---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 012f313f1284ef243026b791c17df19077ba73860197548fb00f4c3d450bea52
  pageDirectory: concepts
  sources:
    - fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - data-collation-with-datacollatorwithpadding
    - DCWD
  citations:
    - file: fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md
title: Data Collation with DataCollatorWithPadding
description: Using Hugging Face data collators to batch input sequences with dynamic padding for training and evaluation
tags:
  - hugging-face
  - data-processing
  - nlp
timestamp: "2026-06-18T12:20:35.042Z"
---

# Data Collation with DataCollatorWithPadding

**DataCollatorWithPadding** is a utility from the Hugging Face Transformers library that dynamically pads input sequences in a batch to the maximum length within that batch, enabling efficient training and evaluation of transformer models. It is part of the [data collator](/concepts/datacollatorwithpadding.md) family in Hugging Face and provides a good baseline performance for text classification tasks. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## How It Works

DataCollatorWithPadding takes a tokenizer and creates batches of training and evaluation data by padding each sequence to match the longest sequence in the batch. This ensures all inputs have uniform length, which is required for GPU tensor operations, while minimizing wasted computation on unnecessary padding tokens. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

The padding is applied dynamically at each batch creation step, meaning the padding length can vary between batches depending on the actual sequence lengths present. This is more efficient than padding all samples to a fixed maximum length. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Usage with Hugging Face Trainer

When using the [Hugging Face Trainer](/concepts/hugging-face-trainer.md) API, DataCollatorWithPadding is passed as the `data_collator` argument. It works with both the `train_dataset` and `eval_dataset` to create consistent batches during training and evaluation. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

### Basic Setup

```python
from transformers import DataCollatorWithPadding

data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
```

### Integration with Trainer

```python
from transformers import Trainer

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_test_dataset["train"],
    eval_dataset=train_test_dataset["test"],
    compute_metrics=compute_metrics,
    data_collator=data_collator,
)
```

^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Key Features

- **Dynamic padding**: Pads to the maximum length in the current batch, not a global maximum.
- **Token-aware**: Uses the provided tokenizer's padding token and padding side settings.
- **Efficient batching**: Minimizes GPU memory usage by avoiding fixed-length padding across all samples.
- **Automatic handling**: Works seamlessly with Hugging Face's batch processing pipeline.

## Benefits for Text Classification

DataCollatorWithPadding gives good baseline performance for text classification tasks, as the padding strategy maintains computational efficiency while preserving model accuracy. For classification tasks where sequence lengths may vary significantly, dynamic padding provides consistent results without requiring manual padding configuration. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Related Concepts

- [Hugging Face Trainer](/concepts/hugging-face-trainer.md) — The training utility that accepts data collators
- [Data collator](/concepts/datacollatorwithpadding.md) — The broader Hugging Face concept for creating training batches
- [AutoTokenizer](/concepts/autotokenizer-for-transformers.md) — Tokenizer used with DataCollatorWithPadding
- Tokenization — Process of converting text to model-ready tokens
- [Batch processing](/concepts/unified-streaming-and-batch-processing.md) — Grouping samples for efficient GPU computation

## Sources

- fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md

# Citations

1. [fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md](/references/fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws-e2c49846.md)
