model_type = "bert" def __init__( self, vocab_size=30522, hidden_size=768,num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_ran...
"model_type": "bert", # 模型类型是bert "num_attention_heads": 12, # 编码器内注意力头数,默认12 "num_hidden_layers": 12, # 编码器内隐藏层层数,默认12 "pad_token_id": 0, # pad_token_id 未找到相关解释 "pooler_fc_size": 768, # 下面应该是pooler层的参数,本质是个全连接层,作为分类...
- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). <class 'transformers.models.bert.modeling_bert.BertModel'> 可以看到,。这一...
"gradient_checkpointing":false,"hidden_act":"gelu","hidden_dropout_prob":0.1,"hidden_size":768,"initializer_range":0.02,"intermediate_size":3072,"layer_norm_eps":1e-12,"max_position_embeddings":512,"model_type":"bert","num_attention_heads":12,"num_hidden_layers":12,"pad_token_id":...
"intermediate_size":3072,"layer_norm_eps":1e-12,"max_position_embeddings":512,"model_type":"bert","num_attention_heads":12,"num_hidden_layers":12,"pad_token_id":0,"position_embedding_type":"absolute","transformers_version":"4.3.3","type_vocab_size":2,"use_cache":true,"vocab_...
中间定义的一些模型中间结构先不管，417行定义了一个类PreTrainedBertModel, 这个类的输入是BertConfig, ...
type_id=random.randint(0, 2), start_length=start_length ) if len(input_ids) + len(template_tokens) >= self.tokenizer.model_max_length - 2: break start = len(input_ids) input_ids.extend(template_tokens) end = len(input_ids)
, type=str, default="http://localhost:8000") parser.add_argument("--model-name", ...
classtransformers.Trainer(model: torch.nn.modules.module.Module = None,args: transformers.training_args.TrainingArguments = None,data_collator: Optional[NewType.<locals>.new_type] = None,train_dataset: Optional[torch.utils.data.dataset.Dataset] = None,eval_dataset: Optional[torch.utils.data.dataset...
您需要model和instance_type才能部署模型。 您可以從模型目錄中的模型頁面開啟快速部署對話方塊,以找到模型的最佳 CPU 或 GPUinstance_type。 請確定您使用具有配額的instance_type。 目錄中顯示的模型會從HuggingFace登錄列出。 使用此範例中的最新版本來部署bert_base_uncased模型。 根據模型名稱和登錄的完整model資產識別...