无需导入config # 这里导入Huggingface里面有的模型:hfl/chinese-roberta-wwm-ext# 使用预训练模型的权重,生成分词器tokenizer= BertTokenizerFast.from_pretrained("hfl/chinese-roberta-wwm-ext")# 载入模型model= BertForSequenceClassification.from_pretrained("hfl/chinese-roberta-wwm-ext") 添加自己通过迁移训练或者...
MODEL_PATH = r"D:\\test\\bert-base-chinese" # 导入模型 tokenizer = transformers.BertTokenizer.from_pretrained(r"D:\\test\\bert-base-chinese\\bert-base-chinese-vocab.txt") # 导入配置文件 model_config = transformers.BertConfig.from_pretrained(MODEL_PATH) # 修改配置 model_config.output_hidden...
bert-base-cased bert-base-chinese bert-base-uncased bert-large-uncased xlm-roberta-base chinese-bert-wwm-ext chinese-electra-180g-base-discriminator chinese-roberta-wwm-ext clip-vit-base-patch32 code_trans_t5_small_program_synthese_transfer_learning_finetune deberta-v3-base deberta-v3-large distil...
(): # 基础模型位置 model_name = "hfl/chinese-roberta-wwm-ext" # 训练集 & 验证集 train_json_path = "datasets/train.json" val_json_path = "datasets/dev.json" max_length = 64 num_classes = 3 epochs = 15 batch_size = 128 lr = 1e-4 model_output_dir = "output" logs_dir = "...
bert-base-cased bert-base-chinese bert-base-uncased bert-large-uncased xlm-roberta-base chinese-bert-wwm-ext chinese-electra-180g-base-discriminator chinese-roberta-wwm-ext clip-vit-base-patch32 code_trans_t5_small_program_synthese_transfer_learning_finetune deberta-v3-base deberta-v3-large distil...
bert-base-cased bert-base-chinese bert-base-uncased bert-large-uncased xlm-roberta-base chinese-bert-wwm-ext chinese-electra-180g-base-discriminator chinese-roberta-wwm-ext clip-vit-base-patch32 code_trans_t5_small_program_synthese_transfer_learning_finetune deberta-v3-base deberta-v3-large distil...
bert_path = 'D:/pretrain/pytorch/chinese_roberta_wwm_ext/' tokenizer = BertTokenizer.from_pretrained(bert_path) BERT = BertModel.from_pretrained(bert_path) ... with torch.no_grad(): last_hidden_states = BERT(input_ids)[0] 以前都是这么使用的,没有任何问题。
AutoModel.from_pretrained("hfl/chinese-roberta-wwm-ext") self.bert_layer_2 = transformers.AutoModel.from_pretrained("bert-base-chinese") self.other_layers = ... # not important def forward(self,): pass # not important When running trainer.save_model(), it will only save the model's ...
gitclonehttps://huggingface.co/bert-base-uncased 下载后使用: fromtransformersimportAutoTokenizer,AutoModelForMaskedLM# modelPath = "./chinese-roberta-wwm-ext" # 相对路径modelPath="D:/chinese-roberta-wwm-ext"# 绝对路径tokenizer=AutoTokenizer.from_pretrained(modelPath)model=AutoModelForMaskedLM.from_...
Word2Vecword2vecw2v-light-tencent-chinese20.0031.4959.462.5755.7855.0420.7035.0323769 SBERTxlm-roberta-basesentence-transformers/paraphrase-multilingual-MiniLM-L12-v218.4238.5263.9610.1478.9063.0152.2846.463138 Instructorhfl/chinese-roberta-wwm-extmoka-ai/m3e-base41.2763.8174.8712.2076.9675.8360.5557.932980 ...