BERT有两个主要的预训练版本,即BERT-Base-Uncased和BERT-Base-Cased。两者之间的区别在于:Uncased版本是对文本进行小写处理的,而Cased版本保留了原始文本的大小写信息。 BERT-Base-Uncased是基于小写文本的预训练模型。在预处理阶段,将所有的文本转换为小写字母,即将文本中所有的大写字母转换成小写字母。这样的预处理...
from transformers import BertTokenizer, BertModel tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased') model = BertModel.from_pretrained("bert-base-multilingual-cased") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output...
View in Studio:https://ml.azure.com/registries/azureml/models/bert-base-cased/version/17 License: apache-2.0 SharedComputeCapacityEnabled: True SHA: 5532cc56f74641d4bb33641f5c76a55d11f846e0 evaluation-min-sku-spec: 4|0|28|56 evaluation-recommended-sku: Standard_DS4_v2, Standard_D8a_v4, St...
bert-base-multilingual-cased在中文上的表现BERT(BidirectionalEncoderRepresentationsfromTransformers)是一种预训练的语言模型,可以用于各种自然语言处理任务。"bert-base-multilingual-cased"是BERT的一个版本,它是在多种语言上进行了预训练,包括中文。在中文上,"bert-base-multilingual-cased"通常表现良好,具有以下优点:多...
Model I am using: distilbert-base-cased Language: English The problem arises when using below code MODELS = [(DistilBertModel, DistilBertTokenizer, 'distilbert-base-cased')] for model_class, tokenizer_class, pretrained_weights in MODELS: ...
在Transformers中,特征抽取默认的模型是distilbert-base-cased。至于为什么使用distilbert而不是其他众多模型?稍微思考一下,首先distilbert较为轻量级这是肯定的。最重要的是,distilbert是HuggingFace的亲儿子。 所谓的特征提取其实就是去掉model head的模型输出了。使用这些特征,我们可以去进行下层任务的学习。当然所有的模型...
v2.0.json --bert_checkpoint /path_to/BERT-STEP-2285714.pt --bert_config /path_to/bert-config.json --pretrained_model_name=bert-base-cased --batch_size 3 --num_epochs 2 --lr_policy SquareRootAnnealing --optimizer adam_w --lr 3e-5 --do_lower_case --version_2_with_negative --no_...
$ python run_glue.py --model_name_or_path bert-base-cased --task_name $TASK_NAME --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 32 --learning_rate 2e-5 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ ...
$ python run_glue.py --model_name_or_path bert-base-cased --task_name $TASK_NAME --do_train --do_eval --max_seq_length 128 --per_device_train_batch_size 32 --learning_rate 2e-5 --num_train_epochs 3 --output_dir /tmp/$TASK_NAME/ ...
OSError:Can't load config for 'bert-base-cased'.Ifyou were tryingtoload itfrom'https://huggingface.co/models', make sure you don't have a local directory with the same name.Otherwise, make sure'bert-base-cased' is the correct path to a directory containing a config.json file ...