Implement plot_embeddings codePython Here you will write a function to plot a set of 2D vectors in 2D space For graphs we will use Matplotlib plt

Here's an implementation of a plot_embeddings function in Python:

import matplotlib.pyplot as plt

def plot_embeddings(embeddings, labels):
    x = embeddings[:, 0]
    y = embeddings[:, 1]
    plt.figure(figsize=(10, 10))
    plt.scatter(x, y)
    for i, label in enumerate(labels):
        plt.annotate(label, (x[i], y[i]))
    plt.show()

This function takes two arguments: embeddings and labels. The embeddings argument is a numpy array of shape (n, 2) where n is the number of vectors to plot. Each row of the embeddings array represents a 2D vector. The labels argument is a list of n strings, where each string represents the label of the corresponding vector in the embeddings array.

The function first extracts the x and y coordinates of the embeddings array and creates a new matplotlib figure with a size of 10 by 10 inches. It then plots the points on a scatter plot using plt.scatter() function.

Finally, the function iterates over the labels list and annotates each point on the scatter plot with the corresponding label using plt.annotate() function.

To use this function, simply call it with the embeddings and labels arguments:

embeddings = np.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
labels = ['a', 'b', 'c']
plot_embeddings(embeddings, labels)

This will plot the three vectors (1.0, 2.0), (3.0, 4.0), and (5.0, 6.0) with labels 'a', 'b', and 'c', respectively