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*This model was released on 2019-10-02 and added to Hugging Face Transformers on 2020-11-16 and contributed by [victorsanh](https://huggingface.co/victorsanh).*

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

[DistilBERT](https://huggingface.co/papers/1910.01108) is a smaller, faster, and more cost-effective Transformer model derived from BERT. By distilling BERT during the pretraining phase, DistilBERT reduces the number of parameters by 40% and increases speed by 60%, while maintaining over 95% of BERT's performance on the GLUE benchmark. The model uses a triple loss function that includes language modeling, distillation, and cosine-distance losses to capture the inductive biases of larger models. This makes DistilBERT suitable for on-device computations and constrained environments.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="fill-mask", model="distilbert/distilbert-base-uncased", dtype="auto")
pipeline("Plants create [MASK] through a process known as photosynthesis.")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

model = AutoModelForMaskedLM.from_pretrained("distilbert/distilbert-base-uncased", dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")

inputs = tokenizer("Plants create [MASK] through a process known as photosynthesis.", return_tensors="pt")
outputs = model(**inputs)
mask_token_id = tokenizer.mask_token_id
mask_position = (inputs.input_ids == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
predicted_word = tokenizer.decode(outputs.logits[0, mask_position].argmax(dim=-1))
print(f"Predicted word: {predicted_word}")
```

</hfoption>
</hfoptions>

## Usage tips

- DistilBERT doesn't have `token_type_ids`. You don't need to indicate which token belongs to which segment. Just separate segments with `tokenizer.sep_token` (or `[SEP]`).
- DistilBERT doesn't support `position_ids` input. This could be added if needed.

## 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

