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Word Embeddings
Specifically, we will see how the program learns an embedding from scratch that
is customized to the spam detection task. Next we will see how to use an external
third-party embedding like the ones we have learned about in this chapter, a process
similar to transfer learning in computer vision. Finally, we will learn how to combine
the two approaches, starting with a third party embedding and letting the network
use that as a starting point for its custom embedding, a process similar to fine tuning
in computer vision.
As usual, we will start with our imports:
import argparse
import gensim.downloader as api
import numpy as np
import os
import shutil
import tensorflow as tf
from sklearn.metrics import accuracy_score, confusion_matrix
Scikit-learn is an open source Python machine learning toolkit that
contains many efficient and easy to use tools for data mining and
data analysis. In this chapter we have used two of its predefined
metrics, accuracy_score and confusion_matrix, to evaluate
our model after it is trained.
You can learn more about scikit-learn at https://scikitlearn.org/stable/.
Getting the data
The data for our model is available publicly and comes from the SMS spam
collection dataset from the UCI Machine Learning Repository [11]. The following
code will download the file and parse it to produce a list of SMS messages and their
corresponding labels:
def download_and_read(url):
local_file = url.split('/')[-1]
p = tf.keras.utils.get_file(local_file, url,
extract=True, cache_dir=".")
labels, texts = [], []
local_file = os.path.join("datasets", "SMSSpamCollection")
with open(local_file, "r") as fin:
for line in fin:
label, text = line.strip().split('\t')
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