---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 373461e16c521cf6568669d8a3defa30d5cb4c1f48d53a1b140e7c73fbcf6f9d
  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-for-transformers
    - DCFT
  citations:
    - file: fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md
title: Data Collation for Transformers
description: Using DataCollatorWithPadding to batch input sequences of varying lengths for training and evaluation of transformer models.
tags:
  - huggingface
  - data-processing
  - nlp
timestamp: "2026-06-19T18:49:51.596Z"
---

# Data Collation for Transformers

**Data collation** is the step in the Hugging Face Transformers training workflow that batches tokenized inputs from a dataset and applies necessary post-processing (such as padding) before feeding them into the model. The collator is passed to the Trainer class and is called dynamically for each batch during training and evaluation. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Common Data Collators

Hugging Face provides several built-in data collators. The source material highlights [DataCollatorWithPadding](/concepts/datacollatorwithpadding.md) as the recommended collator for text classification tasks:

- **DataCollatorWithPadding** – Dynamically pads the inputs in each batch to the length of the longest sequence in that batch. This gives good baseline performance for text classification and avoids wasting computation on unnecessary padding. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

Other collators in the library (such as `DataCollatorForTokenClassification` or `DataCollatorForSeq2Seq`) are not mentioned in the source and are outside the scope of this page.

## Usage

To use a data collator in a training script, instantiate it with the tokenizer and pass it to the `Trainer`:

```python
from transformers import DataCollatorWithPadding

data_collator = DataCollatorWithPadding(tokenizer)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    compute_metrics=compute_metrics,
    data_collator=data_collator,
)
```

The collator is then applied automatically to every batch drawn from the dataset. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Best Practices

- Use `DataCollatorWithPadding` for text classification and sequence classification tasks that use a standard `AutoTokenizer`. It ensures efficient batching without manually fixing a `max_length`. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]
- For token-level tasks (e.g., named entity recognition) or sequence-to-sequence tasks, consult the Hugging Face documentation for task-specific collators.
- When memory is constrained, dynamic padding (as used by `DataCollatorWithPadding`) can help fit larger batch sizes compared to static padding to a fixed maximum length.

## Related Concepts

- Hugging Face Transformers – The library that provides data collators.
- Trainer – The training utility that accepts a data collator.
- Tokenization – The step before collation that produces token IDs.
- [DataCollatorWithPadding](/concepts/datacollatorwithpadding.md) – The specific collator recommended for text classification.
- Fine-tuning Transformers on a Single GPU – The broader workflow that includes data collation.
- MLflow Integration – How to log the trained model and its components after training.

## 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)
