**RuntimeError: 0D or 1D target tensor expected, multi-target not supported **error is raised when the target tensor (labels) has an incorrect shape. In PyTorch, the ** CrossEntropyLoss** expects the target tensor to have a 1D shape.

To **fix** this error, make sure your labels tensor has the correct shape. The **tensor** should be (batch_size,), where **batch_size** is the number of samples in your input. The example assumes a single input sample, so the shape should be** (1,)**.

Each element in this target tensor should be a class index in the range [0, C-1], where C is the number of classes.

If the target tensor is not in this format (for example, if it’s a 2D tensor or one-hot encoded), this error will occur.

```
import torch
from transformers import BertTokenizer, BertForSequenceClassification
from torch.nn import CrossEntropyLoss
# Loading the model and tokenizer
model_name = "bert-base-uncased"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)
# Prepare your input
input_text = "Your input text goes here"
tokens = tokenizer(input_text, return_tensors="pt")
input_ids = tokens["input_ids"]
attention_mask = tokens["attention_mask"]
# Forward pass to get the logits
logits = model(input_ids, attention_mask=attention_mask).logits
# Prepare your labels
labels = torch.tensor([0]) # Assuming you have a binary classification task and a single input sample
# Compute the loss
loss_function = CrossEntropyLoss()
loss = loss_function(logits, labels)
print(loss)
```

**Output**

For **nn.CrossEntropyLoss**, the target has to be a single number from the interval [0, #classes] instead of a one-hot encoded target vector. Your target is [1, 0]. Thus, PyTorch thinks you want multiple labels per input, which is unsupported.

You can replace your one-hot-encoded targets like this:

```
[1, 0] --> 0
[0, 1] --> 1
```

**Correct Target Tensor Shape for a Single Sample**

If you have a single input sample, your target tensor should be of shape (1,) with the single element being the class index.

For example: **target = torch.tensor([class_index])** where **class_index** is the integer representing the class.

**Correct Target Tensor Shape for Batch:**

For a batch of samples, the target tensor should be a 1D tensor, with each element corresponding to the class index of each sample in the batch.

For example: **target = torch.tensor([class_index1, class_index2, …, class_indexN])** for a batch of N samples.

Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.