 使用 Usage  因为[transformers](https://github.com/huggingface/transformers)库中是没有ZEN2相关的模型结构的，所以你可以在我们的[Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)中找到并且运行代码。  Since there is no structure of ZEN2 in [transformers library](https://github.com/huggingface/transformers), you can find the structure of ZEN2 and run the codes in [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).   ```shell  git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git  ```  ```python from fengshen.models.zen2.ngram_utils import ZenNgramDict from fengshen.models.zen2.tokenization import BertTokenizer from fengshen.models.zen2.modeling import ZenForSequenceClassification, ZenForTokenClassification  pretrain_path = 'IDEA-CCNL/Erlangshen-ZEN2-668M-Chinese'  tokenizer = BertTokenizer.from_pretrained(pretrain_path) model_classification = ZenForSequenceClassification.from_pretrained(pretrain_path) model_extraction = ZenForTokenClassification.from_pretrained(pretrain_path) ngram_dict = ZenNgramDict.from_pretrained(pretrain_path, tokenizer=tokenizer)  ```  你可以从下方的链接获得我们做分类和抽取的详细示例。  You can get classification and extraction examples below.  [分类 classification example on fengshen](https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/fengshen/examples/zen2_finetune/fs_zen2_large_tnews.sh)  [抽取 extraction example on fengshen](https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/fengshen/examples/zen2_finetune/ner_zen2_large_ontonotes4.sh)   