【NLP】Transformers 源码阅读和实践
本文主要针对HuggingFace开源的 transformers，以BERT为例介绍其源码并进行一些实践。主要以pytorch为例 (tf 2.0 代码风格几乎和pytorch一致)，介绍BERT使用的Transformer Encoder，Pre-training Tasks和Fine-tuning Tasks。最后，针对预训练好的BERT进行简单的实践，例如产出语句embeddings，预测目标词以及进行抽取式问答。本文主要面向BERT新手，在阅读本文章前，假设读者已经阅读过BERT原论文。
1. Core Components
Transformers: State-of-the-art Natural Language Processing
参考上面的论文，transformers开源库的核心组件包括3个：
「Configuration」：配置类，通常继承自「PretrainedConfig」，保存model或tokenizer的超参数，例如词典大小，隐层维度数，dropout rate等。配置类主要可用于复现模型。
「Tokenizer」：切词类，通常继承自「PreTrainedTokenizer」，主要存储词典，token到index映射关系等。此外，还会有一些model-specific的特性，如特殊token，[SEP], [CLS]等的处理，token的type类型处理，语句最大长度等，因此tokenizer通常和模型是一对一适配的。比如BERT模型有BertTokenizer。Tokenizer的实现方式有多种，如word-level, character-level或者subword-level，其中subword-level包括Byte-Pair-Encoding，WordPiece。subword-level的方法目前是transformer-based models的主流方法，能够有效解决OOV问题，学习词缀之间的关系等。Tokenizer主要为了「将原始的语料编码成适配模型的输入。」
「Model」: 模型类。封装了预训练模型的计算图过程，遵循着相同的范式，如根据token ids进行embedding matrix映射，紧接着多个self-attention层做编码，最后一层task-specific做预测。除此之外，Model还可以做一些灵活的扩展，用于下游任务，例如在预训练好的Base模型基础上，添加task-specific heads。比如，language model heads，sequence classification heads等。在代码库中通常命名为，「XXXForSequenceClassification」 or 「XXXForMaskedLM」，其中XXX是模型的名称（如Bert）， 结尾是预训练任务的名称 (MaskedLM) 或下游任务的类型(SequenceClassification)。
另外，针对上述三大类，transformer还额外封装了「AutoConfig, AutoTokenizer,AutoModel」，可通过模型的命名来定位其所属的具体类，比如'bert-base-cased'，就可以知道要加载BERT模型相关的配置、切词器和模型。非常方便。通常上手时，我们都会用Auto封装类来加载切词器和模型。
2. Transformer-based Pre-trained model
所有已实现的Transformer-based Pre-trained models:
CONFIG_MAPPING = OrderedDict(
[
("retribert", RetriBertConfig,),
("t5", T5Config,),
("mobilebert", MobileBertConfig,),
("distilbert", DistilBertConfig,),
("albert", AlbertConfig,),
("camembert", CamembertConfig,),
("xlm-roberta", XLMRobertaConfig,),
("marian", MarianConfig,),
("mbart", MBartConfig,),
("bart", BartConfig,),
("reformer", ReformerConfig,),
("longformer", LongformerConfig,),
("roberta", RobertaConfig,),
("flaubert", FlaubertConfig,),
("bert", BertConfig,),
("openai-gpt", OpenAIGPTConfig,),
("gpt2", GPT2Config,),
("transfo-xl", TransfoXLConfig,),
("xlnet", XLNetConfig,),
("xlm", XLMConfig,),
("ctrl", CTRLConfig,),
("electra", ElectraConfig,),
("encoder-decoder", EncoderDecoderConfig,),
]
上述是该开源库实现的模型，包括了BERT，GPT2，XLNet，RoBERTa，ALBERT，ELECTRA，T5等家喻户晓的预训练语言模型。
下面将以BERT为例，来介绍BERT相关的源码。建议仔细阅读源码中我做的一些「注释」，尤其是「步骤的细分」。同时，关注下目录的层次，「即：不同类之间的关系。」
2.1 BertModel Transformer
「BertModel」, The bare Bert Model transformer outputting 「raw hidden-states」 without any specific head on top。这个类的目标主要就是利用「Transformer」获取序列的编码向量。抽象出来的目标是为了适配不同的预训练任务。例如：MLM预训练任务对应的类为BertForMaskedLM，其中有个成员实例为BertModel，就是为了编码序列，获取序列的hidden states后，再构建MaskedLM task进行训练或者预测。
核心构造函数和Forward流程代码如下：
# BertModel的构造函数
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = BertEmbeddings(config)
self.encoder = BertEncoder(config)
self.pooler = BertPooler(config)
self.init_weights()
def forward(self, input_ids=None, attention_mask=None,token_type_ids=None,
position_ids=None, head_mask=None, inputs_embeds=None,
encoder_hidden_states=None, encoder_attention_mask=None,
output_attentions=None, output_hidden_states=None,):
# ignore some code here...
# step 1: obtain sequence embedding, BertEmbeddings
embedding_output = self.embeddings(
input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds)
# step 2: transformer encoder, BertEncoder
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
sequence_output = encoder_outputs[0]
# step 3: pooling to obtain sequence-level encoding, BertPooler
pooled_output = self.pooler(sequence_output)
outputs = (sequence_output, pooled_output,) + encoder_outputs[1:]
return outputs # sequence_output, pooled_output, (hidden_states), (attentions)
「参数如下：」
「input_ids」: 带特殊标记([CLS]、[SEP])的「token ids」序列, e.g.,
tensor([[ 101, 1188, 1110, 1126, 7758, 1859, 102]]), 其中101和102分别是[CLS]，[SEP]对应的token id。其「shape」: ，「B」为batch size, 「S」为序列的长度，此例即：1x7。「inputs_embeds:」 和input_ids参数「二选一」。inputs_embeds代表给定了输入tokens对应的token embeddings，比如用word2vec的word embeddings作为token embeddings，这样就不需要用input_ids对默认随机初始化的embedding做lookup得到token embeddings。
「attention_mask」: 「self-attention使用」，可选，shape和input_ids一致。当对encoder端的序列做self-attention时，默认全为1，即都可以attend；decoder端序列做self-attention时，默认为类似下三角矩阵的形式 (对角线也为1)。
「token_type_ids」: 可选，shape和input_ids一致，单语句输入时，取值全为0；在“语句对“的输入中，该取值为0或1，即：前一句为0，后一句为1。
「head_mask」: **self-attention使用，**可选，想用哪些head，就为1或者None，不想用的head就为0。shape为[num_heads] or [num_hidden_layers x num_heads]，即：可以每层每个head单独设置mask。
「position_ids」: 可选，位置id，默认就是0~S。
「encoder_hidden_states/encoder_attention_mask」：decoder端对encoder端做cross-attention时使用，此时K和V即通过encoder_hidden_states得到。
其中，
「Step 1」: 「获取序列的embedding」，对应下文要介绍的「BertEmbeddings」
「Step 2」: 「利用Transformer进行编码」，对应下文要介绍的「BertEncoder」，获取sequence token-level encoding.
「Step 3」: 「对 [CLS] 对应的hidden state进行非线性变换得到」 sequence-level encoding，对应下文要介绍的「BertPooler」。
2.2 BertEmbeddings
「第一步Step 1」，获取序列的embeddings
「token embedding + position embedding + segment embedding」
embedding_output = self.embeddings(
input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds) # embeddings是BertEmbeddings类
基于input_ids或者inputs_embeds获取token embeddings。
基于position_ids获取position embeddings，此处采用的是绝对位置编码。
基于token_type_ids获取语句的segment embeddings。
# BertEmbeddings core forward code:
def forward(self, input_ids=None, token_type_ids=None,
position_ids=None, inputs_embeds=None):
# ignore some codes here...
# step 1: token embeddings
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids) # token embeddings
# step 2: position embeddings
position_embeddings = self.position_embeddings(position_ids)
# step 3: segment embeddings
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
此处还做了layer_norm和dropout。输出的embedding的shape为， 。D默认为768。此处输出的embeddings标记为 。
2.3 BertEncoder
「第二步，step 2」，利用「Transformer」对序列进行编码
# encoder是BertEncoder类
encoder_outputs = self.encoder(
embedding_output, # 序列embedding, B x S x D
attention_mask=extended_attention_mask, # 序列self-attention时使用
head_mask=head_mask, # 序列self-attention时使用
encoder_hidden_states=encoder_hidden_states, # decoder，cross-attention
encoder_attention_mask=encoder_extended_attention_mask, # cross-attention
output_attentions=output_attentions, # 是否输出attention
output_hidden_states=output_hidden_states) # 是否输出每层的hidden state
「embedding_output」：BertEmbeddings的输出，batch中样本序列的每个token的嵌入。
「extended_attention_mask」：「self-attention」使用。根据attention_mask做维度广播 ， 是head数量，此时，方便下文做self-attention时作mask，即：softmax前对logits作处理，「logits+extended_attention_mask」，即：attention_mask取值为1时，extended_attention_mask对应位置的取值为0；否则，attention_mask为0时，extended_attention_mask对应位置的取值为-10000.0 (很小的一个数)，这样softmax后，mask很小的值对应的位置概率接近0达到mask的目的。
「head_mask」：「self-attention」使用。同样可以基于「原始输入head_mask作维度广播」，广播前的shape为H or L x H；广播后的shape为：「L x B x H x S x S」。即每个样本序列中每个token对其他tokens的head attentions 值作mask，head attentions数量为L x H。
「encoder_hidden_states」：可选，「cross-attention使用」。即：decoder端做编码时，要传入encoder的隐状态，「B x S x D」。
「encoder_attention_mask」：可选，「cross-attention使用」。即，decoder端做编码时，encoder的隐状态的attention mask。和extended_attention_mask类似，「B x S。」
「output_attentions」：是否输出attention值，bool。可用于可视化attention scores。
「output_hidden_states」：是否输出每层得到的隐向量，bool。
# BertEncoder由12层BertLayer构成
self.layer = nn.ModuleList([BertLayer(config) for _ in range(config.num_hidden_layers)])
# BertEncoder Forward核心代码
def forward(self, hidden_states,
attention_mask=None, head_mask=None,
encoder_hidden_states=None, encoder_attention_mask=None,
output_attentions=False, output_hidden_states=False):
# ignore some codes here...
all_hidden_states = ()
all_attentions = ()
for i, layer_module in enumerate(self.layer): # 12层BertLayer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# step 1: BertLayer iteration
layer_outputs = layer_module(
hidden_states,
attention_mask,
head_mask[i],
encoder_hidden_states,
encoder_attention_mask,
output_attentions) # BertLayer Forward，核心！！！
hidden_states = layer_outputs[0] # overide for next iteration
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],) # 存每层的attentions，可以用于可视化
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (hidden_states,)
if output_hidden_states:
outputs = outputs + (all_hidden_states,)
if output_attentions:
outputs = outputs + (all_attentions,)
return outputs # last-layer hidden state, (all hidden states), (all attentions)
2.4 BertLayer
上述代码最重要的是循环内的「BertLayer」迭代过程，其核心代码：
def forward(self, hidden_states, attention_mask=None, head_mask=None,
encoder_hidden_states=None, encoder_attention_mask=None,
output_attentions=False,):
# step 1.0: self-attention, attention实例是BertAttention类
self_attention_outputs = self.attention(
hidden_states, attention_mask, head_mask, output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
# step 1.1: 如果是decoder, 就作cross-attention，此时step1.0的输出即为decoder侧的序列的self-attention结果，并作为step1.1的输入；step 1.1的输出为decoder侧的cross-attention结果, crossattention实例也是BertAttention
if self.is_decoder and encoder_hidden_states is not None:
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:] # add cross attentions if we output attention weights
# step 2: intermediate转化，对应原论文中的前馈神经网络FFN
intermediate_output = self.intermediate(attention_output)
# step 3: 做skip-connection
layer_output = self.output(intermediate_output, attention_output)
outputs = (layer_output,) + outputs
return outputs
其中，step 1分为了2个小步骤。如果是encoder (BERT只用了encoder)，只有1.0起作用，即只对输入序列进行self-attention。如果是做seq2seq的模型，还会用到transformer的decoder，此时1.0就是对decoder的seq做self-attention，相应的attention_mask实际上是类下三角形式的矩阵；而1.1步骤此时就是基于1.0得到的self-attention序列的hidden states，对encoder_hidden_states进行cross-attention。这是本部分的重点。
2.4.1 BertAttention
BertAttention是上述代码中attention实例对应的类，也是transformer进行self-attention的核心类。包括了BertSelfAttention和BertSelfOutput成员。
class BertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = BertSelfAttention(config)
self.output = BertSelfOutput(config)
def forward(self, hidden_states, attention_mask=None,
head_mask=None, encoder_hidden_states=None,
encoder_attention_mask=None, output_attentions=False):
# step 1: self-attention, B x S x D
self_outputs = self.self(
hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, output_attentions)
# step 2: skip-connection, B x S x D
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
「BertSelfAttention」: 是「self-attention」，BertSelfAttention可以被实例化为encoder侧的self-attention，也可以被实例化为decoder侧的self-attention，此时attention_mask是非空的 (类似下三角形式的mask矩阵)。同时，还可以实例化为decoder侧的cross-attention，此时，hidden_states即为decoder侧序列的self-attention结果，同时需要传入encoder侧的encoder_hidden_states和encoder_attention_mask来进行cross-attention。
def forward(self, hidden_states, attention_mask=None, head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, output_attentions=False): # step 1: mapping Query/Key/Value to sub-space # step 1.1: query mapping mixed_query_layer = self.query(hidden_states) # B x S x (H*d) # If this is instantiated as a cross-attention module, the keys # and values come from an encoder; the attention mask needs to be # such that the encoder's padding tokens are not attended to. # step 1.2: key/value mapping if encoder_hidden_states is not None: mixed_key_layer = self.key(encoder_hidden_states) # B x S x (H*d) mixed_value_layer = self.value(encoder_hidden_states) attention_mask = encoder_attention_mask else: mixed_key_layer = self.key(hidden_states) # B x S x (H*d) mixed_value_layer = self.value(hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer) # B x H x S x d key_layer = self.transpose_for_scores(mixed_key_layer) # B x H x S x d value_layer = self.transpose_for_scores(mixed_value_layer) # B x H x S x d # step 2: compute attention scores # step 2.1: raw attention scores # B x H x S x d B x H x d x S -> B x H x S x S # Take the dot product between "query" and "key" to get the raw attention scores. attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) attention_scores = attention_scores / math.sqrt(self.attention_head_size) # step 2.2: mask if necessary if attention_mask is not None: # Apply the attention mask, B x H x S x S attention_scores = attention_scores + attention_mask # step 2.3: Normalize the attention scores to probabilities, B x H x S x S attention_probs = nn.Softmax(dim=-1)(attention_scores) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) # Mask heads if we want to if head_mask is not None: attention_probs = attention_probs * head_mask # B x H x S x S B x H x S x d -> B x H x S x d # step 4: aggregate values by attention probs to form context encodings context_layer = torch.matmul(attention_probs, value_layer) # B x S x H x d context_layer = context_layer.permute(0, 2, 1, 3).contiguous() # B x S x D new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) # B x S x D，相当于是多头concat操作 context_layer = context_layer.view(*new_context_layer_shape) outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) return outputs不同head均分768维度，12个head则每个为64维度；具体计算的时候合在一起，即同时算multi-head。记本步骤的输出为： ，输入 即为hidden_states参数。
-
; ，每个token根据其对序列内其它tokens的attention scores，来加权序列tokens的embeddings，得到每个token对应的上下文编码向量。
reshape后的形状为， ， 。
: ，「transpose_for_scores」。
:
,
, 如果是decoder侧的self-attention，则logit加上预先计算好的decoder侧对应的序列的每个位置的attention_mask，实际上就是下三角形式(包括对角线)的mask矩阵。
, ：每个batch每个head内，每个token对序列内其它token的attention score。
、 、 的shape: : 「
」 ， 。此处D=768, H=12, d=64。「attention score计算过程:」
「context_layer」: ：
「BertSelfOutput」
-
, 「self-connection」,
2.4.2 BertIntermediate
， , 其中 , 默认值为3072，用到了gelu激活函数。
2.4.3 BertOutput
, ，其中， .
上述输出 作为下一个BertLayer的输入，输出 ，依次类推，进行迭代，最终输出 ，即共12层BertLayer。
2.5 BertPooler
第三步，step3， 获取sequence-level embedding。
拿到上述BertEncoder的输出 ，shape为 ，其中每个样本序列(S维度)的第一个token为[CLS]标识的hidden state，标识为 ，即： 。则得到序列级别的嵌入表征：，shape为 。这个主要用于下游任务的fine-tuning。
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output) ## nn.tanh
return pooled_output
3. Bert Pre-training Tasks
上文介绍了BERT核心的Transformer编码器，下面将介绍Bert的预训练任务。
3.1 BertForMaskedLM
Bert Model with 「a language modeling head」 on top。上述介绍了BertModel的源码，BertModel主要用于获取序列的编码。本部分要介绍的BertForMaskedLM将基于BertModel得到的序列编码，利用MaskedLM预训练任务进行预训练。
Bert主要利用了Transformer的Encoder，基于encoder得到的序列编码进行预训练，而MLM使得encoder能够进行双向的self-attention。
「BertForMaskedLM」的构造函数：
def __init__(self, config):
super().__init__(config)
assert (
not config.is_decoder
), "If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for bi-directional self-attention." # is_decoder为False，不需要用到decoder
self.bert = BertModel(config) # BertModel进行序列编码
self.cls = BertOnlyMLMHead(config) # 多分类预训练任务, task-specific head
self.init_weights()
核心Forward代码：
def forward(self, input_ids=None, attention_mask=None,
token_type_ids=None,position_ids=None,
head_mask=None, inputs_embeds=None, labels=None,
encoder_hidden_states=None, encoder_attention_mask=None,
output_attentions=None, output_hidden_states=None,
**kwargs):
# step 1: obtain sequence encoding by BertModel
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
sequence_output = outputs[0] # B x S x D
# step 2: output scores of each token in the sequence
prediction_scores = self.cls(sequence_output) # B x S x V, 输出词典中每个词的预测概率
outputs = (prediction_scores,) + outputs[2:] # Add hidden states and attention if they are here
# step 3: build loss, label, B x S
if labels is not None:
loss_fct = CrossEntropyLoss() # -100 index = padding token
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) # 拍扁， (B*S) x V
outputs = (masked_lm_loss,) + outputs
return outputs # (masked_lm_loss), prediction_scores, (hidden_states), (attentions)
参数基本上和BertModel一模一样，多了一个labels参数，主要用于获取MLM loss。
其中，cls对应的「BertOnlyMLMHead」类 (其实就是类「BertLMPredictionHead」) 做的主要事情如下公式，即：MLM多分类预测任务，其中 为BertModel得到的sequence-token-level encoding，shape为 。
其中， ， 为vocab的大小。 的shape为： 。
特别的，label的形式：
「labels」 (torch.LongTensor of shape (batch_size, sequence_length), optional, defaults to None) – Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size]
即，不打算预测的，「label设置为-100」。一般只设置[MASK]位置对应的label，其它位置设置成-100。这样只计算了[MASK]待预测位置的token对应的loss。-100实际上是CrossEntropyLos的ignore_index参数的默认值。
3.2 BertForPreTraining
和BertForMaskedLM类似，多了一个next sentence prediction预训练任务。Bert Model with 「two heads on top」 as done during the pre-training: a 「masked language modeling」 head and 「a next sentence prediction」 (classification) head.
此部分对应的heads的核心代码为：
class BertPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = BertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
其中，BertLMPredictionHead和BertForMaskedLM中的BertLMPredictionHead一样，通过这个来得到MLM loss。另外，多了一个seq_relationship，即拿pooled encoding接一个线性二分类层，判断是否是next sentence，因此可以构造得到next-sentence loss。二者Loss相加。
3.3 BertForNextSentencePrediction
Bert Model with a next sentence prediction (classification) head on top。只有上述的seq_relationship head来构造next-sentence loss，不作赘述。
4. Bert Fine-tuning Tasks
下面将介绍利用预训练好的Bert对下游任务进行Fine-tuning的方式。下文介绍的fine-tuning任务对应的model，已经在BERT基础上加了task-specific parameters，只需要利用该model，输入task-specific data，然后optimization一下，就能够得到fine-tuned model。
4.1 BertForSequenceClassification
句子级别的任务，sentence-level task。Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. 「for GLUE tasks.」
class BertForSequenceClassification(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = BertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels) # 类别数量
self.init_weights()
# forward输入参数和前文介绍的预训练任务一样
def forward(self, input_ids=None, attention_mask=None,
token_type_ids=None, position_ids=None,
head_mask=None, inputs_embeds=None, labels=None,
output_attentions=None, output_hidden_states=None):
# step 1: transformer encoding
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states)
# step 2: use the pooled hidden state corresponding to the [CLS] token
# B x D
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
# B x N
logits = self.classifier(pooled_output)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
# step 3: build loss, labels: (B, )
if labels is not None:
if self.num_labels == 1:
# We are doing regression
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), labels.view(-1))
else:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
outputs = (loss,) + outputs
return outputs # (loss), logits, (hidden_states), (attentions)
看上述代码，非常清晰。先经过BertModel得到encoding，由于是sentence-level classification，直接拿第一个[CLS] token对应的hidden state过一个分类层得到类别的预测分数logits。再基于logits和labels来构造损失函数。这个任务主要用于sentence-level的分类任务，当然也能够用于sentence-pair-level的分类任务。
4.2 BertForMultipleChoice
句子对级别的任务，「sentence-pair」-level task。Bert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for 「RocStories/SWAG tasks.」
给一个提示prompt以及多个选择choice(其中有1个是对的，其它是错的)，判断其中哪个选择是对的。「输入格式会整成[[prompt, choice0], [prompt, choice1]…]的形式」。bertModel得到的pooled基础上接一个全连接层，输出在每个“句对“[prompt, choice i]上的logits，然后过一个softmax，构造交叉熵损失。
4.3 BertForTokenClassification
token级别的下游任务，token-level task。Bert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for 「Named-Entity-Recognition (NER) tasks.」
def forward(self, input_ids=None, attention_mask=None,
token_type_ids=None, position_ids=None, head_mask=None,
inputs_embeds=None, labels=None,
output_attentions=None, output_hidden_states=None):
# step 1: Transformer
outputs = self.bert(input_ids, attention_mask=attention_mask,
token_type_ids=token_type_ids, position_ids=position_ids,
head_mask=head_mask, inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states)
# step 2: get sequence-token encoding, B x S x D
sequence_output = outputs[0]
# step 3: fine-tuning parameters
sequence_output = self.dropout(sequence_output)
# B x S x N
logits = self.classifier(sequence_output) # nn.Linear(config.hidden_size, config.num_labels)
outputs = (logits,) + outputs[2:] # add hidden states and attention if they are here
# step 4: build loss, labels, B x S
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)
active_labels = torch.where(
active_loss, labels.view(-1),
torch.tensor(loss_fct.ignore_index).type_as(labels))
loss = loss_fct(active_logits, active_labels)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
outputs = (loss,) + outputs
return outputs # (loss), scores, (hidden_states), (attentions)
上述代码一目了然。不作赘述。主要应用于token-level的分类任务，如NER等。
4.4 BertForQuestionAnswering
句子对级别的任务，「sentence-pair」-level task，具体而言，即抽取式问答任务。Bert Model with a 「span classification head on top」 for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute span start logits and span end logits).
class BertForQuestionAnswering(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = BertModel(config)
# num_labels为2, 分别代表start_position/end_position对应的下游参数。
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
# 多了俩参数，start_positions，end_positions，抽取式问答的span label, shape都是(B, )
def forward(self, input_ids=None, attention_mask=None,
token_type_ids=None, position_ids=None,
head_mask=None, inputs_embeds=None,
start_positions=None, end_positions=None,
output_attentions=None, output_hidden_states=None):
# step 1: Transformer encoding
outputs = self.bert(
input_ids, # question, passage
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,)
# B x S x D
sequence_output = outputs[0]
# step 2: split to obtain start and end logits
# B x S x N (N为labels数量,此处N=2)
logits = self.qa_outputs(sequence_output)
# split后， B x S x 1, B x S x 1
start_logits, end_logits = logits.split(1, dim=-1)
# B x S, B x S
start_logits = start_logits.squeeze(-1)
end_logits = end_logits.squeeze(-1)
outputs = (start_logits, end_logits,) + outputs[2:]
# step 3: build loss, start_positions, end_positions: (B, )
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
# S 分类
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
outputs = (total_loss,) + outputs
return outputs # (loss), start_logits, end_logits, (hidden_states), (attentions)
上述代码主要就是拿sequence-token-level hidden states接两个全连接层，分别输出start_position预测的logits和end_position预测的logits。
5. Bert Practice
本部分进行Bert的实践，包括3个部分：
利用预训练好的BERT模型，输出目标语句的Embeddings。
利用预训练好的BERT模型，预测目标语句中[MASK]位置的真实词。
利用预训练好的BERT模型，进行抽取式问答系统。
目前该库实现的预训练模型如下：
bert-base-chinese
bert-base-uncased
bert-base-cased
bert-base-german-cased
bert-base-multilingual-uncased
bert-base-multilingual-cased
bert-large-cased
bert-large-uncased
bert-large-uncased-whole-word-masking
bert-large-cased-whole-word-masking
上述预训练好的模型的主要差异在于：
预训练时的文本语言语料，中文、英文、德文、多语言等
有无大小写区分
层数
预训练时遮盖的是 wordpieces 得到的sub-word 还是整个word
接下来主要采用'bert-base-cased'。在QA部分还会使用上述预训练模型‘bert-large-uncased-whole-word-masking’在SQUAD上的fine-tuning好的模型进行推断。
首先加载「切割器和模型：」
MODEL_NAME = "bert-base-cased"
# step 1: 先获取tokenizer, BertTokenizer,
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, cache_dir='tmp/token')
# step 2: 获取预训练好的模型, BertModel
model = AutoModel.from_pretrained(MODEL_NAME, cache_dir='tmp/model')
预览下tokenizer (「transformers.tokenization_bert.BertTokenizer」)：
# 共28996词，包括特殊符号:('[UNK]', 100),('[PAD]', 0),('[CLS]', 101),('[SEP]', 102), ('[MASK]', 103)...
tokenizer.vocab
看下「model」的网络结构：
BertModel(
(embeddings): BertEmbeddings(
(word_embeddings): Embedding(28996, 768, padding_idx=0)
(position_embeddings): Embedding(512, 768)
(token_type_embeddings): Embedding(2, 768)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): BertEncoder(
(layer): ModuleList(
(0): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(1): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(2): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(3): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(4): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(5): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(6): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(7): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(8): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(9): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(10): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(11): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): BertPooler(
(dense): Linear(in_features=768, out_features=768, bias=True)
(activation): Tanh()
)
)
模型结构参考BertModel源码介绍部分。
5.1 Embeddings produced by pre-trained BertModel
text = "This is an input example"
# step 1: tokenize, including add special tokens
tokens_info = tokenizer.encode_plus(text, return_tensors="pt")
for key, value in tokens_info.items():
print("{}:\n\t{}".format(key, value))
# observe the enriched token sequences
print(tokenizer.convert_ids_to_tokens(tokens_info['input_ids'].squeeze(0).numpy()))
# step 2: BertModel Transformer Encoding
outputs, pooled = model(**tokens_info)
print("Token wise output: {}, Pooled output: {}".format(outputs.shape, pooled.shape))
'''
step 1: outputs:
-----------------------------------------------------------
input_ids:
tensor([[ 101, 1188, 1110, 1126, 7758, 1859, 102]])
token_type_ids:
tensor([[0, 0, 0, 0, 0, 0, 0]])
attention_mask:
tensor([[1, 1, 1, 1, 1, 1, 1]])
['[CLS]', 'This', 'is', 'an', 'input', 'example', '[SEP]']
step 2: outputs:
------------------------------------------------------------
Token wise output: torch.Size([1, 7, 768]), Pooled output: torch.Size([1, 768])
'''
5.2 Predict the missing word in a sentence
from transformers import BertForMaskedLM
text = "Nice to [MASK] you" # target token using [MASK] to mask
# step 1: obtain pretrained Bert Model using MLM Loss
maskedLM_model = BertForMaskedLM.from_pretrained(MODEL_NAME, cache_dir='tmp/model')
maskedLM_model.eval() # close dropout
# step 2: tokenize
token_info = tokenizer.encode_plus(text, return_tensors='pt')
tokens = tokenizer.convert_ids_to_tokens(token_info['input_ids'].squeeze().numpy())
print(tokens) # ['[CLS]', 'Nice', 'to', '[MASK]', 'you', '[SEP]']
# step 3: forward to obtain prediction scores
with torch.no_grad():
outputs = maskedLM_model(**token_info)
predictions = outputs[0] # shape, B x S x V, [1, 6, 28996]
# step 4: top-k predicted tokens
masked_index = tokens.index('[MASK]') # 3
k = 10
probs, indices = torch.topk(torch.softmax(predictions[0, masked_index], -1), k)
predicted_tokens = tokenizer.convert_ids_to_tokens(indices.tolist())
print(list(zip(predicted_tokens, probs)))
'''
output:
[('meet', tensor(0.9712)),
('see', tensor(0.0267)),
('meeting', tensor(0.0010)),
('have', tensor(0.0003)),
('met', tensor(0.0002)),
('know', tensor(0.0001)),
('join', tensor(7.0005e-05)),
('find', tensor(5.8323e-05)),
('Meet', tensor(2.7171e-05)),
('tell', tensor(2.4689e-05))]
'''
可以看出，meet的概率最大，且达到了0.97，非常显著。
5.3 Extractive QA
展示sentence-pair level的下游任务。
from transformers import BertTokenizer, BertForQuestionAnswering
import torch
# step 1: obtain pretrained-model in SQUAD
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', cache_dir='tmp/token_qa')
model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad', cache_dir='tmp/model_qa')
# step 2: tokenize, sentence-pair, question, passage
question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
encoding = tokenizer.encode_plus(question, text, return_tensors='pt')
input_ids, token_type_ids = encoding["input_ids"], encoding["token_type_ids"]
print(input_ids, token_type_ids)
# observe enriched tokens
all_tokens = tokenizer.convert_ids_to_tokens(input_ids.squeeze().numpy())
print(all_tokens)
# step 3: obtain start/end position scores, B x S
start_scores, end_scores = model(input_ids, token_type_ids=token_type_ids) # (B, S)
answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])
print(answer)
assert answer == "a nice puppet"
'''
output:
step 2:
input_ids: tensor([[ 101, 2040, 2001, 3958, 27227, 1029, 102, 3958, 27227, 2001, 1037, 3835, 13997, 102]])
token_type_ids: tensor([[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]])
all_tokens:
['[CLS]', 'who', 'was', 'jim', 'henson', '?', '[SEP]', 'jim', 'henson', 'was', 'a', 'nice', 'puppet', '[SEP]']
step 3:
answer:
a nice puppet
'''
可以看出，模型能准确预测出答案，「a nice puppet」。
6. Summary
之前一直没有机会阅读BERT源码。这篇文章也算是对BERT源码的一次粗浅的阅读笔记。想要进一步学习的话，可以参考文章，進擊的 BERT：NLP 界的巨人之力與遷移學習。总之，基于huggingface提供的transfomers进行二次开发和fine-tune还是比较方便的。下一次会尝试结合AllenNLP，在AllenNLP中使用transformers来解决NLP tasks。
7. References
Transformers: State-of-the-art Natural Language Processing
深入理解NLP Subword算法：BPE、WordPiece、ULM
huggingface transformers doc
huggingface transformers source code
進擊的 BERT：NLP 界的巨人之力與遷移學習
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
往期精彩回顾
适合初学者入门人工智能的路线及资料下载机器学习及深度学习笔记等资料打印机器学习在线手册深度学习笔记专辑《统计学习方法》的代码复现专辑
AI基础下载机器学习的数学基础专辑
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