def test_extract_embeddings_invalid_pooling(self):
with self.assertRaises(ValueError):
extract_embeddings(
self.model_path,
[
('all work and no play', 'makes jack a dull boy'),
('makes jack a dull boy', 'all work and no play'),
],
poolings=['invalid'],
)
def test_extract_embeddings_default(self):
embeddings = extract_embeddings(
self.model_path,
['all work and no play', 'makes jack a dull boy~'])
self.assertEqual(2, len(embeddings))
self.assertEqual((7, 4), embeddings[0].shape)
self.assertEqual((8, 4), embeddings[1].shape)
def test_extract_embeddings_pair(self):
embeddings = extract_embeddings(
self.model_path,
[
('all work and no play', 'makes jack a dull boy'),
('makes jack a dull boy', 'all work and no play'),
],
)
self.assertEqual(2, len(embeddings))
self.assertEqual((13, 4), embeddings[0].shape)
def test_extract_embeddings_single_pooling(self):
embeddings = extract_embeddings(
self.model_path,
[
('all work and no play', 'makes jack a dull boy'),
('makes jack a dull boy', 'all work and no play'),
],
poolings=POOL_NSP,
)
self.assertEqual(2, len(embeddings))
self.assertEqual((4, ), embeddings[0].shape)
def test_extract_embeddings_multi_pooling(self):
embeddings = extract_embeddings(
self.model_path,
[
('all work and no play', 'makes jack a dull boy'),
('makes jack a dull boy', 'all work and no play'),
],
poolings=[POOL_NSP, POOL_MAX, POOL_AVE],
output_layer_num=2,
)
self.assertEqual(2, len(embeddings))
self.assertEqual((24, ), embeddings[0].shape)
def test_extract_embeddings_variable_lengths(self):
tokens = [
'[PAD]',
'[UNK]',
'[CLS]',
'[SEP]',
'all',
'work',
'and',
'no',
'play',
'makes',
'jack',
'a',
'dull',
'boy',
'~',
]
token_dict = {token: i for i, token in enumerate(tokens)}
inputs, outputs = get_model(
token_num=len(tokens),
pos_num=20,
seq_len=None,
embed_dim=13,
transformer_num=1,
feed_forward_dim=17,
head_num=1,
training=False,
)
model = keras.models.Model(inputs, outputs)
embeddings = extract_embeddings(
model,
[
('all work and no play', 'makes jack'),
('a dull boy', 'all work and no play and no play'),
],
vocabs=token_dict,
batch_size=2,
)
self.assertEqual(2, len(embeddings))
self.assertEqual((10, 13), embeddings[0].shape)
self.assertEqual((14, 13), embeddings[1].shape)
def test_extract_embeddings_from_file(self):
with codecs.open(os.path.join(self.model_path, 'vocab.txt'), 'r',
'utf8') as reader:
texts = map(lambda x: x.strip(), reader)
embeddings = extract_embeddings(self.model_path, texts)
self.assertEqual(15, len(embeddings))
from bertTAT.bert import extract_embeddings
from bertTAT.bert import load_trained_model_from_checkpoint
import numpy as np
import os, codecs
# 1. 提取预训练模型文件的路径
now_path = os.path.dirname(__file__)
pretrained_path = now_path + "/../pretrained_model/chinese_L-12_H-768_A-12"
# 1. 如果不需要微调，只想提取词/句子的特征，如提取每个句子对应的全部词的特征
texts = ["世上无难事", '只要肯攀登!']
embeddings = extract_embeddings(pretrained_path, texts)
print("embedding:", np.array(embeddings[0]).shape)
# 2. 输入是成对的句子，想使用最后4层特征，且提取NSP位位置输出和max-pooling的结果
# 输出结果中不再包含词的特征，NSP和max-pooling的输出会拼接在一起，每个numpy数组的大小为(768 x 4 x 2,)
from bertTAT.bert import extract_embeddings, POOL_NSP, POOL_MAX
texts = [('公司加班很严重', '但也要保持学习！'), ('算法学习', '永不止步。')]
embeddings = extract_embeddings(pretrained_path,
texts,
output_layer_num=4,
poolings=[POOL_NSP, POOL_MAX])
print("句子对:", np.array(embeddings).shape)
# 3. 可以使用adapter来对预训练模型进行微调,下面的代码只让adapter和layer normalization成为可训练的层
layer_num = 12
config_path = os.path.join(pretrained_path, 'bert_config.json')
model_path = os.path.join(pretrained_path, 'bert_model.ckpt')
model = load_trained_model_from_checkpoint(
