我是这方面的初学者。请帮助我找到解决方案。我已经使用RobertaTokenizerFast对文本和摘要进行标记（max_token_length分别为 200 和 50）。计划是使用RoBERTa作为第一层。然后使用 conv2d、maxpool2d 和dense 压缩其输出以匹配目标摘要。最后一个密集层的输出是一个浮点向量。因此，我已将包含long input_ids 的目标向量标准化为浮点值（0 到 1）。最后，我使用了 CrossEntropy函数来获得损失。
class Summarizer(pl.LightningModule):
def __init__(self):
super().__init__()
self.roberta = RobertaModel.from_pretrained('roberta-base', return_dict = True, is_decoder=True, use_cache=False)
self.convlayer = torch.nn.Conv2d(in_channels=BATCH_SIZE, out_channels=1, kernel_size=4)
## BATCH_SIZE=20
self.relu = torch.nn.ReLU()
self.fc = torch.nn.Linear(in_features=97*381, out_features=50)
self.cross_entropy_loss = torch.nn.CrossEntropyLoss()
def forward(self, input_ids, attention_mask, labels):
output = self.roberta(input_ids=input_ids, attention_mask=attention_mask)
x = output['last_hidden_state']
x = torch.unsqueeze(x, 0)
x = self.convlayer(x)
x = self.relu(x)
x = F.max_pool2d(x, kernel_size=4, stride=2)
x = x.squeeze().flatten()
x = self.fc(x)
output = self.relu(x)
crossent_loss = self.cross_entropy_loss(labels, output)
return crossent_loss, output
def training_step(self, batch, batch_idx):
input_ids = batch['text_input_ids']
attention_mask = batch['text_attention_mask']
l = batch['labels'].float()
l = torch.tensor(l/torch.linalg.norm(l))
labels = l # normalized labels in (0,1)
labels_attention_mask = batch['labels_attention_mask']
loss, outputs = self(
input_ids = input_ids,
attention_mask = attention_mask,
labels = labels
)
self.log('train_loss', loss, prog_bar = True, logger = True)
return loss
def validation_step(self, batch, batch_idx):
input_ids = batch['text_input_ids']
attention_mask = batch['text_attention_mask']
l = batch['labels'].float()
l = torch.tensor(l/torch.linalg.norm(l))
labels = l
labels_attention_mask = batch['labels_attention_mask']
loss, outputs = self(
input_ids = input_ids,
attention_mask = attention_mask,
labels = labels
)
self.log('val_loss', loss, prog_bar = True, logger = True)
return loss
def test_step(self, batch, batch_idx):
input_ids = batch['text_input_ids']
attention_mask = batch['text_attention_mask']
l = batch['labels'].float()
l = torch.tensor(l/torch.linalg.norm(l))
labels = l
labels_attention_mask = batch['labels_attention_mask']
loss, outputs = self(
input_ids = input_ids,
attention_mask = attention_mask,
labels = labels
)
self.log('test_loss', loss, prog_bar = True, logger = True)
return loss
def configure_optimizers(self):
return AdamW(self.parameters(), lr=0.0001)
使用pl.Trainer返回的培训ValueError: Expected input batch_size (20) to match target batch_size (50).
我无法得到错误。