我正在尝试使用3 个输入、3 个input_masks 和一个标签作为我的训练数据集的张量来训练一个预训练的 roberta 模型。
我使用以下代码执行此操作：
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
batch_size = 32
# Create the DataLoader for our training set.
train_data = TensorDataset(train_AT, train_BT, train_CT, train_maskAT, train_maskBT, train_maskCT, labels_trainT)
train_dataloader = DataLoader(train_data, batch_size=batch_size)
# Create the Dataloader for our validation set.
validation_data = TensorDataset(val_AT, val_BT, val_CT, val_maskAT, val_maskBT, val_maskCT, labels_valT)
val_dataloader = DataLoader(validation_data, batch_size=batch_size)
# Pytorch Training
training_args = TrainingArguments(
output_dir='C:/Users/samvd/Documents/Master/AppliedMachineLearning/FinalProject/results', # output directory
num_train_epochs=1, # total # of training epochs …