由于我们微调bert的时候一般选用的是中文版的模型,因此,接下来我们加载的就是中文预训练模型bert。直接看代码: importtorch fromtransformersimportBertTokenizer, BertModel bertModel = BertModel.from_pretrained('bert-base-chinese', output_hidden_states=True, output_attentions=True) tokenizer = BertTokenizer.from...
BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "bert-base-uncased", "bert-large-uncased", "bert-base-cased", "bert-large-cased", "bert-base-multilingual-uncased", "bert-base-multilingual-cased", "bert-base-chinese", "bert-base-german-cased", "bert-large-uncased-whole-word-masking", "bert-larg...
fromtransformersimportAutoTokenizer#BertTokenizertokenizer=AutoTokenizer.from_pretrained('bert-base-chinese...
这是用bert-base-chinese预训练模型和THUCNews数据集微调后的结果。其他的英文数据集就没有尝试了，但是...
model = BertModel.from_pretrained('base-base-chinese') 找到源码文件:modeling_bert.py: classBertModel(BertPreTrainedModel): 会继承BertPreTrainedModel, classBertPreTrainedModel(PreTrainedModel): 而BertPreTrainedModel继承PreTrainedModel, from...modeling_utilsimport( ...
答:我理解此问题的情形是:已有的lora模型只训练了一部分数据,要训练另一部分数据的话,是在这个lora上继续训练呢,还是跟base模型合并后再套一层lora,或者从头开始训练一个lora? 我认为把之前的LoRA跟base model合并后,继续训练就可以,为了保留之前的知识和能力,训练新的LoRA时,加入一些之前的训练数据是需要的。另外...
1:官方提供的Bert-base模型参数信息如下: from tensorflow.python import pywrap_tensorflow from tensorflow.contrib.slim import get_variables_to_restore # display the bert hyperparameter def display_bert_base_hyperparameter(): bert_model_path="/chinese_L-12_H-768_A-12/" ...
bert-serving-start -model_dir /home/lcm/chinese_L-12_H-768_A-12 -num_worker=2 蓝色字体是根据自己情况变化的,第一处是我刚解压的预训练模型的绝对路径,第二处是指定进程数,不能超过GPU数量。 执行后出现下图的 all set, ready to serve request ! 表明成功开启bert服务器,之后运行代码跑模型的时候,...
bertModel=BertModel.from_pretrained('bert-base-chinese',output_hidden_states=True,output_attentions=True)tokenizer=BertTokenizer.from_pretrained('bert-base-chinese') 代码语言:javascript 复制 text='让我们来看一下bert的输出都有哪些'input_ids=torch.tensor([tokenizer.encode(text)]).long()outputs=bert...
BERT_PRETRAINED_MODEL_ARCHIVE_LIST = ["bert-base-uncased","bert-large-uncased","bert-base-cased","bert-large-cased","bert-base-multilingual-uncased","bert-base-multilingual-cased","bert-base-chinese","bert-base-german-cased","bert-large-uncased-whole-word-masking","bert-large-cased-whole-...