In :
"""
We use following lines because we are running on Google Colab
If you are running notebook on a local computer, you don't need this cell
"""
drive.mount('/content/gdrive')
import os
os.chdir('/content/gdrive/My Drive/finch/tensorflow1/text_matching/snli/main')

In :
import numpy as np
import re

from collections import Counter
from pathlib import Path


Make Data

In :
def normalize(x):
x = x.lower()
x = x.replace('.', '')
x = x.replace(',', '')
x = x.replace(';', '')
x = x.replace('!', '')
x = x.replace('#', '')
x = x.replace('(', '')
x = x.replace(')', '')
x = x.replace(':', '')
x = x.replace('%', '')
x = x.replace('&', '')
x = x.replace('\$', '')
x = x.replace('?', '')
x = x.replace('"', '')
x = x.replace('/', ' ')
x = x.replace('-', ' ')
x = x.replace("n't", " n't ")
x = x.replace("'", " ' ")
x = re.sub(r'\d+', ' <num> ', x)
x = re.sub(r'\s+', ' ', x)
return x

In :
def write_text(in_path, out_path):
with open(in_path) as f_in, open(out_path, 'w') as f_out:
for line in f_in:
line = line.rstrip()
sp = line.split('\t')
label, sent1, sent2 = sp, sp, sp

sent1 = normalize(sent1)
sent2 = normalize(sent2)

f_out.write(label+'\t'+sent1+'\t'+sent2+'\n')

In :
write_text('../data/snli_1.0/snli_1.0_train.txt', '../data/train.txt')
write_text('../data/snli_1.0/snli_1.0_test.txt', '../data/test.txt')


Make Vocabulary

In :
counter = Counter()
with open('../data/train.txt') as f:
for line in f:
line = line.rstrip()
label, sent1, sent2 = line.split('\t')
counter.update(sent1.split())
counter.update(sent2.split())

words = [w for w, freq in counter.most_common() if freq >= 3]

Path('../vocab').mkdir(exist_ok=True)

with open('../vocab/word.txt', 'w') as f:
for w in words:
f.write(w+'\n')


Make Pretrained Embedding

In :
def norm_weight(nin, nout, scale=0.01):
W = scale * np.random.randn(nin, nout)
return W.astype(np.float32)

In :
word2idx = {}
with open('../vocab/word.txt') as f:
for i, line in enumerate(f):
line = line.rstrip()
word2idx[line] = i

In :
embedding = norm_weight(len(word2idx)+1, 300)

with open('../data/glove.840B.300d.txt') as f:
count = 0
for i, line in enumerate(f):
if i % 100000 == 0:
print('- At line {}'.format(i))
line = line.rstrip()
sp = line.split(' ')
word, vec = sp, sp[1:]
if word in word2idx:
count += 1
embedding[word2idx[word]] = np.asarray(vec, dtype=np.float32)

print("[%d / %d] words have found pre-trained values"%(count, len(word2idx)))
np.save('../vocab/word.npy', embedding)
print('Saved ../vocab/word.npy')

- At line 0
- At line 100000
- At line 200000
- At line 300000
- At line 400000
- At line 500000
- At line 600000
- At line 700000
- At line 800000
- At line 900000
- At line 1000000
- At line 1100000
- At line 1200000
- At line 1300000
- At line 1400000
- At line 1500000
- At line 1600000
- At line 1700000
- At line 1800000
- At line 1900000
- At line 2000000
- At line 2100000
[20333 / 20883] words have found pre-trained values
Saved ../vocab/word.npy