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]
