Mismatched target and input size for BCE using "multi_label_classification"

Intermediate
1

I am trying to build a multi-label, multi-class classification model. Any input text can have zero or more labels, up to 11 possible classes.

I have been trying to use the problem_type="multi_label_classification" and everything looks OK, but I get ValueError: Target size (torch.Size([16, 11])) must be the same as input size (torch.Size([16, 2])) when it tries to calculate the binary_cross_entropy_with_logits

I presume my data is in the wrong shape somehow, but I can’t see where exactly. Any suggestions?

transformers==4.8.2

Here is a minimal example:

import torch
from torch.utils.data.dataset import Dataset

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Example data. 
# In reality, the strings are usually longer and there are 11 possible classes
texts = [
    "This is the first sentence.",
    "This is the second sentence.",
    "This is another sentence.",
    "Finally, the last sentence.",
]

labels = [
    [0, 0, 0, 0, 1],
    [1, 0, 0, 0, 0],
    [0, 1, 1, 0, 0],
    [0, 0, 0, 0, 0],
]

train_texts = texts[:2]
train_labels = labels[:2]

eval_texts = texts[2:]
eval_labels = labels[2:]

tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)

train_encodings = tokenizer(train_texts, padding="max_length", truncation=True, max_length=512)
eval_encodings = tokenizer(eval_texts, padding="max_length", truncation=True, max_length=512)


class TextClassifierDataset(Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels

    def __len__(self):
        return len(self.labels)

    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item["labels"] = torch.tensor(self.labels[idx])
        return item

train_dataset = TextClassifierDataset(train_encodings, train_labels)
eval_dataset = TextClassifierDataset(eval_encodings, eval_labels)

model = AutoModelForSequenceClassification.from_pretrained(
    "bert-base-uncased", 
    problem_type="multi_label_classification",
)

training_arguments = TrainingArguments(
    output_dir=".",
    evaluation_strategy="epoch",
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=1,
)

trainer = Trainer(
    model=model,
    args=training_arguments,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
)

trainer.train()

# long traceback, but here is the important bit...
~/python3.8/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
    711 
    712     def forward(self, input: Tensor, target: Tensor) -> Tensor:
--> 713         return F.binary_cross_entropy_with_logits(input, target,
    714                                                   self.weight,
    715                                                   pos_weight=self.pos_weight,

~/python3.8/site-packages/torch/nn/functional.py in binary_cross_entropy_with_logits(input, target, weight, size_average, reduce, reduction, pos_weight)
   2956 
   2957     if not (target.size() == input.size()):
-> 2958         raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
   2959 
   2960     return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum)

ValueError: Target size (torch.Size([16, 11])) must be the same as input size (torch.Size([16, 2]))
1 Like
2

When defining your model, you did not specify the number of labels (with num_labels=xxx).

4 Likes
3

Changes needed to run the above code are below:

model = AutoModelForSequenceClassification.from_pretrained(
“bert-base-uncased”,
problem_type=“multi_label_classification”,
num_labels=5
)

and changing label to make it float:


labels = [
    [0.0, 0.0, 0.0, 0.0, 1.0],
    [1.0, 0.0, 0.0, 0.0, 0.0],
    [0.0, 1.0, 1.0, 0.0, 0.0],
    [0.0, 0.0, 0.0, 0.0, 0.0],
]
2 Likes