Label Dendrogram Clusters after Latent Semantic Analysis

Question

I'm trying to create a dendrogram that looks like this, i.e. that has words a the bottom which correspond to each cluster.

import pandas as pd
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import AgglomerativeClustering
from sklearn.decomposition import TruncatedSVD
import numpy as np

# Data set
url = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/mtcars.csv'
df = pd.read_csv(url)
df = df.set_index('model')

# Calculate the distance between each sample
Z = linkage(df, 'ward')

# Plot with Custom leaves
dendrogram(Z, leaf_rotation=90, leaf_font_size=8, labels=df.index)

# Show the graph
plt.show()

However I'm doing this for a large natural language dataset, so I'm trying to reduce the dataset with LSA using TruncatedSVD; otherwise my machine crashes.

Following the scikit learn documentation here I can plot a dendrogram but the labels correspond to the number of counts of samples under each node. I would like a cluster label for each node instead that I can read as in the motorcars example.

How do I do that? Would really appreciate any help/insight.

Code for where I've got to so far; note I'm not working with the IMDB dataset, it's just as an example dataset to make the code replicable.

df = pd.read_csv(
    'https://raw.githubusercontent.com/peetck/IMDB-Top1000-Movies/master/IMDB-Movie-Data.csv'
)

vectorizer = TfidfVectorizer(stop_words='english', max_features=1000)
X = vectorizer.fit_transform(df.Description)
TSVD = TruncatedSVD(n_components=10)
X_reduced = TSVD.fit_transform(X)
model = AgglomerativeClustering(distance_threshold=0, n_clusters=None)
model = model.fit(X_reduced)


def plot_dendrogram(model, **kwargs):
    # Create linkage matrix and then plot the dendrogram
    # create the counts of samples under each node
    counts = np.zeros(model.children_.shape[0])
    n_samples = len(model.labels_)
    for i, merge in enumerate(model.children_):
        current_count = 0
        for child_idx in merge:
            if child_idx < n_samples:
                current_count += 1  # leaf node
            else:
                current_count += counts[child_idx - n_samples]
        counts[i] = current_count

    linkage_matrix = np.column_stack(
        [model.children_, model.distances_, counts]).astype(float)

    # Plot the corresponding dendrogram
    out = dendrogram(linkage_matrix, **kwargs)


plot_dendrogram(model, p=3, truncate_mode='level', leaf_rotation=90)