__(self, texts, labels):
        self.texts = texts
        self.vocab = Dictionary(texts)
        special_tokens = {'<pad>': 0, '<unk>':1}
        self.vocab = Dictionary(texts)
        self.vocab.patch_with_special_tokens(special_tokens)
        
        # Vectorize labels
        self.labels = torch.tensor(labels)

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import os
from argparse import Namespace
from collections import Counter
import json
import re
import string

import numpy as np
import pandas as pd
import torch

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alvations

                       Language is never, ever, ever, random

                                                               ADAM KILGARRIFF




Abstract
Language users never choose words randomly, and language is essentially
non-random. Statistical hypothesis testing uses a null hypothesis, which

alvations

How fast can we make the ngrams() function in NLTK?

From https://stackoverflow.com/q/21883108/610569, it suggested:

def zipgrams(sequence, n):
    """ From https://stackoverflow.com/q/21883108/610569"""
    return zip(*[sequence[i:] for i in range(n)])