custom_tensorflow_keras_nlp/util/preprocessing.py [102:111]:
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                coefs = np.asarray(values[1:], dtype="float32")
                embeddings_index[word] = coefs
    print("Loaded {} of {} word vectors for tokenizer vocabulary length {}".format(
        len(embeddings_index),
        emb_vocab_size,
        tokenizer_vocab_size,
    ))

    # create a weight matrix for words in training docs
    embedding_matrix = np.zeros((tokenizer_vocab_size, emb_d))
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pytorch_alternatives/custom_pytorch_nlp/util/preprocessing.py [128:137]:
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                coefs = np.asarray(values[1:], dtype="float32")
                embeddings_index[word] = coefs
    print("Loaded {} of {} word vectors for tokenizer vocabulary length {}".format(
        len(embeddings_index),
        emb_vocab_size,
        tokenizer_vocab_size,
    ))

    # create a weight matrix for words in training docs
    embedding_matrix = np.zeros((tokenizer_vocab_size, emb_d))
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