def __call__(self, vocab, name=None):
        t = time()
        w2v_vocab = joblib.load(os.path.join(self.data_dir, '3m_w2v_gn_vocab.jl'))
        w2v_embed = joblib.load(os.path.join(self.data_dir, '3m_w2v_gn.jl'))
        mapping = {}
        for i, w in enumerate(w2v_vocab):
            w = w.lower()
            if w in mapping:
                mapping[w].append(i)
            else:
                mapping[w] = [i]
        widxs = []
        w2vidxs = []
        for i, w in enumerate(vocab):
            w = w.replace('`', "'")
            if w in mapping:
                w2vi = min(mapping[w])
                w2vidxs.append(w2vi)
                widxs.append(i)
        # w = np_rng.uniform(low=-0.05, high=0.05, size=(len(vocab), w2v_embed.shape[1]))
        w = np.zeros((len(vocab), w2v_embed.shape[1]))
        w[widxs, :] = w2v_embed[w2vidxs, :]/2.
        return sharedX(w, name=name)
dataset/ETHPy150Open IndicoDataSolutions/Foxhound/foxhound/inits.py/W2VEmbedding.__call__/original