def model_save():
model = TF2BertModel(9, 128, 'models/albert_crf')
model.load()
vocab = TF2Tokenizer()
export_path = 'models/albert_crf/saved_model/1'
encoded = model.predict(vocab.tokenize_to_ids(['你好！'], model.max_seq_len))
tf.saved_model.save(model,
export_path,
signatures=model.call.get_concrete_function(
tf.TensorSpec(shape=[None, 2, 128],
dtype=tf.int32,
name="input")))
model.save(os.path.join(model.model_dir, 'saved_model'), save_format='tf')
np.save('models/albert_crf/transition.npy',
model.transition_params.numpy())
outs = tf.nn.softmax(x)
# outs = tf.nn.softmax(x) * mask
return outs
def load_pretrained(self, pretrained_ckpt):
self.build(input_shape=(None, 2, self._max_seq_len))
bert.load_albert_weights(self.l_bert, pretrained_ckpt)
def load(self, weight_file):
self.build(input_shape=(None, 2, self._max_seq_len))
self.load_weights(weight_file)
if __name__ == '__main__':
model = TF2BertModel(9, 128, 'models/alBERT_only_NER')
model.load(os.path.join(model.model_dir, 'ckpt_best'))
# model.load_pretrained('models/albert_base_zh/albert_model.ckpt')
vocab = TF2Tokenizer()
encoded = model.predict(
vocab.tokenize_to_ids(['马云，祖籍浙江省绍兴嵊州市谷来镇，后父母移居杭州，出生于浙江省杭州市，中国企业家，中国共产党党员。曾为亚洲首富、阿里巴巴集团董事局主席。'], model.max_seq_len))
print(encoded.shape)
print(np.argmax(encoded, axis=-1))
# from losses import masked_sparse_categorical_crossentropy
#
# label = np.argmax(encoded, axis=-1)
# label[label == 8] = 9
# print(masked_sparse_categorical_crossentropy(tf.convert_to_tensor(label),
# tf.convert_to_tensor(encoded)))