---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 97c10589b5ad0cc67ec7d86a282e0aedf36e7dd57a20466e8553f47e891b9bef
  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-for-transformers
    - AFT
    - AutoTokenizer
  citations:
    - file: fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md
title: AutoTokenizer for Transformers
description: A Hugging Face Auto class that loads the tokenizer corresponding to a pre-trained model, ensuring consistent tokenization across training and inference datasets.
tags:
  - huggingface
  - tokenization
  - nlp
timestamp: "2026-06-19T10:32:34.540Z"
---

# AutoTokenizer for Transformers

**AutoTokenizer** is a class from the Hugging Face `transformers` library that automatically loads the appropriate tokenizer for a given pretrained model. It eliminates the need to manually specify which tokenizer class corresponds to a particular model architecture.

## Overview

When fine-tuning Hugging Face Transformers models, input text must be converted into tokenized format before it can be passed to the model. The `AutoTokenizer` class handles this conversion automatically by loading the correct tokenizer based on the model identifier provided. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

The primary use case for `AutoTokenizer` is during dataset preparation for training or fine-tuning. Tokenized input is required by all Hugging Face Transformers models, as they operate on numerical token IDs rather than raw text. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Usage

To use `AutoTokenizer`, import it from the `transformers` library and call `from_pretrained()` with the name or path of your base model:

```python
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(base_model)
```

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

Once loaded, the tokenizer can be applied to text data using the `.map()` function from the Hugging Face `datasets` library. This ensures that tokenization is applied consistently to both training and testing datasets:

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

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

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

### Key Parameters

- **`padding`**: Controls whether sequences are padded to equal length. Setting `padding=False` keeps sequences at their original length.
- **`truncation`**: Controls whether sequences longer than the model's maximum length are truncated. Setting `truncation=True` truncates exceeding tokens.

## Integration with Other Components

### Data Collators

Tokenized datasets often use a Data Collator to batch inputs during training. `DataCollatorWithPadding` is commonly used with `AutoTokenizer` to dynamically pad batches to the length of the longest sequence:

```python
from transformers import DataCollatorWithPadding

data_collator = DataCollatorWithPadding(tokenizer)
```

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

### MLflow Logging

When logging fine-tuned models to [MLflow](/concepts/mlflow.md), the tokenizer can be included as part of a [Transformers pipeline](/concepts/mlflow-transformers-flavor.md) or passed directly to `mlflow.transformers.log_model()`:

```python
# As part of a pipeline
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
model_info = mlflow.transformers.log_model(
    transformers_model=pipe,
    artifact_path="classification",
)

# Or as a separate component
model_info = mlflow.transformers.log_model(
    transformers_model={"model": model, "tokenizer": tokenizer},
    task="text-classification",
    artifact_path="text_classifier",
)
```

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

## Related Concepts

- AutoModel — The model class counterpart that automatically loads the correct model architecture
- Hugging Face Datasets — The library used for efficient data loading and preprocessing
- [Hugging Face Trainer](/concepts/hugging-face-trainer.md) — The training utility that accepts tokenized datasets
- Fine-tuning Hugging Face Models on Databricks — Complete workflow for fine-tuning on single GPU
- [MLflow Transformers Integration](/concepts/mlflow-transformers-flavor.md) — Logging and serving Transformers models with MLflow

## 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)
