import mindspore.dataset as ds from ..base_dataset import CLSBaseDataset class SST2Dataset(CLSBaseDataset): """ SST2 dataset. Args: paths (Union[str, Dict[str, str]], Optional): Dataset file path or Dataset directory path, default None. tokenizer (Union[str]): Tokenizer function, default...
tokenizer= RobertaTokenizer.from_pretrained("roberta-base") class RobertaSim(nn.Module): def __init__(self,device): super().__init__() # 加载RoBERTa模型和分词器 self.device = device self.roberta = RobertaModel.from_pretrained("roberta-base") self.pool = Pooling() self.mse = MSELoss()...
使用“BERT”作为编码器和解码器(BERT2BERT)来改进Seq2Seq文本摘要模型
下面的代码可以看到把单词acquire分词成了'ac'和'quire' from transformers import AutoTokenizer, RobertaForMaskedLM import torch tokenizer = AutoTokenizer.from_pretrained("./@_PLMs/roberta/roberta-base") model = RobertaForMaskedLM.from_pretrained("./@_PLMs/roberta/roberta-base") inputs = tok...
from transformers import RobertaModel, RobertaTokenizer # 加载预训练的RoBERTa模型和tokenizer model_name = 'roberta-base' tokenizer = RobertaTokenizer.from_pretrained(model_name) model = RobertaModel.from_pretrained(model_name) # 输入文本 text = "Hello, how are you?" # 将文本转换为token IDs input...
from transformersimportAutoModel,AutoTokenizer tokenizer1=AutoTokenizer.from_pretrained("roberta-base"tokenizer2=AutoTokenizerfrom_pretrainedsequence="A Titan RTX has 24GB of VRAM"printtokenizer1sequence 输出: ‘,’Ġ‘泰坦’,'ĠRTX',‘Ġ’有'Ġ24','GB','Ġof','ĠVR','AM‘ ...
model = RobertaForMaskedLM.from_pretrained("./@_PLMs/roberta/roberta-base")inputs = tokenizer("acquire", return_tensors="pt")# {'input_ids': tensor([[ 0, 1043, 17446, 2]]), 'attention_mask': tensor([[1, 1, 1, 1]])}tokenizer.decode([1043])# 'ac'tokenizer.decode([17446])#...
RobertaTokenizerFast 的引用可以参考以下代码示例: python from transformers import RobertaTokenizerFast tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base') 在上述代码中,我们首先导入了 RobertaTokenizerFast 类,然后使用 from_pretrained 方法加载了一个预训练的 RoBERTa 分词器模型。你可以根据需要选择不...
tokenizer.save_pretrained(args.output_dir) # 加载保存的模型和tokenizer model = model_class.from_pretrained(args.output_dir) tokenizer = tokenizer_class.from_pretrained(args.output_dir) model.to(args.device) 报错的是这一句:model = model_class.from_pretrained(args.output_dir), 这里model_class我设...
model = RobertaForSequenceClassification.from_pretrained('roberta-base', num_labels=2)# 示例数据texts = ["I love programming.","This is a great tutorial!"] labels = [1,1]# 1表示正面情感,0表示负面情感# 数据预处理inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt...