GloVe Word-Embeddings with Google Colaboratory and Go Programming Language

Are you interested in natural language processing and want to get started with the Go programming language? In this tutorial, we will go through the steps of using pre-trained GloVe word-embeddings in Go using Google Colaboratory.

What is GloVe?

GloVe stands for "Global Vectors for Word Representation" and is a technique for creating vector representations of words based on their co-occurrence statistics in a corpus. GloVe embeddings have been trained on large-scale text datasets and are widely used in natural language processing tasks such as sentiment analysis, text classification, and machine translation.

How to Use GloVe with Google Colaboratory and Go?

Google Colaboratory, or Colab for short, is a free online platform for executing Python code in a Jupyter Notebook environment. Colab provides access to powerful computing resources, including GPU and TPU, which can be used to train and test machine learning models.

To use GloVe embeddings with Go in Colab, follow these steps:

Step 1: Create a New Notebook in Colab

Go to https://colab.research.google.com/ and create a new notebook.

Step 2: Import the Required Libraries

To use the pre-trained GloVe embeddings in Go, we will need to install the Go bindings for the Python h5py library, which is used to read the embeddings file. We can do this by running the following commands in a code cell:

!apt-get install python3-dev
!pip install h5py
!go get github.com/danieldk/golinear
!go get github.com/sbinet/go-python

Next, we can import the required Go packages:

import (
    "bufio"
    "fmt"
    "os"
    "strings"

    "github.com/danieldk/golinear"
    "github.com/sbinet/go-python"
)

Step 3: Load the GloVe Embeddings

The pre-trained GloVe embeddings can be downloaded from the Stanford NLP website at https://nlp.stanford.edu/projects/glove/. We can use the 100-dimensional embeddings trained on the Wikipedia 2014 + Gigaword 5 dataset, which have a file size of around 822 MB.

We can download the embeddings file using the following command in a code cell:

!wget http://nlp.stanford.edu/data/glove.6B.zip -O /tmp/glove.6B.zip
!unzip -q /tmp/glove.6B.zip -d /tmp/glove.6B/

After we have downloaded and extracted the embeddings file, we can load it into a Go map using the h5go library as follows:

func loadGloveEmbeddings(file string) map[string][]float64 {
    // Open the embeddings file.
    h5file, err := h5.OpenFile(file, h5.F_ACC_RDONLY)
    if err != nil {
        panic(fmt.Errorf("could not open file %q: %v", file, err))
    }
    defer h5file.Close()

    // Get the embedding matrix.
    embeddings, err := h5file.OpenDataset("embedding/embeddings")
    if err != nil {
        panic(fmt.Errorf("could not open dataset 'embedding/embeddings': %v", err))
    }
    defer embeddings.Close()

    // Convert the embedding matrix to a Go map.
    map_ := make(map[string][]float64)
    rows, cols := embeddings.Dims()
    slice_ := make([]float64, rows*cols)
    embeddings.Read(&slice_)
    for i := 0; i < rows; i++ {
        start := i * cols
        end := start + cols
        key := strings.TrimSpace(h5file.DecodeAttribute(embeddings.AttributeNames()[i]))
        map_[key] = slice_[start:end]
    }

    return map_
}

Step 4: Use the GloVe Embeddings

Now that we have loaded the GloVe embeddings into a Go map, we can use them to create vector representations of words in our text data. For example, the following function takes a string of text and returns a matrix of GloVe embeddings for the words in the text:

func computeEmbeddings(text string, embeddings map[string][]float64) [][]float64 {
    // Split the text into words.
    words := strings.Split(strings.ToLower(text), " ")
    // Create a matrix to hold the GloVe embeddings for the words.
    embeddings_ := make([][]float64, 0, len(words))
    // Compute the embedding for each word.
    for _, word := range words {
        embedding, ok := embeddings[word]
        if ok {
            embeddings_ = append(embeddings_, embedding)
        }
    }
    return embeddings_
}

Step 5: Train a Linear Classifier on the GloVe Embeddings

We can use the GloVe embeddings as features for a linear classifier to predict the sentiment of text data. For example, we can train a linear support vector machine (SVM) on the GloVe embeddings to classify movie reviews as positive or negative.

The following function trains a linear SVM on a dataset of movie reviews:

func trainSVM(dataset string, embeddings map[string][]float64) *golinear.Model {
    // Open the dataset file.
    file, err := os.Open(dataset)
    if err != nil {
        panic(fmt.Errorf("could not open dataset %q: %v", dataset, err))
    }
    defer file.Close()

    // Initialize the SVM model.
    model := golinear.NewCModel(golinear.NewL2R_L2LOSS_SVC_DUAL)

    // Parse the dataset and extract the GloVe embeddings for the text.
    scanner := bufio.NewScanner(file)
    for scanner.Scan() {
        line := scanner.Text()
        parts := strings.SplitN(line, "\t", 2)
        label, text := parts[0], parts[1]
        embeddings_ := computeEmbeddings(text, embeddings)
        // Convert the embeddings matrix to a 1D slice.
        var features []float64
        for _, row := range embeddings_ {
            for _, value := range row {
                features = append(features, value)
            }
        }
        // Add the features to the SVM model.
        x, err := golinear.NewSparseNodeSetSparseFeatures(features, golinear.L2R_L2LOSS_SVC_DUAL)
        if err != nil {
            panic(fmt.Errorf("could not create sparse node: %v", err))
        }
        y, err := strconv.Atoi(label)
        if err != nil {
            panic(fmt.Errorf("could not convert label %q to int: %v", label, err))
        }
        model.AddExample(x, y)
    }

    // Train the SVM model and return it.
    parameters := golinear.NewParameter()
    parameters.SetC(1.0)
    model.Train(parameters)
    return model
}

Conclusion

In this tutorial, we have shown how to use pre-trained GloVe word-embeddings in Go using Google Colaboratory. We demonstrate how to load the embeddings into a Go map and use them to create vector representations of words in text data. We also show how to train a linear classifier on the GloVe embeddings using the Go bindings for the liblinear library. With this knowledge, you can start building your own natural language processing applications in Go.