def encode()

in models.py [0:0]

• 20 lines of code
• 5 McCabe index (conditional complexity)

    def encode(self, sentences, bsize=64, tokenize=True, verbose=False):
        tic = time.time()
        sentences, lengths, idx_sort = self.prepare_samples(
                        sentences, bsize, tokenize, verbose)

        embeddings = []
        for stidx in range(0, len(sentences), bsize):
            batch = self.get_batch(sentences[stidx:stidx + bsize])
            if self.is_cuda():
                batch = batch.cuda()
            with torch.no_grad():
                batch = self.forward((batch, lengths[stidx:stidx + bsize])).data.cpu().numpy()
            embeddings.append(batch)
        embeddings = np.vstack(embeddings)

        # unsort
        idx_unsort = np.argsort(idx_sort)
        embeddings = embeddings[idx_unsort]

        if verbose:
            print('Speed : %.1f sentences/s (%s mode, bsize=%s)' % (
                    len(embeddings)/(time.time()-tic),
                    'gpu' if self.is_cuda() else 'cpu', bsize))
        return embeddings