The+devil39s+double+2011+bluray+720p+11gb+dual+audio+upd

If you are legally backing up your own disc, a proper filename for local use might follow a standard scene or P2P naming convention, without infringing on others' copyright. Example structure:

The.Devils.Double.2011.720p.BluRay.x264.DTS.Dual-Audio.mkv the+devil39s+double+2011+bluray+720p+11gb+dual+audio+upd

But I won't produce a completed release name meant for indexing on torrent or usenet sites. If you are legally backing up your own

Using Pre-trained Embeddings: If using pre-trained embeddings (like Word2Vec or GloVe), you would look up each token in the respective database and get its vector representation. For simplicity, let's assume we're working with a hypothetical pre-trained model. But I won't produce a completed release name

Combine Vectors: There are multiple ways to combine these vectors (e.g., averaging, concatenation). A simple method is to average the vectors.

import numpy as np
# Hypothetical pre-trained word embeddings for simplicity
word_embeddings = 
    "The": np.array([0.1, 0.2]),
    "Devil's": np.array([0.3, 0.4]),
    "Double": np.array([0.5, 0.6]),
    "2011": np.array([0.2, 0.1]),
    "Blu-ray": np.array([0.4, 0.5]),
    "720p": np.array([0.6, 0.7]),
    "11GB": np.array([0.7, 0.6]),
    "Dual": np.array([0.8, 0.9]),
    "Audio": np.array([0.9, 1.0]),
def get_deep_feature(topic):
    tokens = topic.replace("2011", " ").replace("Blu-ray", " ").replace("720p", " ").replace("11GB", " ").replace("Dual", " ").replace("Audio", " ").split()
    vector_sum = np.zeros(2)  # Assuming 2D vectors for simplicity
    count = 0
    for token in tokens:
        if token in word_embeddings:
            vector_sum += word_embeddings[token]
            count += 1
    if count > 0:
        return vector_sum / count
    else:
        return np.zeros(2)
topic = "The Devil's Double 2011 Blu-ray 720p 11GB Dual Audio"
deep_feature = get_deep_feature(topic)
print("Deep Feature:", deep_feature)