import torch from transformers import BertTokenizer from IPython.display import clear_output I got error in line from transformers import BertTokenizer: ImportError: /lib/x86_64-linux-gnu/libm.so.6: version `GLIBC_2.29' not found (required by /mnt/home/wbj/anaconda3/envs/pytorch/lib/python...
首先,我们可以尝试修改llamatokenizer为llamaTokenizer,即将双引号中的名称修改为小写字母。这样做后,错误提示应该会变成以下内容: Error:Cannotimportname'llamaTokenizer'from'transformers' 这样,我们就可以正常导入llamaTokenizer了。 另外,我们还可以使用from transformers import LLAMATOKENIZER来解决这个问题。这样做后,即使...
在实际的应用中,如果你在使用llamatokenizer时遇到了导入错误,可能是因为你的环境中缺少了这个模块。解决这个问题的方法非常简单,只需要确保你的环境中已经安装了llamatokenizer对应的PyTorch版本即可。你可以通过pip命令来安装,例如:pip install torch transformers。 总的来说,imporror: cannot import name 'llamatokenizer...
from transformers import AutoTokenizer proxies = { "http": "http://127.0.0.1:7890", "https": "http://127.0.0.1:7890" } tokenizer = AutoTokenizer.from_pretrained('your_model', proxies=proxies) 👍 1 Sign up for free to join this conversation on GitHub. Already have an account? Sign...
transformers/models/auto/tokenization_auto.py in from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs) 548 tokenizer_class_py, tokenizer_class_fast = TOKENIZER_MAPPING[type(config)] 549 if tokenizer_class_fast and (use_fast or tokenizer_class_py is None): --> 550 return...
# 需要导入模块: from transformers import AutoTokenizer [as 别名]# 或者: from transformers.AutoTokenizer importfrom_pretrained[as 别名]def__init__(self, alias: str, cache_dir: Optional[str] = None, max_len_truncate: int =500, add_special_tokens: bool = True, **kwargs)->None:"""Initia...
def __init__(self, cache_dir=DEFAULT_CACHE_DIR, verbose=False): from transformers import AutoModelForTokenClassification from transformers import AutoTokenizer # download the model or load the model path weights_path = download_model('bert.ner', cache_dir, process_func=_unzip_process_func, ver...
import transformers # 加载HuggingFace Transformers库tokenizer = transformers.AutoTokenizer.from_pretrained('bert-base-multilingual-cased')model = transformers.BertForSequenceClassification.from_pretrained('bert-base-multilingual-cased') 加载数据 train_data = torch.utils.data.DataLoader(dataset=train_dataset, ...
def test_infer_dynamic_axis_tf(self): """ Validate the dynamic axis generated for each parameters are correct """ from transformers import TFBertModel model = TFBertModel(BertConfig.from_pretrained("bert-base-cased")) tokenizer = BertTokenizerFast.from_pretrained("bert-base-cased") self._tes...