custom_tensorflow_keras_nlp/util/preprocessing.py [84:99]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    if wait_for_file_stable(vecs_gz_filepath):
        print("Using existing embeddings file")
    else:
        print("Downloading word vectors...")
        subprocess.run([" ".join(["wget", "-NP", folder, vecs_url])], check=True, shell=True)

    print("Loading into memory...")
    embeddings_index = dict()
    with gzip.open(vecs_gz_filepath, "rt") as zipf:
        firstline = zipf.readline()
        emb_vocab_size, emb_d = firstline.split(" ")
        emb_vocab_size = int(emb_vocab_size)
        emb_d = int(emb_d)
        for line in zipf:
            values = line.split()
            word = values[0]
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



pytorch_alternatives/custom_pytorch_nlp/util/preprocessing.py [110:125]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    if wait_for_file_stable(vecs_gz_filepath):
        print("Using existing embeddings file")
    else:
        print("Downloading word vectors...")
        subprocess.run([" ".join(["wget", "-NP", folder, vecs_url])], check=True, shell=True)

    print("Loading into memory...")
    embeddings_index = dict()
    with gzip.open(vecs_gz_filepath, "rt") as zipf:
        firstline = zipf.readline()
        emb_vocab_size, emb_d = firstline.split(" ")
        emb_vocab_size = int(emb_vocab_size)
        emb_d = int(emb_d)
        for line in zipf:
            values = line.split()
            word = values[0]
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -