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from pyknp import Juman
import torch
from pytorch_transformers import *

config = BertConfig.from_json_file('Japanese_L-12_H-768_A-12_E-30_BPE/bert_config.json')
model = BertForMaskedLM.from_pretrained('Japanese_L-12_H-768_A-12_E-30_BPE/pytorch_model.bin',
                                        config=config)
tokenizer = BertTokenizer('Japanese_L-12_H-768_A-12_E-30_BPE/vocab.txt',
                          do_lower_case=False, do_basic_tokenize=False)

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# -*- coding: utf-8 -*-
from lxml import etree
import os
import sys
from io import TextIOWrapper
from nltk.tokenize import RegexpTokenizer

#
# USAGE:
#

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import sys
import torch
import torch.nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import numpy as np
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt

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import io, sys, math, random
import numpy as np
import matplotlib.pyplot as plt

import torch
import torch.nn
from torch.autograd import Variable
from torch import optim

sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')

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import torch
from torch.autograd import Variable
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d

torch.manual_seed(102)
np.random.seed(22)

fig = plt.figure()

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import sys
import io
import MeCab
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf8')
m = MeCab.Tagger()
fp = open('a1.txt', encoding='utf8')
wseq = list()
for line in fp:
    for ol in m.parse(line.strip()).split('\n'):
        if len(ol.split()) > 1:

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import pandas as pd
import statsmodels.api as sm

# glow500.xls at https://www.umass.edu/statdata/statdata/data/glow/index.html
xls_file = pd.ExcelFile('glow500.xls')
df = xls_file.parse(header=0)

rate_dummies = pd.get_dummies(df['RATERISK'])
rate_dummies.columns = ['RATERISK1', 'RATERISK2', 'RATERISK3']

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#include <stdio.h>
#include <stdlib.h>
#include <stdbool.h>
#include <string.h>

#define SEPCHAR '_'
#define MAXLEN 32

#define BUFFSIZE 1000000
#define TOKENLEN 8