import torch from transformers import BertModel, BertTokenizer # 这里我们调用bert-base模型,同时模型的词典经过小写处理 model_name = 'bert-base-uncased' # 读取模型对应的tokenizer tokenizer = BertTokenizer.from_pretrained(model_name) # 载入模型 model = BertModel.from_pretrained(model_name) # 输入文本...
from transformers import AutoTokenizer, AutoModel import torch # 指定模型名称 model_name = "bert-base-uncased" # 加载 Tokenizer 和模型 tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModel.from_pretrained(model_name) # 输入文本 input_text = "This is a sample sentence." # ...
if args.model_name == 'bert': self.tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') self.input_size = 768 if args.model_name == 'bert': 这里的input-size,需要根据什么来确定值 所以是因为这个模型bert本来就应该是768,而不用根据具体的应用来选择这个值?self.tokenizer = AutoTokeniz...
pretrained_model_name为模型的预训练权重,字符串类型,包含文件名及缓存目录,比如“bert-base-uncased”,即BERT的bert-base-uncased模型。 **kwargs参数有下列选项: map_location:将模型中的变量与已经存储在本地的变量进行匹配; cache_dir:将模型存储在本地的缓存目录; from_tf:将TensorFlow模型加载到PyTorch中; ...
OSError: Can't load the model for 'bert-base-uncased'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'bert-base-uncased' is the correct path to a directory containing a file...
Model name 'bert-base-chinese' was not found in model name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese). We assumed 'https://s3.amazonaws.com/models.huggingface.co/bert/bert...
model = BertModel.from_pretrained("bert-base-uncased") device = torch.device("cuda"iftorch.cuda.is_available()else"cpu") model.to(device) input_size = (1,512)# input sizeinput_tensor = torch.randint(0,10000, input_size, dtype=torch.long, device=device) ...
BertConfig.from_pretrained("./test/saved_model/")# E.g. config (or model) was saved using *save_pretrained('./test/saved_model/')*config = BertConfig.from_pretrained("./test/saved_model/my_configuration.json")config = BertConfig.from_pretrained("bert-base-uncased", output_attentions=...
代码基础结构如下👉pipeline('任务类型', model='model_name'),只需一行代码就能让模型 下面是常见的代码 # 掩码任务frommodelscope.pipelinesimportpipeline unmasker=pipeline('fill-mask',model='bert-base-uncased')#序列标注任务part-of-speechpipeline_ins=pipeline(task='part-of-speech',model='damo/nlp_...
self.model = model_class.from_pretrained(pretrained_model_name, cache_dir=temp_dir)#self.model = BertModel.from_pretrained('bert-base-uncased', cache_dir=temp_dir)else: self.model = model_class(pretrained_config) 开发者ID:microsoft,项目名称:nlp-recipes,代码行数:10,代码来源:model_builder.py...