Two answers suggest different solutions. Autotokenizer automatically selects the relevant tokenizer class based on the model name or. Import torch from transformers import automodelforcausallm, autotokenizer # load the model and tokenizer model = automodelforcausallm.from_pretrained(gpt2).
huggingface AutoTokenizer.from_pretrained流程 知乎
See basic functions like from_pretrained, encode, decode, and more, with examples and tips.
From datasets import load_dataset import torch, multiprocessing, sys from transformers import autotokenizer, automodelforcausallm, bitsandbytesconfig from peft.
You can vote up the ones you like or vote down the ones you don't like, and go to the original project or. A user asks how to load a pretrained tokenizer from huggingface using autotokenizer.from_pretrained after saving it locally. Autoclasses are here to do this job for you so that you automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary. In this blog post, we will try to understand the huggingface tokenizers in depth and will go through all the parameters and also the outputs returned by a tokenizer.
介绍了 hugging face transformers 库中的 autoconfig、autotokenizer、automodel 类的 from_pretrained () 函数，用于根据预训练模型的名称或路径创建配置对象、令. You can load any tokenizer from the hugging. The following are 26 code examples of transformers.autotokenizer.from_pretrained (). To effectively utilize pretrained tokenizers for various nlp tasks,.
Learn how to use autotokenizer to create a tokenizer from a pretrained model configuration.
