def load(cls,
model_path: str,
parameter_path: str,
batch_size: int = 32,
use_cudnn: bool = False):
"""
Load a NPAnnotator annotator
Args:
model_path (str): path to trained model
parameter_path (str): path to model parameters
batch_size (int, optional): inference batch_size
use_cudnn (bool, optional): use gpu for inference (cudnn cells)
Returns:
NPAnnotator class with loaded model
"""
_model_path = path.join(path.dirname(path.realpath(__file__)),
model_path)
validate_existing_filepath(_model_path)
_parameter_path = path.join(path.dirname(path.realpath(__file__)),
parameter_path)
validate_existing_filepath(_parameter_path)
model = SequenceChunker(use_cudnn=use_cudnn)
model.load(_model_path)
with open(_parameter_path, "rb") as fp:
model_params = pickle.load(fp)
word_vocab = model_params["word_vocab"]
chunk_vocab = model_params["chunk_vocab"]
char_vocab = model_params.get("char_vocab", None)
return cls(model, word_vocab, char_vocab, chunk_vocab, batch_size)
train_features = [words_train, char_train]
test_features = [words_test, char_test]
else:
train_features = words_train
test_features = words_test
train_labels = [pos_train, chunk_train]
test_labels = [pos_test, chunk_test]
chunk_f1_cb = ConllCallback(test_features,
chunk_test,
dataset.chunk_vocab.vocab,
batch_size=64)
model.fit(train_features,
train_labels,
epochs=args.e,
batch_size=args.b,
validation_data=(test_features, test_labels),
callbacks=[chunk_f1_cb])
# save model
_save_model()
# load model
model = SequenceChunker(use_cudnn=args.use_cudnn)
model.load(model_path)
# print evaluation metric
chunk_pred = model.predict(test_features, 64)
res = get_conll_scores(chunk_pred, chunk_test,
dataset.chunk_vocab.reverse_vocab())
print(res)