(): # 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 = "...
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...
from transformers import BertConfig from transformers import BertTokenizer from transformers import BertModel model_name = "hfl/chinese-roberta-wwm-ext/" model_dir = "models/pretrain_models/" + model_name config = BertConfig.from_pretrained(model_dir) model = BertForMaskedLM.from_pretrained(model...
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 distilbart-cnn-12-6 ...
self.bert = BertForSequenceClassification.from_pretrained("hfl/chinese-roberta-wwm-ext", num_labels=num_labels) self.device = torch.device("cuda") for param in self.bert.parameters(): param.requires_grad = True # 每个参数都要求梯度,也可以冻结一些层 def forward(self, batch_seqs, batch...
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 distilbart-cnn-12-6 ...
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_pretrained(modelPath) ...
因为发现了这个现象,我又尝试了roberta。 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] ...
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 CoSENThfl/chinese-macbert-baseshibing624/text2vec-base-chinese31.9342.6770.1617.2179.3070...
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 ...