# "ValueError: Target size must be same as input size" when training twitter-roberta-base-emotion-multilabel-latest

**URL:** <https://discuss.huggingface.co/t/valueerror-target-size-must-be-same-as-input-size-when-training-twitter-roberta-base-emotion-multilabel-latest/38178>\
**Category:** Beginners\
**Created:** [April 30, 2023, 8:30am UTC](https://discuss.huggingface.co/t/valueerror-target-size-must-be-same-as-input-size-when-training-twitter-roberta-base-emotion-multilabel-latest/38178 "2023-04-30T08:30:03Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![nevar](https://avatars.discourse-cdn.com/v4/letter/n/c4cdca/32.png) [@nevar](https://discuss.huggingface.co/u/nevar)\
**Post date:** [April 30, 2023, 8:30am UTC](https://discuss.huggingface.co/t/valueerror-target-size-must-be-same-as-input-size-when-training-twitter-roberta-base-emotion-multilabel-latest/38178/1 "2023-04-30T08:30:04Z")

</div>

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:

```auto
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

```auto
,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:

```auto
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!

---

<div class="post-metadata">

**Author:** ![nevar](https://avatars.discourse-cdn.com/v4/letter/n/c4cdca/32.png) [@nevar](https://discuss.huggingface.co/u/nevar)\
**Post date:** [May 2, 2023, 6:33am UTC](https://discuss.huggingface.co/t/valueerror-target-size-must-be-same-as-input-size-when-training-twitter-roberta-base-emotion-multilabel-latest/38178/2 "2023-05-02T06:33:59Z")

</div>

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 the

> cardiffnlp/twitter-roberta-base-emotion-multilabel-latest

So, in the exampel code it should be:

```auto
MODEL = 'cardiffnlp/twitter-roberta-base-emotion-multilabel-latest'

```