huggingface--transformers/docs/source/en/model_doc/bert.md

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*This model was published in HF papers on 2018-10-11 and contributed to Hugging Face Transformers on 2020-11-16.*

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        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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# BERT

[BERT](https://huggingface.co/papers/1810.04805) is a bidirectional transformer pretrained on unlabeled text to predict masked tokens in a sentence and to predict whether one sentence follows another. The main idea is that by randomly masking some tokens, the model can train on text to the left and right, giving it a more thorough understanding. BERT is also very versatile because its learned language representations can be adapted for other NLP tasks by fine-tuning an additional layer or head.

You can find all the original BERT checkpoints under the [BERT](https://huggingface.co/collections/google/bert-release-64ff5e7a4be99045d1896dbc) collection.

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

The example below demonstrates how to predict the `[MASK]` token with [`Pipeline`], [`AutoModel`], and from the command line.

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

```python
from transformers import pipeline


pipeline = pipeline(
    task="fill-mask",
    model="google-bert/bert-base-uncased",
    device=0
)
pipeline("Plants create [MASK] through a process known as photosynthesis.")
```

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

```python
import torch

from transformers import AutoModelForMaskedLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained(
    "google-bert/bert-base-uncased",
)
model = AutoModelForMaskedLM.from_pretrained(
    "google-bert/bert-base-uncased",
    device_map="auto",
    attn_implementation="sdpa"
)
inputs = tokenizer("Plants create [MASK] through a process known as photosynthesis.", return_tensors="pt").to(model.device)

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

masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
predicted_token = tokenizer.decode(predicted_token_id)

print(f"The predicted token is: {predicted_token}")
```

</hfoption>
</hfoptions>

## Notes

- Inputs should be padded on the right because BERT uses absolute position embeddings.

## BertConfig

[[autodoc]] BertConfig
    - all

## BertTokenizer

[[autodoc]] BertTokenizer
    - get_special_tokens_mask
    - save_vocabulary

## BertTokenizerLegacy

[[autodoc]] BertTokenizerLegacy

## BertTokenizerFast

[[autodoc]] BertTokenizerFast

## BertModel

[[autodoc]] BertModel
    - forward

## BertForPreTraining

[[autodoc]] BertForPreTraining
    - forward

## BertLMHeadModel

[[autodoc]] BertLMHeadModel
    - forward

## BertForMaskedLM

[[autodoc]] BertForMaskedLM
    - forward

## BertForNextSentencePrediction

[[autodoc]] BertForNextSentencePrediction
    - forward

## BertForSequenceClassification

[[autodoc]] BertForSequenceClassification
    - forward

## BertForMultipleChoice

[[autodoc]] BertForMultipleChoice
    - forward

## BertForTokenClassification

[[autodoc]] BertForTokenClassification
    - forward

## BertForQuestionAnswering

[[autodoc]] BertForQuestionAnswering
    - forward

## Bert specific outputs

[[autodoc]] models.bert.modeling_bert.BertForPreTrainingOutput