对于 transformers 库,你应该使用 AutoTokenizer 和AutoModel 而不是 autotokenizer 和automodel。 安装或更新 transformers 库:如果 transformers 库未安装或版本过旧,可能会导致问题。你可以通过以下命令安装或更新它: bash pip install transformers --upgrade 使用正确的导入语句:使用正确的类名进行导入,如下所示: ...
tokenizer.save_pretrained( "code-search-net-tokenizer" ) Tokenizer的其他功能 第一个是编码相关的功能,以BERT为例如下,涉及到的常用方法见代码,后面不一一赘述。 from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-cased",cache_dir='D:\\temp\\huggingface\\chen\\da...
I have these updated packages versions: tqdm-4.65.0 transformers-4.27.4 I am running this code: from transformers import AutoTokenizer, AutoModel I am obtaining this erros: ImportError: cannot import name 'ObjectWrapper' from 'tqdm.utils' (/Users/anitasancho/opt/anaconda3/lib/python3.7/site-pa...
1、使用repo id下载到缓存并加载 ...fromtransformersimportAutoTokenizer,AutoModelForSeq2SeqLMtokenizer=AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")model=AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zh-en")... 2、使用本地路径加载 ...fromtransformersimportAutoTokenizer,...
Hugging Face Transformers是一个强大的Python库,它包含了大量预训练的模型和工具,可用于自然语言处理任务。其中,AutoConfig、AutoTokenizer和AutoModel from_pretrained()是三个非常实用的功能。以下是它们的参数详解: AutoConfigAutoConfig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获取模型的...
加载tokenizer 测试代码:如果加载成功,就打印1。 fromtransformersimportAutoTokenizer tokenizer = AutoTokenizer.from_pretrained("./bert-base-chinese")print(1) 文件目录结构: |- bert-base-chinese |-- 各种checkpoint文件 |- test.py 如果checkpoint文件只有tokenizer.json: ...
from transformers import AutoTokenizer model_id = "mistralai/Mistral-7B-Instruct-v0.3" auto_tokenizer = AutoTokenizer.from_pretrained(model_id) When I inspect auto_tokenizer variable, then I get LlamaTokenizerFast: LlamaTokenizerFast(name_or_path='mistralai/Mistral-7B-Instruct-v0.3', vocab_size...
I am a bit confused about how to use huggingface transformers. I thought to train a simple language model that predicts if Albert Einstein said this sentence or not. from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") model = AutoModel...
from transformers import AutoTokenizer, TFDistilBertForSequenceClassification from datasets import load_dataset imdb = load_dataset('imdb') sentences = imdb['train']['text'][:500] tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased") model = TFDist...
>>> from transformers import AutoTokenizer >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") >>> def encode(batch): ... return tokenizer(batch["sentence1"], padding="longest", truncation=True, max_length=512, return_tensors="pt") >>> dataset.set_transform(encode) >>>...