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*This model was published in HF papers on 2019-10-02 and contributed to Hugging Face Transformers on 2020-11-16.*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
    </div>
</div>

# DistilBERT

[DistilBERT](https://huggingface.co/papers/1910.01108) is pretrained by knowledge distillation to create a smaller model with faster inference and requires less compute to train. Through a triple loss objective during pretraining, language modeling loss, distillation loss, cosine-distance loss, DistilBERT demonstrates similar performance to a larger transformer language model.

You can find all the original DistilBERT checkpoints under the [DistilBERT](https://huggingface.co/distilbert) organization.

> [!TIP]
> Click on the DistilBERT models in the right sidebar for more examples of how to apply DistilBERT to different language tasks.

The example below demonstrates how to classify text with [`Pipeline`], [`AutoModel`], and from the command line.

<hfoptions id="usage">

<hfoption id="Pipeline">

```python
from transformers import pipeline


classifier = pipeline(
    task="text-classification",
    model="distilbert-base-uncased-finetuned-sst-2-english",
    device=0
)

result = classifier("I love using Hugging Face Transformers!")
print(result)
# Output: [{'label': 'POSITIVE', 'score': 0.9998}]
```

</hfoption>

<hfoption id="AutoModel">

```python
import torch

from transformers import AutoModelForSequenceClassification, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained(
    "distilbert/distilbert-base-uncased-finetuned-sst-2-english",
)
model = AutoModelForSequenceClassification.from_pretrained(
    "distilbert/distilbert-base-uncased-finetuned-sst-2-english",
    device_map="auto",
    attn_implementation="sdpa"
)
inputs = tokenizer("I love using Hugging Face Transformers!", return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model(**inputs)

predicted_class_id = torch.argmax(outputs.logits, dim=-1).item()
predicted_label = model.config.id2label[predicted_class_id]
print(f"Predicted label: {predicted_label}")
```

</hfoption>

</hfoptions>

## Notes

- DistilBERT doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just
  separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`).
- DistilBERT doesn't have options to select the input positions (`position_ids` input). This could be added if
  necessary though, just let us know if you need this option.

## DistilBertConfig

[[autodoc]] DistilBertConfig

## DistilBertTokenizer

[[autodoc]] DistilBertTokenizer

## DistilBertTokenizerFast

[[autodoc]] DistilBertTokenizerFast

## DistilBertModel

[[autodoc]] DistilBertModel
    - forward

## DistilBertForMaskedLM

[[autodoc]] DistilBertForMaskedLM
    - forward

## DistilBertForSequenceClassification

[[autodoc]] DistilBertForSequenceClassification
    - forward

## DistilBertForMultipleChoice

[[autodoc]] DistilBertForMultipleChoice
    - forward

## DistilBertForTokenClassification

[[autodoc]] DistilBertForTokenClassification
    - forward

## DistilBertForQuestionAnswering

[[autodoc]] DistilBertForQuestionAnswering
    - forward
