然后我们就可以将训练完毕时存储的checkpoint导出成可以被serving的模型文件 # 参数说明:# EXPORT_MODEL_PATH 导出的模型存放的路径# OUTPUT_PATH 训练模型时checkpoint的保存路径# BERT_BASE 预训练模型的保存路径python run_classifier.py\--task_name=spam\--do_save=true\--save_dir=${EXPORT_MODEL_PATH}\--...
Bert-base-chinese Table of Contents Model Details Uses Risks, Limitations and Biases Training Evaluation How to Get Started With the Model Model Details Model Description: This model has been pre-trained for Chinese, training and random input masking has been applied independently to word pieces (...
The state-of-the-art SQuAD results from the paper currently cannot be reproduced on a 12GB-16GB GPU due to memory constraints (in fact, even batch size 1 does not seem to fit on a 12GB GPU usingBERT-Large). However, a reasonably strongBERT-Basemodel can be trained on the GPU with th...
Language(s):Chinese License:[More Information needed] Parent Model:See theBERT base uncased modelfor more information about the BERT base model. Model Sources Paper:BERT Uses Direct Use This model can be used for masked language modeling
bert_model ="bert-base-chinese" max_seq_len =128 batch_size =32 设置参数之后,我们就可以读取预置的 Tokenizer 了,并且将它存入到 bert_tokenizer 变量中。 bert_tokenizer = BertTokenizer.from_pretrained(bert_model) 我们检查一下,看预训练模型都认识哪些字。
1 bert-base-chinese (https://huggingface.co/bert-base-chinese) 这是最常见的中文bert语言模型,基于中文维基百科相关语料进行预训练。把它作为baseline,在领域内无监督数据进行语言模型预训练很简单。只需要使用官方给的例子就好。 https://github.com/huggingface/transformers/tree/master/examples/language-...
同理,由于谷歌官方发布的BERT-base(Chinese)中,中文是以字为粒度进行切分,没有考虑到传统NLP中的中文分词(CWS)。我们将全词Mask的方法应用在了中文中,即对组成同一个词的汉字全部进行[MASK]。该模型使用了中文维基百科(包括简体和繁体)进行训练,并且使用了哈工大语言技术平台LTP(http://ltp.ai)作为分词工具。
从huggingface 官网上下载 bert-base-chinese 模型权重、配置文件和词典到 pretrained_bert 文件夹中,下载地址:https://huggingface.co/bert-base-chinese/tree/main 模型训练 文本分类模型训练: pythonmain.py--modetrain--data_dir./data--pretrained_bert_dir./pretrained_bert ...
(1) bert-base-chinese TaskAFQMCCMNLICSLIFLYTEKOCNLITNEWSWSC P72.17%75.74%80.93%60.22%78.31%57.52%75.33% F152.96%75.74%81.71%60.22%78.30%57.52%80.82% (2) chinese-roberta-wwm-ext: TaskAFQMCCMNLICSLIFLYTEKOCNLITNEWSWSC P73.10%80.75%80.07%60.98%80.75%57.93%86.84% ...
BERT-Base, Multilingual Uncased (Orig, not recommended) (Not recommended, use Multilingual Cased instead): 102 languages, 12-layer, 768-hidden, 12-heads, 110M parameters BERT-Base, Chinese: Chinese Simplified and Traditional, 12-layer, 768-hidden, 12-heads, 110M parameters BERT-base, Chinese...