针对文本分类任务、问答任务等下游任务对预训练的BERT模型进行微调。 有两种:不区分大小写(BERT-uncased)和区分大小写(BERT-cased)。 对于NER任务,named entity recognition,命名实体识别,必须使用区分大小写的模型。 数据集包含文本,故而需要对文本进行向量化,常用算法:TF-IDF、word2vec。 Hugging Face,一个致力于通...
如果我们去翻看 BERT 的词表(vocab.txt),相信很多人都会注意到开头若干个“[unused*]”的的特殊tokens,比如说,bert-uncased-base 模型就有994个此类 tokens([unused0]to[unused993])[1]。那么,为什么 BERT 要保留这么多看起来没什么用的特殊 tokens 呢?类比"[CLS]" 和 "[SEP]",我们不难推测,这些“[...
针对文本分类任务、问答任务等下游任务对预训练的BERT模型进行微调。 有两种:不区分大小写(BERT-uncased)和区分大小写(BERT-cased)。 对于NER任务,named entity recognition,命名实体识别,必须使用区分大小写的模型。 数据集包含文本,故而需要对文本进行向量化,常用算法:TF-IDF、word2vec。 Hugging Face,一个致力于通...
bert-tiny: 16.9M bert-mini: 43M bert-small: 111M bert-medium: 159M bert-base-uncased: 420M bert-large-uncased: 1.25G
model = BertForMaskedLM.from_pretrained("bert-large-uncased") # 解冻所有层 for param in model...
1、bert-base-uncased:bert的预训练文件; 2、model:存放bert模型代码; 3、Reuters-21578:存放数据集; 4、run.py:项目运行主程序; 5、utils.py:处理数据集并且预加载; 6、train_eval.py:模型训练、验证、测试代码。 本篇介绍:5、utils.py:处理数据集并...
nBERT-Large, Uncased (Whole Word Masking): 24-layer, 1024-hidden, 16-heads, 340M parameters nBERT-Large, Cased (Whole Word Masking): 24-layer, 1024-hidden, 16-heads, 340M parameters nBERT-Base, Uncased: 12-layer, 768-hidden, 12-heads, 110M parameters ...
1、bert-base-uncased:bert的预训练文件; 2、model:存放bert模型代码; 3、Reuters-21578:存放数据集; 4、run.py:项目运行主程序; ...
“DistilBERT-Base-Uncased-Emotion”, which is “BERTMini”: DistilBERT is constructed during the pre-training phase via knowledge distillation, which decreases the size of a BERT model by 40% while keeping 97% of its language understanding. It is faster and smaller than any other BERT-based ...
This is a release of 24 smaller BERT models (English only, uncased, trained with WordPiece masking) referenced inWell-Read Students Learn Better: On the Importance of Pre-training Compact Models. We have shown that the standard BERT recipe (including model architecture and training objective) is ...