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." # ...
print(tokenizer) 上述代码将自动下载并加载’bert-base-uncased’模型的分词器,并打印输出。 AutoModel from_pretrained()AutoModel from_pretrained()是Hugging Face Transformers库中的一个功能,它可以根据给定的模型名称自动下载和加载相应的预训练模型。以下是AutoModel from_pretrained()函数的参数: model_name:模型...
import torch from transformers import AutoModel, AutoConfig from huggingface_hub import hf_hub_download model_id = "bert-base-uncased" num_classes = 2 model_class = AutoModel state_dict = torch.load(hf_hub_download(model_id, revision="main", filename="pytorch_model.bin")) huggingface_confi...
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; ...
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_...
例如,如果您想使用 bert-base-uncased 模型作为编码层,可以按照以下方式进行配置: python Copy from transformers import AutoModel encoder_layer = AutoModel.from_pretrained('bert-base-uncased') 其中,AutoModel 类是 transformers 库中的一个工厂函数,用于根据模型名称自动加载对应的模型。from_pretrained 方法用于...
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...
之前我使用huggingface库进行语言模型微调。这需要一个语料库、一个现有的 BERT 模型,并使用该语料库对该模型进行微调。我的命令是 python run_language_modelling.py --output_dir=lm_finetune --model_type=bert --model_name_or_path=bert-base-uncased --do_train --train_data_file=thread0_wdc20.txt ...
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...