An implementation guide to Word2Vec using NumPy and Google Sheets
10 min read

An implementation guide to Word2Vec using NumPy and Google Sheets

An implementation guide to Word2Vec using NumPy and Google Sheets

Word2Vec is touted as one of the biggest, most recent breakthrough in the field of Natural Language Processing (NLP). The concept is simple, elegant and (relatively) easy to grasp. A quick Google search returns multiple results on how to use them with standard libraries such as Gensim and TensorFlow. Also, for the curious minds, check out the original implementation using C by Tomas Mikolov. The original paper can be found here too.

The main focus on this article is to present Word2Vec in detail. For that, I implemented Word2Vec on Python using NumPy (with much help from other tutorials) and also prepared a Google Sheet to showcase the calculations. Here are the links to the code and Google Sheet.

Fig. 1 — Step-by-step introduction to Word2Vec. Presented in code and Google Sheets


The objective of Word2Vec is to generate vector representations of words that carry semantic meanings for further NLP tasks. Each word vector is typically several hundred dimensions and each unique word in the corpus is assigned a vector in the space. For example, the word “happy” can be represented as a vector of 4 dimensions [0.24, 0.45, 0.11, 0.49] and “sad” has a vector of [0.88, 0.78, 0.45, 0.91].

The transformation from words to vectors is also known as word embedding. The reason for this transformation is so that machine learning algorithm can perform linear algebra operations on numbers (in vectors) instead of words.

To implement Word2Vec, there are two flavors to choose from — Continuous Bag-Of-Words (CBOW) or continuous Skip-gram (SG). In short, CBOW attempts to guess the output (target word) from its neighbouring words (context words) whereas continuous Skip-Gram guesses the context words from a target word. Effectively, Word2Vec is based on distributional hypothesis where the context for each word is in its nearby words. Hence, by looking at its neighbouring words, we can attempt to predict the target word.

According to Mikolov (quoted in this article), here is the difference between Skip-gram and CBOW:

Skip-gram: works well with small amount of the training data, represents well even rare words or phrases
CBOW: several times faster to train than the skip-gram, slightly better accuracy for the frequent words

To elaborate further, since Skip-gram learns to predict the context words from a given word, in case where two words (one appearing infrequently and the other more frequently) are placed side-by-side, both will have the same treatment when it comes to minimising loss since each word will be treated as both the target word and context word. Comparing that to CBOW, the infrequent word will only be part of a collection of context words used to predict the target word. Therefore, the model will assign the infrequent word a low probability.

Fig. 2 — Word2Vec — CBOW and skip-gram model architectures. Credit: IDIL

Implementation Process

In this article, we will be implementing the Skip-gram architecture. The content is broken down into the following parts for easy reading:

  1. Data Preparation — Define corpus, clean, normalise and tokenise words
  2. Hyperparameters — Learning rate, epochs, window size, embedding size
  3. Generate Training Data — Build vocabulary, one-hot encoding for words, build dictionaries that map id to word and vice versa
  4. Model Training — Pass encoded words through forward pass, calculate error rate, adjust weights using backpropagation and compute loss
  5. Inference — Get word vector and find similar words
  6. Further improvements — Speeding up training time with Skip-gram Negative Sampling (SGNS) and Hierarchical Softmax

1. Data Preparation

To begin, we start with the following corpus:

natural language processing and machine learning is fun and exciting

For simplicity, we have chosen a sentence without punctuation and capitalisation. Also, we did not remove stop words “and” and “is”.

In reality, text data are unstructured and can be “dirty”. Cleaning them will involve steps such as removing stop words, punctuations, convert text to lowercase (actually depends on your use-case), replacing digits, etc. KDnuggets has an excellent article on this process. Alternatively, Gensim also provides a function to perform simple text preprocessing using gensim.utils.simple_preprocess where it converts a document into a list of lowercase tokens, ignoring tokens that are too short or too long.

After preprocessing, we then move on to tokenising the corpus. Here, we tokenise our corpus on whitespace and the result is a list of words:

[“natural”, “language”, “processing”, “ and”, “ machine”, “ learning”, “ is”, “ fun”, “and”, “ exciting”]

2. Hyperparameters

Before we jump into the actual implementation, let us define some of the hyperparameters we need later.

[window_size]: As mentioned above, context words are words that are neighbouring the target word. But how far or near should these words be in order to be considered neighbour? This is where we define the window_size to be 2 which means that words that are 2 to the left and right of the target words are considered context words. Referencing Figure 3 below, notice that each of the word in the corpus will be a target word as the window slides.

FIg. 3 — With a window_size of 2, the target word is highlighted in orange and context words in green

[n]: This is the dimension of the word embedding and it typically ranges from 100 to 300 depending on your vocabulary size. Dimension size beyond 300 tends to have diminishing benefit (see page 1538 Figure 2 (a)). Do note that the dimension is also the size of the hidden layer.

[epochs]: This is the number of training epochs. In each epoch, we cycle through all training samples.

[learning_rate]: The learning rate controls the amount of adjustment made to the weights with respect to the loss gradient.

3. Generate Training Data

In this section, our main objective is to turn our corpus into a one-hot encoded representation for the Word2Vec model to train on. From our corpus, Figure 4 zooms into each of the 10 windows (#1 to #10) as shown below. Each window consists of both the target word and its context words, highlighted in orange and green respectively.

Fig. 4 — One-hot encoding for each target word and its context words

Example of the first and last element in the first and last training window is shown below:

# 1 [Target (natural)], [Context (language, processing)]
[list([1, 0, 0, 0, 0, 0, 0, 0, 0])
list([[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0, 0]])]
*****#2 to #9 removed****
#10 [Target (exciting)], [Context (fun, and)]
[list([0, 0, 0, 0, 0, 0, 0, 0, 1])
list([[0, 0, 0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0]])]

To generate the one-hot training data, we first initialise the word2vec() object and then using the object w2v to call the function generate_training_data by passing settings and corpus as arguments.

Inside the function generate_training_data, we performed the following operations:

  1. self.v_count — Length of vocabulary (note that vocabulary refers to the number of unique words in the corpus)
  2. self.words_list — List of words in vocabulary
  3. self.word_index — Dictionary with each key as word in vocabulary and value as index
  4. self.index_word — Dictionary with each key as index and value as word in vocabulary
  5. for loop to append one-hot representation for each target and its context words to training_data using word2onehot function.

4. Model Training

Fig. 5 — Word2Vec — skip-gram network architecture

With our training_data, we are now ready to train our model. Training starts with w2v.train(training_data) where we pass in the training data and call the function train.

The Word2Vec model consists of 2 weight matrices (w1 and w2) and for demo purposes, we have initialised the values to a shape of (9x10) and (10x9) respectively. This facilitates the calculation of backpropagation error which will be covered later in the article. In the actual training, you should randomly initialise the weights (e.g. using np.random.uniform()). To do that, comment line 9 and 10 and uncomment line 11 and 12.

Training — Forward Pass

Next, we start training our first epoch using the first training example by passing in w_t which represents the one-hot vector for target word to theforward_pass function. In the forward_pass function, we perform a dot product between w1 and w_t to produce h(Line 24). Then, we perform another dot product using w2 and h to produce the output layer u(Line 26). Lastly, we run u through softmax to force each element to the range of 0 and 1 to give us the probabilities for prediction (Line 28) before returning the vector for predictiony_pred, hidden layer h and output layer u.

I have attached some screenshots to show the calculation for the first training sample in the first window (#1) where the target word is ‘natural’ and context words are ‘language’ and ‘processing’. Feel free to look into the formula in the Google Sheet here.

Fig. 6— Calculate hidden layer, output later and softmax

Training — Error, Backpropagation and Loss

Error — With y_pred, h and u, we proceed to calculate the error for this particular set of target and context words. This is done by summing up the difference between y_pred and each of the context words inw_c.

Fig. 7 — Calculating Error — context words are ‘language’ and ‘processing’

Backpropagation — Next, we use the backpropagation function, backprop, to calculate the amount of adjustment we need to alter the weights using the function backprop by passing in error EI, hidden layer h and vector for target word w_t.

To update the weights, we multiply the weights to be adjusted (dl_dw1 and dl_dw2) with learning rate and then subtract it from the current weights (w1 and w2).

Fig. 8 — Backpropagation — Calculating delta for W1 and W2
Fig. 9 — Backpropagation — Adjusting weights to get updated W1 and W2

Loss — Lastly, we compute the overall loss after finishing each training sample according to the loss function. Take note that the loss function comprises of 2 parts. The first part is the negative of the sum for all the elements in the output layer (before softmax). The second part takes the number of the context words and multiplies the log of sum for all elements (after exponential) in the output layer.

Fig. 10 — Loss function for Word2Vec skip-gram. Credit:

5. Inferencing

Now that we have completed training for 50 epochs, both weights (w1 and w2) are now ready to perform inference.

Getting Vector for a Word

With a trained set of weights, the first thing we can do is to look at the word vector for a word in the vocabulary. We can simply do this by looking up the index of the word against the trained weight (w1). In the following example, we look up the vector for the word “machine”.

Find similar words

Another thing we can do is to find similar words. Even though our vocabulary is small, we can still implement the function vec_sim by computing the cosine similarity between words.

> w2v.vec_sim("machine", 3)
machine 1.0
fun 0.6223490454018772
and 0.5190154215400249

6. Further improvements

If you are still reading the article, well done and thank you! But this is not the end. As you might have noticed in the backpropagation step above, we are required to adjust the weights for all other words that were not involved in the training sample. This process can take up a long time if the size of your vocabulary is large (e.g. tens of thousands).

To solve this, below are the two features in Word2Vec you can implement to speed things up:

  • Skip-gram Negative Sampling (SGNS) helps to speed up training time and improve quality of resulting word vectors. This is done by training the network to only modify a small percentage of the weights rather than all of them. Recall in our example above, we update the weights for every other word and this can take a very long time if the vocab size is large. With SGNS, we only need to update the weights for the target word and a small number (e.g. 5 to 20) of random ‘negative’ words.
  • Hierarchical Softmax is also another trick to speed up training time replacing the original softmax. The main idea is that instead of evaluating all the output nodes to obtain the probability distribution, we only need to evaluate about log (based 2) of it. It uses a binary tree (Huffman coding tree) representation where the nodes in the output layer are represented as leaves and its nodes are represented in relative probabilities to its child nodes.
Fig. 11 — Hierarchical Binary Tree — Path from root to W2 is highlighted

Beyond that, why not try tweaking the code to implement the Continuous Bag-of-Words (CBOW) architecture? 😃


This article is an introduction to Word2Vec and into the world of word embedding. It is also worth noting that there are pre-trained embeddings available such as GloVe, fastText and ELMo where you can download and use directly. There are also extensions of Word2Vec such as Doc2Vec and the most recent Code2Vec where documents and codes are turned into vectors. 😉

Lastly, I want to thank to Ren Jie Tan, Raimi and Yuxin for taking time to comment and read the drafts of this. 💪