---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 54e19cbe6f4dc3a80319f7b53b5fded57deef686af11232244e716d8c74bcc53
  pageDirectory: concepts
  sources:
    - fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md
  confidence: 0.95
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - autotokenizer-and-tokenization-for-fine-tuning
    - Tokenization for Fine-tuning and AutoTokenizer
    - AATFF
  citations:
    - file: fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md
title: AutoTokenizer and Tokenization for Fine-tuning
description: Using AutoTokenizer with Hugging Face datasets to tokenize text for model training
tags:
  - hugging-face
  - nlp
  - data-preprocessing
timestamp: "2026-06-18T12:20:07.533Z"
---

# AutoTokenizer and Tokenization for Fine-tuning

**AutoTokenizer** is a Hugging Face Transformers utility that automatically loads the correct tokenizer for a given pre-trained model. When fine-tuning Hugging Face [Transformers models](/concepts/mlflow-transformers-flavor.md), tokenization is a critical preprocessing step because models expect tokenized numeric input rather than raw text. The `AutoTokenizer` class ensures that the tokenizer configuration—including vocabulary, special tokens, and normalization rules—matches the base model that will be fine-tuned. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Loading an AutoTokenizer

The tokenizer is loaded using `AutoTokenizer.from_pretrained()`, passing the identifier of the base model. This identifier is the same string used when loading the model itself, such as a model name from the Hugging Face Hub or a local path to a saved model. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

```python
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(base_model)
```

The loaded tokenizer can then be applied to both training and testing datasets to ensure consistent preprocessing. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Tokenizing a Dataset

Hugging Face Datasets library allows you to apply the tokenizer to an entire dataset using the `.map()` method. This approach processes all examples with the same tokenizer configuration, producing a tokenized dataset ready for training. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

### Example Tokenization Function

```python
def tokenize_function(examples):
    return tokenizer(examples["text"], padding=False, truncation=True)
```

The function accepts a batch of examples from the dataset and passes the text column to the tokenizer. Key parameters control how sequences are handled:

- **`padding=False`**: Sequences are not padded to equal length during tokenization. Padding is typically handled later by a [data collator](/concepts/datacollatorwithpadding.md) during batching, which is more memory-efficient.
- **`truncation=True`**: Sequences exceeding the model's maximum length are truncated, preventing errors during training.

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

### Applying Tokenization to the Dataset

```python
train_test_tokenized = train_test_dataset.map(tokenize_function, batched=True)
```

The `batched=True` parameter processes examples in batches, which is significantly faster than processing one example at a time. The resulting `train_test_tokenized` dataset contains tokenized inputs (typically `input_ids`, `attention_mask`, and optionally `token_type_ids`) alongside the original label columns. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Data Collation for Batching

After tokenization, a [DataCollatorWithPadding](/concepts/datacollatorwithpadding.md) is typically used to dynamically pad sequences to the same length within each training batch. This approach is more efficient than padding all sequences to the maximum length of the entire dataset. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

```python
from transformers import DataCollatorWithPadding

data_collator = DataCollatorWithPadding(tokenizer)
```

The data collator uses the tokenizer's padding token (`tokenizer.pad_token_id`) and understands the model's expected input format, making it compatible with the Trainer API. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Best Practices

- **Use the same tokenizer as the base model.** Always load the tokenizer from the same model identifier used for `AutoModelForSequenceClassification` or other Auto Model classes. Using an incompatible tokenizer can produce incorrect token IDs and degrade model performance. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]
- **Apply truncation during tokenization.** Set `truncation=True` to prevent sequence length errors. The model's maximum input size is determined by the tokenizer's `model_max_length` attribute. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]
- **Defer padding to the data collator.** Avoid padding during the initial tokenization step (`padding=False`). Use `DataCollatorWithPadding` during training to pad dynamically per batch, reducing memory usage and training time. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]
- **Tokenize with batching.** Use `batched=True` in the `.map()` call to process multiple examples at once, which is significantly faster than individual processing. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Relationship to MLflow Logging

When a fine-tuned model is logged to [MLflow](/concepts/mlflow.md) using `mlflow.transformers.log_model()`, the tokenizer is automatically included as part of the logged model artifact. This ensures that the same tokenizer is used during inference, preserving preprocessing consistency. The tokenizer can be included either as part of a [Transformers pipeline](/concepts/mlflow-transformers-flavor.md) or as a component in a dictionary passed to the logging function. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Related Concepts

- [AutoModelForSequenceClassification](/concepts/automodelforsequenceclassification.md) — The Auto Model class for text classification fine-tuning
- Transformer Trainer — The Hugging Face training utility that consumes tokenized datasets
- Data Collation — Dynamic padding during batching for efficient training
- Hugging Face Datasets — The library for loading and preprocessing datasets
- Fine-tuning on a Single GPU — Hardware considerations for training
- [MLflow Transformers Integration](/concepts/mlflow-transformers-flavor.md) — Logging tokenizers and models for governance

## 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)
