!uv pip install gensim numpy matplotlib scikit-learn -qWord2Vec
This notebook explores Word2Vec embeddings to understand how they capture semantic relationships.
Uses pre-trained embeddings from Google News (trained on ~100 billion words).
Install required packages
Load pretrained Word2Vec model
import gensim.downloader as api
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
import warnings
warnings.filterwarnings('ignore')
# Load pre-trained Word2Vec model (Google News, 300-dimensional vectors)
print("Loading Word2Vec model...")
model = api.load('word2vec-google-news-300')
print(f"Model loaded. Vocabulary size: {len(model)} words")
print(f"Vector dimension: {model.vector_size}") # type: ignoreLoading Word2Vec model...
Model loaded. Vocabulary size: 3000000 words
Vector dimension: 300word = "cat"
vector = model[word]
vector[:10]array([ 0.0123291 , 0.20410156, -0.28515625, 0.21679688, 0.11816406,
0.08300781, 0.04980469, -0.00952148, 0.22070312, -0.12597656],
dtype=float32)word = "dog"
vector = model[word]
vector[:10]array([ 0.05126953, -0.02233887, -0.17285156, 0.16113281, -0.08447266,
0.05737305, 0.05859375, -0.08251953, -0.01538086, -0.06347656],
dtype=float32)word = "pizza"
vector = model[word]
vector[:10]array([-1.2597656e-01, 2.5390625e-02, 1.6699219e-01, 5.5078125e-01,
-7.6660156e-02, 1.2890625e-01, 1.0253906e-01, -3.9482117e-04,
1.2158203e-01, 4.3212891e-02], dtype=float32)Find similar words
Words with similar meanings have similar vectors.
def find_similar_words(word, top_n=10):
"""Find the most similar words to a given word."""
try:
similar = model.most_similar(word, topn=top_n) # type: ignore
print(f"\nWords most similar to '{word}':")
print("-" * 40)
for similar_word, similarity in similar:
print(f"{similar_word:20s} | similarity: {similarity:.4f}")
except KeyError:
print(f"Word '{word}' not in vocabulary")find_similar_words("cat")
find_similar_words("dog")
find_similar_words("pizza")
Words most similar to 'cat':
----------------------------------------
cats | similarity: 0.8099
dog | similarity: 0.7609
kitten | similarity: 0.7465
feline | similarity: 0.7326
beagle | similarity: 0.7151
puppy | similarity: 0.7075
pup | similarity: 0.6934
pet | similarity: 0.6892
felines | similarity: 0.6756
chihuahua | similarity: 0.6710
Words most similar to 'dog':
----------------------------------------
dogs | similarity: 0.8680
puppy | similarity: 0.8106
pit_bull | similarity: 0.7804
pooch | similarity: 0.7627
cat | similarity: 0.7609
golden_retriever | similarity: 0.7501
German_shepherd | similarity: 0.7465
Rottweiler | similarity: 0.7438
beagle | similarity: 0.7419
pup | similarity: 0.7407
Words most similar to 'pizza':
----------------------------------------
pizzas | similarity: 0.7863
Domino_pizza | similarity: 0.7343
Pizza | similarity: 0.6988
pepperoni_pizza | similarity: 0.6903
sandwich | similarity: 0.6840
burger | similarity: 0.6570
sandwiches | similarity: 0.6495
takeout_pizza | similarity: 0.6492
gourmet_pizza | similarity: 0.6401
meatball_sandwich | similarity: 0.6377Compute similarity
def compute_similarity(word1, word2):
"""Compute cosine similarity between two words."""
try:
similarity = model.similarity(word1, word2) # type: ignore
print(f"Similarity between '{word1}' and '{word2}': {similarity:.4f}")
except KeyError as e:
print(f"Word not in vocabulary: {e}")compute_similarity('cat', 'dog')
compute_similarity('cat', 'kitten')
compute_similarity('cat', 'car')
compute_similarity('doctor', 'hospital')
compute_similarity('king', 'queen')Similarity between 'cat' and 'dog': 0.7609
Similarity between 'cat' and 'kitten': 0.7465
Similarity between 'cat' and 'car': 0.2153
Similarity between 'doctor' and 'hospital': 0.5143
Similarity between 'king' and 'queen': 0.6511Vector arithmetic
def vector_arithmetic(positive, negative, top_n=5):
"""Perform vector arithmetic: positive words - negative words."""
try:
result = model.most_similar(positive=positive, negative=negative, topn=top_n) # type: ignore
print(f"\n{' + '.join(positive)} - {' - '.join(negative)}:")
print("-" * 50)
for word, similarity in result:
print(f"{word:20s} | similarity: {similarity:.4f}")
except KeyError as e:
print(f"Word not in vocabulary: {e}")vector_arithmetic(['king', 'woman'], ['man'])
vector_arithmetic(['Paris', 'Italy'], ['France'])
vector_arithmetic(['walking', 'swim'], ['walk'])
king + woman - man:
--------------------------------------------------
queen | similarity: 0.7118
monarch | similarity: 0.6190
princess | similarity: 0.5902
crown_prince | similarity: 0.5499
prince | similarity: 0.5377
Paris + Italy - France:
--------------------------------------------------
Milan | similarity: 0.7222
Rome | similarity: 0.7028
Palermo_Sicily | similarity: 0.5968
Italian | similarity: 0.5911
Tuscany | similarity: 0.5633
walking + swim - walk:
--------------------------------------------------
swimming | similarity: 0.8246
swam | similarity: 0.6807
swims | similarity: 0.6538
swimmers | similarity: 0.6495
paddling | similarity: 0.64242D visualization
def visualize_words(words, method='tsne'):
"""Visualize word embeddings in 2D."""
# Get vectors for words that exist in vocabulary
valid_words = [w for w in words if w in model]
if len(valid_words) < 2:
print("Need at least 2 valid words to visualize")
return
vectors = np.array([model[w] for w in valid_words])
# Reduce to 2D
if method == 'tsne':
reducer = TSNE(n_components=2, random_state=42, perplexity=min(5, len(valid_words)-1))
else:
reducer = PCA(n_components=2, random_state=42)
vectors_2d = reducer.fit_transform(vectors)
# Plot
plt.figure(figsize=(12, 8))
plt.scatter(vectors_2d[:, 0], vectors_2d[:, 1], s=200, alpha=0.6)
for i, word in enumerate(valid_words):
plt.annotate(word,
xy=(vectors_2d[i, 0], vectors_2d[i, 1]),
xytext=(5, 5),
textcoords='offset points',
fontsize=12,
fontweight='bold')
plt.title(f'Word Embeddings Visualization ({method.upper()})', fontsize=16)
plt.xlabel('Dimension 1')
plt.ylabel('Dimension 2')
plt.grid(True, alpha=0.3)
plt.tight_layout()
plt.show()
words = ['cat', 'dog', 'kitten', 'puppy', 'lion', 'tiger', 'elephant', 'mouse', 'chicken', 'rat']
visualize_words(words)