LM_FINE_TUNING=TrueclassPTuningBert(nn.Module):def__init__(self):super(PTuningBert,self).__init__()self.pre_train=PRE_TRAIN# TODO 如果仅微调prompt则冻结预训练模型forparaminself.pre_train.parameters():param.requires_grad=LM_FINE_TUNINGself.embedding=self.pre_train.bert.get_input_embeddings...
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) def forward(self, input_ids): embeddings = self.embedding(input_ids) output = self.transformer(embeddings) return self.lm_head(output) model = GPT2Model(config) 使用预定义模型 from transformers importGPT2LMHeadModelmode...
defget_output_embeddings(self): returnself.lm_head defprepare_inputs_for_generation(self,input_ids,past=None,**kwargs): token_type_ids=kwargs.get("token_type_ids",None) # only last token for inputs_ids if past is defined in kwargs ifpast: input_ids=input_ids[:,-1].unsqueeze(-1)...
return word_embeddings[input_ids] def position_embedding(position_ids, position_embeddings): return position_embeddings[position_ids] def token_type_embedding(token_type_ids, token_type_embeddings): return token_type_embeddings[token_type_ids] def softmax(x, axis=None): # e_x = np.exp(x)....
masked_input = input_.clone() - self.vocab_start_index masked_input[input_mask] = 0 else: masked_input = input_ # Get the embeddings. if self.deterministic_mode: output_parallel = self.weight[masked_input] else: # F.embedding currently has a non-deterministic backward function ...
input_ids= np.array(token_ids)#输入的词汇idreturninput_ids word_embeddings= model_data["transformer.wte.weight"] position_embeddings= model_data["transformer.wpe.weight"]defmodel_input(input_ids,position_ids): word_embedded=word_embedding(input_ids, word_embeddings) ...
@@ -497,7 +499,6 @@ def get_input_embeddings(self): def set_input_embeddings(self, new_embeddings): self.wte = new_embeddings # Copied from transformers.models.gpt2.modeling_gpt2.GPT2Model.forward def forward( self, input_ids: Optional[torch.LongTensor] = None, @@ -548,7 +549,7...
importtorch.nnasnnimporttorch.nn.functionalasFclassGPT(nn.Module):def__init__(self,vocab_size,d_model):super().__init__()self.wte=nn.Embedding(vocab_size,d_model)# word token embeddingsdefforward(self,inputs,targets=None):logits=self.wte(inputs)# dim -> batch_size, sequence_length, ...
中文的GPT2模型训练代码,基于Pytorch-Transformers. Contribute to lgstd/GPT2-Chinese development by creating an account on GitHub.
翻译自 :https://www.modeldifferently.com/en/2021/12/generaci%C3%B3n-de-fake-news-con-gpt-2/ (注:未去联系取得翻译授权,纯学习自用) 在这篇文章中,我们将看到如何使用基于Transformers架构的模型生成文本,