Audio 720p | Mission Impossible 4 Ghost Protocol Dual

# Training a simple Word2Vec model model = Word2Vec(sentences, vector_size=100, min_count=1)

# Getting a vector for a word def get_word_vector(word): try: return model.wv[word] except KeyError: return np.zeros(100) # Default vector for out-of-vocabulary words Mission Impossible 4 Ghost Protocol Dual Audio 720p

# Concatenate all vectors for a deep feature deep_feature = np.concatenate([title_vector, genre_vector, resolution_vector, audio_vector, part_of_series_vector]) # Training a simple Word2Vec model model =

print(deep_feature) This example simplifies many aspects and is intended to illustrate the process. Real-world applications might use more sophisticated models (like BERT for text embeddings) and incorporate additional metadata. Mission Impossible 4 Ghost Protocol Dual Audio 720p

import numpy as np from gensim.models import Word2Vec

# Example list of sentences (pre-tokenized) sentences = [["Mission", "Impossible", "4", "Ghost", "Protocol", "Dual", "Audio", "720p"]]