def init_word_embs_model(model_path, model_type, force_reload=False, top_k=None): global WORD_EMBS_MODELS if model_type in WORD_EMBS_MODELS and not force_reload: WORD_EMBS_MODELS[model_type].top_k = top_k return WORD_EMBS_MODELS[model_type] if model_type == 'word2vec': model = nmw.Word2vec(top_k=top_k) model.read(model_path) elif model_type == 'glove': model = nmw.GloVe(top_k=top_k) model.read(model_path) elif model_type == 'fasttext': model = nmw.Fasttext(top_k=top_k) model.read(model_path) else: raise ValueError( 'Model type value is unexpected. Expected values include {}'. format(model_types)) WORD_EMBS_MODELS[model_type] = model return model
def init_glove_model(model_path, force_reload=False): # Load model once at runtime global GLOVE_MODEL if GLOVE_MODEL and not force_reload: return GLOVE_MODEL glove = nmw.GloVe() glove.read(model_path) GLOVE_MODEL = glove return GLOVE_MODEL
def init_glove_model(model_path, force_reload=False, top_k=None): # Load model once at runtime global GLOVE_MODEL if model_path in GLOVE_MODEL and not force_reload: GLOVE_MODEL[model_path].top_k = top_k return GLOVE_MODEL[model_path] glove = nmw.GloVe(top_k=top_k) glove.read(model_path) GLOVE_MODEL[model_path] = glove return GLOVE_MODEL[model_path]