Hi,
I have a multilabel task (num_labels=8) and I want to use BertForSequenceClassification using Trainer to train the model.
But I get the following error:

ValueError: Expected input batch_size (8) to match target batch_size (64).

I assume that the problem is the data format of the labels. Currently, my label is a 8-dim list (e.g., [1,0,0,0,0,1,0,0]).
What is the right format for the label data?
Here my code:

class EmotionDataset(torch.utils.data.Dataset):
def init(self, encodings, labels):
self.encodings = encodings
self.labels = 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

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

MODEL_NAME = ‘dbmdz/bert-base-german-uncased’
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = BertForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=8)
tokenize data
dataset_train = Dataset.from_pandas(df_train)
train_encodings = tokenizer(dataset_train['text], truncation=True, padding=True)
train_dataset = EmotionDataset(train_encodings, dataset_train['label])
training_args = TrainingArguments(
output_dir=’./results’,          # output directory
num_train_epochs=1,              # total # of training epochs
per_device_train_batch_size=8,  # batch size per device during training
per_device_eval_batch_size=32,   # batch size for evaluation
warmup_steps=500,                # number of warmup steps for learning rate scheduler
weight_decay=0.01,               # strength of weight decay
logging_dir=’./logs’,            # directory for storing logs
)
trainer = Trainer(
model=model,                         # the instantiated  Transformers model to be trained
args=training_args,                  # training arguments, defined above
train_dataset=train_dataset,         # training dataset
eval_dataset=test_dataset            # evaluation dataset
)
_ = trainer.train()
trainer.evaluate()

Thanks,
Max