I tired and can't solve this error , ValueError: The model did not return a loss from the inputs, only the following keys: logits. For reference, the inputs it received are input_ids,attention_mask
i have 24 labels and my data are look like thistext   | labelsfrom datasets import load_dataset

data = load_dataset('csv',data_files='/content/drive/MyDrive/new_stories.csv')

# Create a dictionary to map IDs to text
id2text = {i: word for i, word in enumerate(data['labels'].unique())}

# Create a dictionary to map text to IDs
text2id = {word: i for i, word in enumerate(data['labels'].unique())}

# Print the mappings
print("ID to Text Mapping: ", id2text)
print("Text to ID Mapping: ", text2id)

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")

def preprocess_function(examples):
    return tokenizer(examples["story"], truncation=True)

tokenized_data = data.map(preprocess_function, batched=True,num_proc=10)

from transformers import DataCollatorWithPadding

data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer

model = AutoModelForSequenceClassification.from_pretrained(
    "distilbert-base-uncased", num_labels=24, id2label=id2text, label2id=text2id
)

import evaluate
accuracy = evaluate.load("accuracy")

import numpy as np

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=-1)
    return accuracy.compute(predictions=predictions, references=labels)

training_args = TrainingArguments(
    output_dir="text_classification",
    evaluation_strategy="steps",
    eval_steps=500,
    learning_rate=2e-5,
    per_device_train_batch_size=2,
    per_device_eval_batch_size=8,
    num_train_epochs=3,
    weight_decay=0.01,
    fp16=True,
    gradient_accumulation_steps=16,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_data["train"],
    eval_dataset=tokenized_data["validation"],
    tokenizer=tokenizer,
    data_collator=data_collator,
    compute_metrics=compute_metrics,
)

trainer.train()show me this errorValueError: The model did not return a loss from the inputs, only the following keys: logits. For reference, the inputs it received are input_ids,attention_mask.
2023-03-29T10:28:37Z
@sguggerplease answer me