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This tutorial contains an introduction to word embeddings. You will train your own word embeddings using a simple Keras model for a sentiment classification task, and then visualize them in the Embedding Projector (shown in the image below).
Representing text as numbers
Machine learning models take vectors (arrays of numbers) as input. When working with text, the first thing you must do is come up with a strategy to convert strings to numbers (or to "vectorize" the text) before feeding it to the model. In this section, you will look at three strategies for doing so.
One-hot encodings
As a first idea, you might "one-hot" encode each word in your vocabulary. Consider the sentence "The cat sat on the mat". The vocabulary (or unique words) in this sentence is (cat, mat, on, sat, the). To represent each word, you will create a zero vector with length equal to the vocabulary, then place a one in the index that corresponds to the word. This approach is shown in the following diagram.
To create a vector that contains the encoding of the sentence, you could then concatenate the one-hot vectors for each word.
Encode each word with a unique number
A second approach you might try is to encode each word using a unique number. Continuing the example above, you could assign 1 to "cat", 2 to "mat", and so on. You could then encode the sentence "The cat sat on the mat" as a dense vector like [5, 1, 4, 3, 5, 2]. This approach is efficient. Instead of a sparse vector, you now have a dense one (where all elements are full).
There are two downsides to this approach, however:
The integer-encoding is arbitrary (it does not capture any relationship between words).
An integer-encoding can be challenging for a model to interpret. A linear classifier, for example, learns a single weight for each feature. Because there is no relationship between the similarity of any two words and the similarity of their encodings, this feature-weight combination is not meaningful.
Word embeddings
Word embeddings give us a way to use an efficient, dense representation in which similar words have a similar encoding. Importantly, you do not have to specify this encoding by hand. An embedding is a dense vector of floating point values (the length of the vector is a parameter you specify). Instead of specifying the values for the embedding manually, they are trainable parameters (weights learned by the model during training, in the same way a model learns weights for a dense layer). It is common to see word embeddings that are 8-dimensional (for small datasets), up to 1024-dimensions when working with large datasets. A higher dimensional embedding can capture fine-grained relationships between words, but takes more data to learn.
Above is a diagram for a word embedding. Each word is represented as a 4-dimensional vector of floating point values. Another way to think of an embedding is as "lookup table". After these weights have been learned, you can encode each word by looking up the dense vector it corresponds to in the table.
Setup
import io
import os
import re
import shutil
import string
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense, Embedding, GlobalAveragePooling1D
from tensorflow.keras.layers import TextVectorization
2024-07-19 12:44:24.531008: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-07-19 12:44:24.551991: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-07-19 12:44:24.558387: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
Download the IMDb Dataset
You will use the Large Movie Review Dataset through the tutorial. You will train a sentiment classifier model on this dataset and in the process learn embeddings from scratch. To read more about loading a dataset from scratch, see the Loading text tutorial.
Download the dataset using Keras file utility and take a look at the directories.
url = "https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
dataset = tf.keras.utils.get_file("aclImdb_v1.tar.gz", url,
untar=True, cache_dir='.',
cache_subdir='')
dataset_dir = os.path.join(os.path.dirname(dataset), 'aclImdb')
os.listdir