英文:
Add a Classification Head on Top of Huggingface Vilt Model
问题
我想在huggingface vilt transformer的基础上添加一个分类层，以便对我的文本标签进行分类。
通常情况下，vilt接受一对图像和问题，然后在前向传递后输出问题的答案。
我想将这个任务变成一个分类任务，而不是文本生成任务。我有一组标签，我希望vilt能够分配哪个标签最有可能成为给定问题的答案。
我对transformers完全不了解，对如何实现这个任务几乎一无所知。有人可以帮助我吗？
我查看了这篇中等博客文章，但无法理解其中的内容。
英文:
I want to add a classification layer in pytorch on top of the huggingface vilt transformer, so that I can classify my text labels.
Generally in normal settings vilt takes an image, question pair and outputs the answer of the question after forward pass
I Want to make the task a classification task instead of a text generation task. I have a set of labels which I want the vilt to assign which label has the highest probability of being the answer of the given question.
I'm completely new to the transformers and have very little idea of how this task can be achieved. Can someone please help me?
I checked this medium blog but couldn't make sense out of it.
答案1
得分: 2
你可以在Vilt模型的顶部添加自己的Classification_Head。
这只是概述，根据你的要求进行更改。
class ClassificationHead(nn.Module):
def __init__(self, input_size, num_classes):
super(ClassificationHead, self).__init__()
self.fc = nn.Linear(input_size, num_classes)
def forward(self, x):
return self.fc(x)
# 定义你的类别数量
num_classes = .. # 你的分类任务中的类别数量
# 创建分类头
classification_head = ClassificationHead(vilt_model.config.hidden_size, num_classes)
# 训练循环
for epoch in range(num_epochs):
for batch in dataloader:
inputs = batch["input_ids"]
labels = batch["labels"]
# 前向传播
outputs = vilt_model(inputs).last_hidden_state[:, 0, :]
logits = classification_head(outputs)
# 计算损失
loss = criterion(logits, labels)
# 反向传播
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 推断
with torch.no_grad():
inputs = .. # 准备你的输入数据
outputs = vilt_model(inputs).last_hidden_state[:, 0, :]
logits = classification_head(outputs)
predicted_labels = logits.argmax(dim=1)
英文:
You can add your own Classification_Head on top of Vilt Model.
This is simply the overview, make changes as per you requirements
class ClassificationHead(nn.Module):
def __init__(self, input_size, num_classes):
super(ClassificationHead, self).__init__()
self.fc = nn.Linear(input_size, num_classes)
def forward(self, x):
return self.fc(x)
# Define your number of classes
num_classes = .. # Number of classes in your classification task
# Create the classification head
classification_head = ClassificationHead(vilt_model.config.hidden_size, num_classes)
# Training Loop
for epoch in range(num_epochs):
for batch in dataloader:
inputs = batch["input_ids"]
labels = batch["labels"]
# Forward pass
outputs = vilt_model(inputs).last_hidden_state[:, 0, :]
logits = classification_head(outputs)
# Calculate loss
loss = criterion(logits, labels)
# Backpropagation
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Inference
with torch.no_grad():
inputs = .. # Prepare your input data
outputs = vilt_model(inputs).last_hidden_state[:, 0, :]
logits = classification_head(outputs)
predicted_labels = logits.argmax(dim=1)
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