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-> 人工智能 -> 基于tensorflow2.0+使用bert获取中文词、句向量并进行相似度分析 -> 正文阅读

[人工智能]基于tensorflow2.0+使用bert获取中文词、句向量并进行相似度分析

本文基于transformers库，调用bert模型，对中文、英文的稠密向量进行探究

开始之前还是要说下废话，主要是想吐槽下，为啥写这个东西呢？因为我找了很多文章要么不是不清晰，要么就是基于pytorch，所以特地写了这篇基于tensorflow2.0+的

运行环境

这个环境没有严格要求，仅供参考
win10 + python 3.8 + tensorflow 2.9.1 + transformers 4.20.1

导入库

from transformers import AutoTokenizer, TFAutoModel
import tensorflow as tf
import matplotlib.pyplot as plt

加载模型

model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModel.from_pretrained(model_name,
                                    output_hidden_states=True)  # 是否返回bert所有隐藏层的稠密向量

输入测试句子

utt = ['今天的月亮又大又圆', '月亮真的好漂亮啊', '今天去看电影吧', "爱情睡醒了,天琪抱着小贝进酒店", "侠客行风万里"]
inputs = tokenizer(utt, return_tensors="tf", padding="max_length", truncation=True, max_length=64)
outputs = model(inputs)
hidden_states = outputs[2]  # 获得各个隐藏层输出

解释下输出(hidden_states)：

The layer number (13 layers)
The batch number (5 sentence) 也就是输入句子的个数
The word / token number (64 tokens in our sentence) 也就是max_length
The hidden unit / feature number (768 features)

疑惑点:
1.为啥是13层？bert不是12层吗？
第一层是输入的嵌入层，其余12层才是bert的

打印出出看下shape：

print("Number of layers:", len(hidden_states), "  (initial embeddings + 12 BERT layers)")
# Number of layers: 13   (initial embeddings + 12 BERT layers)

layer_i = 0
print("Number of batches:", len(hidden_states[layer_i]))
# umber of batches: 5

batch_i = 0
print("Number of tokens:", len(hidden_states[layer_i][batch_i]))
# Number of tokens: 64

token_i = 0
print("Number of hidden units:", len(hidden_states[layer_i][batch_i][token_i]))
# Number of hidden units: 768

查看下第一个句子第五个词在第五层的表示

batch_i = 0
token_i = 5
layer_i = 5
vec = hidden_states[layer_i][batch_i][token_i]

嗯，看下分布吧

plt.figure(figsize=(10, 10))
plt.hist(vec, bins=200)
plt.show()

请添加图片描述

现在多个句子的张量做一些改动

因为hidden_states是元组，所以现在要把他的维度嵌入到张量中

sentence_embeddings = tf.stack(hidden_states, axis=0)  # 在维度0的位置插入，也就是把13放入最前面
print(f"sentence_embeddings.shape : {sentence_embeddings.shape}")
# sentence_embeddings.shape : (13, 5, 64, 768)

调换维度，使每个词都有13层的嵌入表示

sentence_embeddings_perm = tf.transpose(sentence_embeddings, perm=[1, 2, 0, 3])
print(f"sentence_embeddings_perm.shape : {sentence_embeddings_perm.shape}")
# sentence_embeddings_perm.shape : (5, 64, 13, 768)

获取词的稠密向量

第一种方式：拼接后四层的稠密向量

for sentence_embedding in sentence_embeddings_perm:  # 获取每个句子的embedding
    print(f"sentence_embedding.shape: {sentence_embedding.shape}")
    token_vecs_cat = []
    for token_embedding in sentence_embedding:  # 获取句子每个词的embedding
        print(f"token_embedding.shape : {token_embedding.shape}")
        cat_vec = tf.concat([token_embedding[-1], token_embedding[-2], token_embedding[-3], token_embedding[-4]], axis=0)
        print(f"cat_vec.shape : {cat_vec.shape}")
        token_vecs_cat.append(cat_vec)
    print(f"len(token_vecs_cat) : {len(token_vecs_cat)}")

第二种方式：加和后四层的稠密向量

for sentence_embedding in sentence_embeddings_perm:  # 获取每个句子的embedding
    print(f"sentence_embedding.shape: {sentence_embedding.shape}")
    token_vecs_cat = []
    for token_embedding in sentence_embedding:  # 获取句子每个词的embedding
        print(f"token_embedding.shape : {token_embedding.shape}")
        cat_vec = sum(token_embedding[-4:])
        print(f"cat_vec.shape : {cat_vec.shape}")
        token_vecs_cat.append(cat_vec)
    print(f"len(token_vecs_cat) : {len(token_vecs_cat)}")

获取句子的稠密向量

平均每个token倒数第二层的稠密向量

token_vecs = sentence_embeddings[-2]
print(f"token_vecs.shape : {token_vecs.shape}")
# token_vecs.shape : (5, 64, 768)
sentences_embedding = tf.reduce_mean(token_vecs, axis=1)
print(f"sentences_embedding.shape : {sentences_embedding.shape}")
# sentences_embedding.shape : (5, 768)

相似度探讨

不同句子间的相似度

tensor_test = sentences_embedding[0]
consine_sim_tensor = tf.keras.losses.cosine_similarity(tensor_test, sentences_embedding)
print(f"consine_sim_tensor : {consine_sim_tensor}")
# consine_sim_tensor : [-0.99999994 -0.9915971  -0.9763253  -0.7641263  -0.9695324 ]

探讨下相同词bank在不同上下文时其vector的相似度

utt = ["After stealing money from the bank vault, the bank robber was seen fishing on the Mississippi river bank."]
inputs = tokenizer(utt, return_tensors="tf", padding="max_length", truncation=True, max_length=22)
"""
0 [CLS]
1 after
2 stealing
3 money
4 from
5 the
6 bank
7 vault
8 ,
9 the
10 bank
11 robber
12 was
13 seen
14 fishing
15 on
16 the
17 mississippi
18 river
19 bank
20 .
21 [SEP]

bank单词的位置分别在6, 10, 19
"""
outputs = model(inputs)
hidden_states = outputs[2]  # 获得各个隐藏层输出
tokens_embedding = tf.reduce_sum(hidden_states[-4:], axis=0) # 使用加和方式
bank_vault = tokens_embedding[0][6]
bank_robber = tokens_embedding[0][10]
river_bank = tokens_embedding[0][19]
consine_sim_tensor = tf.keras.losses.cosine_similarity(bank_vault, [bank_robber, river_bank])
print(f"consine_sim_tensor : {consine_sim_tensor}")
# consine_sim_tensor : [-0.93863535 -0.69570863]

可以看出**bank_vault(银行金库)和bank_robber(银行抢劫犯)**中的bank相似度更高些，合理！

完整代码

from transformers import AutoTokenizer, TFAutoModel
import tensorflow as tf
import matplotlib.pyplot as plt

# 加载模型
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = TFAutoModel.from_pretrained(model_name,
                                    output_hidden_states=True)  # Whether the model returns all hidden-states.

# 输入测试句子
utt = ['今天的月亮又大又圆', '月亮真的好漂亮啊', '今天去看电影吧', "爱情睡醒了,天琪抱着小贝进酒店", "侠客行风万里"]
inputs = tokenizer(utt, return_tensors="tf", padding="max_length", truncation=True, max_length=64)
outputs = model(inputs)
hidden_states = outputs[2]  # 获得各个隐藏层输出
"""
解释下输出(hidden_states)：
1. The layer number (13 layers)
2. The batch number (5 sentence) 也就是输入句子的个数
3. The word / token number (64 tokens in our sentence) 也就是max_length
4. The hidden unit / feature number (768 features)

疑惑点:
1.为啥是13层？bert不是12层吗？
第一层是输入的嵌入层，其余12层才是bert的
"""
print("Number of layers:", len(hidden_states), "  (initial embeddings + 12 BERT layers)")

layer_i = 0
print("Number of batches:", len(hidden_states[layer_i]))

batch_i = 0
print("Number of tokens:", len(hidden_states[layer_i][batch_i]))

token_i = 0
print("Number of hidden units:", len(hidden_states[layer_i][batch_i][token_i]))

# For the 5th token in our sentence, select its feature values from layer 5.
token_i = 5
layer_i = 5
vec = hidden_states[layer_i][batch_i][token_i]

# Plot the values as a histogram to show their distribution.
plt.figure(figsize=(10, 10))
plt.hist(vec, bins=200)
plt.show()


# Concatenate the tensors for all layers. We use `stack` here to
# create a new dimension in the tensor.
sentence_embeddings = tf.stack(hidden_states, axis=0)  # 在维度0的位置插入，也就是把13放入最前面
print(f"sentence_embeddings.shape : {sentence_embeddings.shape}")

# 调换维度，使每个词都有13层的嵌入表示
sentence_embeddings_perm = tf.transpose(sentence_embeddings, perm=[1, 2, 0, 3])
print(f"sentence_embeddings_perm.shape : {sentence_embeddings_perm.shape}")

# 获取词的稠密向量
## 第一种方式：拼接后四层的稠密向量
for sentence_embedding in sentence_embeddings_perm:  # 获取每个句子的embedding
    print(f"sentence_embedding.shape: {sentence_embedding.shape}")
    token_vecs_cat = []
    for token_embedding in sentence_embedding:  # 获取句子每个词的embedding
        print(f"token_embedding.shape : {token_embedding.shape}")
        cat_vec = tf.concat([token_embedding[-1], token_embedding[-2], token_embedding[-3], token_embedding[-4]], axis=0)
        print(f"cat_vec.shape : {cat_vec.shape}")
        token_vecs_cat.append(cat_vec)
    print(f"len(token_vecs_cat) : {len(token_vecs_cat)}")

## 第二种方式：加和后四层的稠密向量
for sentence_embedding in sentence_embeddings_perm:  # 获取每个句子的embedding
    print(f"sentence_embedding.shape: {sentence_embedding.shape}")
    token_vecs_cat = []
    for token_embedding in sentence_embedding:  # 获取句子每个词的embedding
        print(f"token_embedding.shape : {token_embedding.shape}")
        cat_vec = sum(token_embedding[-4:])
        print(f"cat_vec.shape : {cat_vec.shape}")
        token_vecs_cat.append(cat_vec)
    print(f"len(token_vecs_cat) : {len(token_vecs_cat)}")


# 获取句子的稠密向量
## 平均每个token倒数第二层的稠密向量
token_vecs = sentence_embeddings[-2]
print(f"token_vecs.shape : {token_vecs.shape}")
sentences_embedding = tf.reduce_mean(token_vecs, axis=1)
print(f"sentences_embedding.shape : {sentences_embedding.shape}")


# 计算余弦相似度
## 不同句子间的相似度
tensor_test = sentences_embedding[0]
consine_sim_tensor = tf.keras.losses.cosine_similarity(tensor_test, sentences_embedding)
print(f"consine_sim_tensor : {consine_sim_tensor}")


##探讨下相同词bank在不同上下文时其vector的相似度
utt = ["After stealing money from the bank vault, the bank robber was seen fishing on the Mississippi river bank."]
inputs = tokenizer(utt, return_tensors="tf", padding="max_length", truncation=True, max_length=22)
"""
0 [CLS]
1 after
2 stealing
3 money
4 from
5 the
6 bank
7 vault
8 ,
9 the
10 bank
11 robber
12 was
13 seen
14 fishing
15 on
16 the
17 mississippi
18 river
19 bank
20 .
21 [SEP]

bank单词的位置分别在6, 10, 19
"""
outputs = model(inputs)
hidden_states = outputs[2]  # 获得各个隐藏层输出
tokens_embedding = tf.reduce_sum(hidden_states[-4:], axis=0) # 使用加和方式
bank_vault = tokens_embedding[0][6]
bank_robber = tokens_embedding[0][10]
river_bank = tokens_embedding[0][19]
consine_sim_tensor = tf.keras.losses.cosine_similarity(bank_vault, [bank_robber, river_bank])
print(f"consine_sim_tensor : {consine_sim_tensor}")
# consine_sim_tensor : [-0.93863535 -0.69570863]
# 可以看出bank_vault(银行金库)和bank_robber(银行抢劫犯)中的bank相似度更高些，合理！