def load_pytorch_embedding_layer(pretrained_embedding: str, cache_dir=DEFAULT_CACHE_DIR, verbose=False): """ :param pretrained_embedding: :param cache_dir: the directory for storing cached models :return: an pytorch Embedding module and a list id2word """ word_embeddings_available(pretrained_embedding, can_use_subword=False) import torch from torch.nn import Embedding word_vectors = load_wv_with_gensim(pretrained_embedding, cache_dir=cache_dir, verbose=verbose) weights = torch.FloatTensor(word_vectors.vectors) return Embedding.from_pretrained(weights), word_vectors.index2word