# ValueError: Expected input batch\_size (8) to match target batch\_size (280)

**URL:** <https://discuss.huggingface.co/t/valueerror-expected-input-batch-size-8-to-match-target-batch-size-280/59441>\
**Category:** Beginners\
**Created:** [October 22, 2023, 11:31am UTC](https://discuss.huggingface.co/t/valueerror-expected-input-batch-size-8-to-match-target-batch-size-280/59441 "2023-10-22T11:31:32Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![LohTC](https://avatars.discourse-cdn.com/v4/letter/l/838e76/32.png) [@LohTC](https://discuss.huggingface.co/u/LohTC)\
**Post date:** [October 22, 2023, 11:31am UTC](https://discuss.huggingface.co/t/valueerror-expected-input-batch-size-8-to-match-target-batch-size-280/59441/1 "2023-10-22T11:31:32Z")

</div>

Hi folks,

I’m newbie and got stuck by this error msg “ValueError: Expected input batch\_size (8) to match target batch\_size (280)”.

Appreciate any advice, please… Thank you!

```auto
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")

def preprocess_function(examples):
    model_inputs = tokenizer(
        text = examples["text"],
        max_length=max_text_length,
        truncation=True,
        padding = True,
    )
    label = tokenizer(examples["label"], max_length=max_label_length, truncation=True, padding = True)
    model_inputs["label"] = label["input_ids"]
    return model_inputs

tokenized_aicgp = aicgp.map(preprocess_function, batched=True)

from transformers import DataCollatorWithPadding
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=no_of_labels, id2label=id2label, label2id=label2id)

training_args = TrainingArguments(
    output_dir="my_model",
    learning_rate=2e-5,
    per_device_train_batch_size=8, #16
    per_device_eval_batch_size=8, #16
    num_train_epochs=2,
    weight_decay=0.01,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    load_best_model_at_end=True,
    push_to_hub=False,
)
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_aicgp["train"],
    eval_dataset=tokenized_aicgp["test"],
    tokenizer=tokenizer,
    data_collator=data_collator,
    compute_metrics=compute_metrics,
)
trainer.train()

Note:

The program aborted at trainer.train() with the below error message:
ValueError: Expected input batch_size (8) to match target batch_size (280).

tokenized_aicgp

DatasetDict({
    train: Dataset({
        features: ['label', 'text', 'lblId', 'input_ids', 'attention_mask'],
        num_rows: 56
    })
    test: Dataset({
        features: ['label', 'text', 'lblId', 'input_ids', 'attention_mask'],
        num_rows: 14
    })
})

length label 56
length text 56
length lblId 56
length input_ids 56
length attention_mask 56

```

---

<div class="post-metadata">

**Author:** ![miguelpr](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/miguelpr/32/34586_2.png) [@miguelpr](https://discuss.huggingface.co/u/miguelpr)\
**Post date:** [November 18, 2024, 8:36pm UTC](https://discuss.huggingface.co/t/valueerror-expected-input-batch-size-8-to-match-target-batch-size-280/59441/2 "2024-11-18T20:36:14Z")

</div>

Work for me:

```auto
def tokenize_function(examples):
    inputs = tokenizer(examples["text"] , truncation=True , padding="max_length" , max_length=128 )
    inputs['labels'] = inputs['input_ids'].copy()
    return inputs

```

See an example: [Fine-Tuning a Pre-Trained GPT-2 Model and Performing Inference: A Hands-On Guide | by prashanth ramanathan | Medium](https://medium.com/@prashanth.ramanathan/fine-tuning-a-pre-trained-gpt-2-model-and-performing-inference-a-hands-on-guide-57c097a3b810)