pretrained ( bool) – If True, returns a model pre-trained on ImageNet progress ( bool) – If True, displays a progress bar of the download to stderr memory_efficient ( bool) – but slower. Default: False. See “paper”.
21.09.2020 · I believe it has to be a relative PATH rather than an absolute one. So if your file where you are writing the code is located in 'my/local/', then your code should be like so: PATH = 'models/cased_L-12_H-768_A-12/' tokenizer = BertTokenizer.from_pretrained (PATH, local_files_only=True) You just need to specify the folder where all the files are ...
The following are 19 code examples for showing how to use transformers.BertModel.from_pretrained().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
Obtaining a pre-trained quantized model can be done with a few lines of code: import torchvision.models as models model = models.quantization.mobilenet_v2(pretrained=True, quantize=True) model.eval() # run the model with quantized inputs and weights out = model(torch.rand(1, 3, 224, 224))
import torchvision.models as models # Download a pretrained alexnet alexnet = models.alexnet(pretrained=True) # Set it to evaluation mode, i.e. turn off ...