supported_classes = (PreTrainedModel,) if not is_peft_available() else (PreTrainedModel, PeftModel) # Save a trained model and configuration using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` if not isinstance(self.model, supported_classes): if state_dict is ...
和from_pretrained()函数类似,我们使用save_pretrained()函数保存模型,如下所示。 model.save_pretrained("directory_on_my_computer") 上述操作会保存两个文件,如下所示。 ls directory_on_my_computer config.json pytorch_model.bin config.json文件包含了构建模型需要的属性及其值,同时也包括一些元数据(checkp...
config = 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", out...
保存模型通过调用Model.save_pretrained()函数实现,例如保存加载的 BERT 模型 fromtransformersimportAutoModel model=AutoModel.from_pretrained("bert-base-cased")model.save_pretrained("./models/bert-base-cased/")# 再次加载模型# Model.from_pretrained() 加载,只需要传递保存目录的路径。AutoModel.from_pretraine...
pytorch_model = BertForSequenceClassification.from_pretrained('./save/', from_tf=True) #Quickly test a few predictions - MRPC is a paraphrasing task, let's see if our model learned the tasksentence_0 = "This research was consistent with his findings.“sentence_1 = "His findings were comp...
transformers-cli upload./path/to/pretrained_model/# 上传一个文件夹包含weights/tokenizer/config # 通过`.save_pretrained()`保存 transformers-cli upload./config.json[--filename folder/foobar.json]# 上传单个文件 #(你可以选择覆盖其文件名,该文件名可以嵌套在文件夹中) ...
models and tokenizers model.save_pretrained('./directory/to/save/') # save model = model_class.from_pretrained('./directory/to/save/') # re-load tokenizer.save_pretrained('./directory/to/save/') # save tokenizer = tokenizer_class.from_pretrained('./directory/to/save/') # re-load #...
save_pretraining():将模型中的提示配置文件、权重文件、词表文件保存在本地,以便可以使用from_pretraining方法对它们进行新加载。 1.5.2 自动加载 在使用时,通过向from_pretrained方法中传入指定模型的版本名称,进行自动下载,并加载到内存中。 from transformers import BertTokenizer,BertForMaskedLM# 使用bert-base-...
我在刚开始接触 huggingface (后简称 hf) 的 transformers 库时候感觉很冗杂,比如就模型而言,有 PretrainedModel, AutoModel,还有各种 ModelForClassification, ModelForCausalLM, AutoModelForPreTraining, AutoModelForCausalLM等等;不仅如此,还设计了多到让人头皮发麻的各种 ModelOutput,比如BaseModelOutput, BaseModelOu...
model.save_pretrained("ner_model")tokenizer.save_pretrained("tokenizer") 如果您希望使用管道(pipeline)使用模型,则必须读取配置文件,并根据label_list对象中使用的标签正确分配label2id和id2label: id2label = {str(i): label for i,label in enumerate(label_list)}label2id = {label: str(i) for i,...