Condition for Implemant Average Word2Vec using Gensim
Description: Average Word2Vec refers to a technique where the vector representations (embeddings) of two or more words are averaged to create a single vector. This method is commonly used to capture the semantic meaning of a sequence of words or a phrase by combining the individual word embeddings into one. The average vector can be useful for sentence-level or context-level representations, as it aggregates the information from multiple words into a fixed-size vector.
Step-by-Step Process
Word2Vec Training: First, a Word2Vec model is trained on a small corpus of sentences. This model learns to represent each word in the vocabulary as a dense, continuous-valued vector(embedding) based on the context in which the word appears.
Vector Calculation: Average After training, the code computes the average vector for pairs of consecutive words in a given sentence.For example, in the sentence ["word2vec", "models", "convert", "words"], it computes: • The average vector for the words 'word2vec' and 'models'. • The average vector for the words 'models' and 'convert'. • The average vector for the words 'convert' and 'words'.
Storing Results: These average vectors are stored in a dictionary with the word pairs as keys and the average vectors as values.
Output: Finally, it prints the average vectors for each consecutive word pair in the sentence.
Sample Code
from gensim.models import Word2Vec
import numpy as np
# Example sentences for training the Word2Vec model
sentences = [
["this", "is", "a", "simple", "example"],
["word2vec", "models", "convert", "words", "to", "vectors"],
["average", "word2vec", "represents", "sentences"]
]
# Step 1: Train the Word2Vec model
model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4,
epochs=10)
# Step 2: Function to compute average Word2Vec for two consecutive words
def average_word2vec(word1, word2, model):
if word1 in model.wv and word2 in model.wv:
# Get the Word2Vec vectors for the words
vector1 = model.wv[word1]
vector2 = model.wv[word2]
# Compute the average vector for the consecutive words
avg_vector = (vector1 + vector2) / 2
return avg_vector
else:
return np.zeros(model.vector_size) # Return a zero vector if any word is missing
# Step 3: Function to calculate the average Word2Vec for consecutive word pairs
def calculate_consecutive_avg_word_vectors(sentence, model):
avg_vectors = {}
for i in range(len(sentence) - 1): # Loop through the sentence, but skip the last word
word1 = sentence[i]
word2 = sentence[i + 1]
# Compute the average Word2Vec vectors for the consecutive words
avg_vector = average_word2vec(word1, word2, model)
# Store the average vector in the dictionary
avg_vectors[(word1, word2)] = avg_vector
return avg_vectors
# Example sentence to calculate the average vectors between consecutive words
example_sentence = ["word2vec", "models", "convert", "words"]
avg_vectors = calculate_consecutive_avg_word_vectors(example_sentence, model)
# Print the average vectors between consecutive words
print(f"Average Word2Vec vectors for consecutive words in sentence: '{' '.join(example_sentence)}'")
for (word1, word2), avg_vector in avg_vectors.items():
print(f"Average vector between '{word1}' and '{word2}':\n{avg_vector}")
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