"ValueError: Target size must be same as input size" when training twitter-roberta-base-emotion-multilabel-latest
Hello,I am trying to tweak/train a pretrained model (cardiffnlp/twitter-roberta-base-sentiment-latest), and am getting the following error when I try to run the Trainer.train command:raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
ValueError: Target size (torch.Size([7])) must be the same as input size (torch.Size([7, 11]))Note: the test dataset I am using for the tuning is 7 lines, just for testing. It’s format is,text,labels
0, "some text here", 2
1, "some text here", 0
2, "some text here", 9
...I have successfully managed to run the same code on both the twitter-roberta-base-sentiment-latest and the roberta-base-emotion models.Here is a minimum example that produces the error with the mentioned model, and works for the others:from numpy import argmax 
from evaluate import load as evload
from datasets import load_dataset
from transformers import TrainingArguments, Trainer, DataCollatorWithPadding
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig

def compute_metrics(eval_pred) -> str:
        load_accuracy = evload("accuracy") # load_metric("accuracy")
        load_f1 = evload("f1") #load_metric("f1")

        logits, labels = eval_pred
        predictions = argmax(logits)
        accuracy = load_accuracy.compute(predictions=predictions, references=labels)["accuracy"]
        f1 = load_f1.compute(predictions=predictions, references=labels)["f1"]
        return {"accuracy": accuracy, "f1": f1}

def tokenization(examples):
    return tokenizer(examples["text"])#, truncation=True)

MODEL = 'cardiffnlp/roberta-base-emotion'

model = AutoModelForSequenceClassification.from_pretrained(MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
config = AutoConfig.from_pretrained(MODEL)

data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

dataset = load_dataset("csv", data_files={"train": "emotion_train_data.csv"})

tokenized_train = dataset["train"].map(tokenization, batched=True)

training_args = TrainingArguments(
            output_dir="trained_model",
            learning_rate=2e-5,
            per_device_train_batch_size=16,
            num_train_epochs=2,
            weight_decay=0.01,
            save_strategy="epoch",
            push_to_hub=False,
)

trainer = Trainer(
                model=model,
                args=training_args,
                train_dataset=tokenized_train,
                tokenizer=tokenizer,
                data_collator=data_collator,
                compute_metrics=compute_metrics,
)

trainer.train()Any advice on this would be great!
2023-05-02T06:33:59Z
Just a correction to add, since I noticed that the I had mentioned the wrong model regarding the one that is not working in the post text and in the example code:The one giving me the issues is thecardiffnlp/twitter-roberta-base-emotion-multilabel-latestSo, in the exampel code it should be:MODEL = 'cardiffnlp/twitter-roberta-base-emotion-multilabel-latest'