文章目录
bert理解简单机理encoder输入输出输出的结果
作用code(notebook)总结引用
bert理解
一句话概括， bert就是一个抽取器。输入一句话（词序列），输出抽取后的embedding序列。 再简单理解就是，它就是一个 encoder。
简单机理
我们可以用transformer在语言模型上做预训练。因为transformer是encoder-decoder结构，语言模型就只需要encoder部分就够了。BERT，利用transformer的encoder来进行预训练。
那么什么是transformer？ 这是一个新的训练结构，发展历程而言就是CNN，RNN，transformer； transformer是基于attention机理发展而来。 transformer由编码器和解码器组成。编码器和解码器都是基于attention机制。如下图
什么是注意力机制，一图简单领会，后面我们单独开一篇动手实践一下 注意力机制就是，当前词的含义，必须结合结合上下文才能更好的理解。
encoder输入输出
输入会加入特殊的[CLS]代表整句话的含义，可以用于分类。
input的词help，prince，mayuko等，一共512，这是截取的最大长度。
然后经过12层的encoder
最后输出的是每个token对应的embedding序列，每个token对应一个768维的向量。这个应该很好理解。
输出的结果
out = bert(xx)
Return:
:obj:`tuple(torch.FloatTensor)` comprising various elements depending on the configuration (:class:`~transformers.BertConfig`) and inputs:
**last_hidden_state** (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
**pooler_output** (:obj:`torch.FloatTensor`: of shape :obj:`(batch_size, hidden_size)`):
Last layer hidden-state of the first token of the sequence (classification token)
further processed by a Linear layer and a Tanh activation function. The Linear
layer weights are trained from the next sentence prediction (classification)
objective during pre-training.
This output is usually *not* a good summary
of the semantic content of the input, you're often better with averaging or pooling
the sequence of hidden-states for the whole input sequence.
**hidden_states** (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
**attentions** (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
作用
有了词序列对应的embedding向量，就可以对词分类、句子向量构建，句子分类、句子相似度比较等。
code(notebook)
#%% md
# bert
#%%
!pip install transformers
#%%
import torch
from transformers import BertModel, BertTokenizer
#%%
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
#%%
input_ids = tokenizer.encode('hello world bert!')
input_ids
#%%
type(input_ids)
#%%
ids = torch.LongTensor(input_ids)
ids
#%%
text = tokenizer.convert_ids_to_tokens(input_ids)
text
#%%
model = BertModel.from_pretrained('bert-base-uncased', output_hidden_states=True)
# Set the device to GPU (cuda) if available, otherwise stick with CPU
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = model.to(device)
ids = ids.to(device)
model.eval()
#%%
print(ids.size())
# unsqueeze IDs to get batch size of 1 as added dimension
granola_ids = ids.unsqueeze(0)
print(granola_ids.size())
#%% md
In the example below, an additional argument has been given to the model initialisation. output_hidden_states will give us more output information. By default, a BertModel will return a tuple but the contents of that tuple differ depending on the configuration of the model. When passing output_hidden_states=True, the tuple will contain (in order; shape in brackets):
1. the last hidden state (batch_size, sequence_length, hidden_size)
1. the pooler_output of the classification token (batch_size, hidden_size)
1. the hidden_states of the outputs of the model at each layer and the initial embedding outputs (batch_size, sequence_length, hidden_size)
#%%
out = model(input_ids=granola_ids) # tuple
hidden_states = out[2]
print("last hidden state:",out[0].shape) #torch.Size([1, 6, 768])
print("pooler_output of classification token:",out[1].shape)#[1,768] cls
print("all hidden_states:", len(out[2]))
#%%
for i, each_layer in enumerate(hidden_states):
print('layer=',i, each_layer)
#%%
sentence_embedding = torch.mean(hidden_states[-1], dim=1).squeeze()
print(sentence_embedding)
print(sentence_embedding.size())
#%%
# get last four layers
last_four_layers = [hidden_states[i] for i in (-1, -2, -3, -4)]
# cast layers to a tuple and concatenate over the last dimension
cat_hidden_states = torch.cat(tuple(last_four_layers), dim=-1)
print(cat_hidden_states.size())
# take the mean of the concatenated vector over the token dimension
cat_sentence_embedding = torch.mean(cat_hidden_states, dim=1).squeeze()
print(cat_sentence_embedding)
print(cat_sentence_embedding.size())
不同的emebdding组合会带来不一样的结果，参考。 利用concat的向量，最优结果。
总结
不同的层代表不同的特征含义，向量组合的实验可以证明这一点。bert就是抽取器不同隐层输出的向量的使用是核心所在仔细理解文中的两幅图，和样例代码。然后就是感悟了！
引用
https://github.com/huggingface/transformers/issues/2986https://github.com/BramVanroy/bert-for-inference/blob/master/introduction-to-bert.ipynbhttps://www.cnblogs.com/gczr/p/11785930.htmlhttps://blog.csdn.net/longxinchen_ml/article/details/86533005
