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<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
        <img alt="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
        <img alt="Flax" src="https://img.shields.io/badge/Flax-29a79b.svg?style=flat&logo=data:image/png;base64, 
        ">
        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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# RoBERTa

[RoBERTa](https://huggingface.co/papers/1907.11692) improves BERT with new pretraining objectives, demonstrating [BERT](./bert) was undertrained and training design is important. The pretraining objectives include dynamic masking, sentence packing, larger batches and a byte-level BPE tokenizer.

You can find all the original RoBERTa checkpoints under the [Facebook AI](https://huggingface.co/FacebookAI) organization.


> [!TIP]
> Click on the RoBERTa models in the right sidebar for more examples of how to apply RoBERTa 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">

```py
import torch
from transformers import pipeline

pipeline = pipeline(
    task="fill-mask",
    model="FacebookAI/roberta-base",
    torch_dtype=torch.float16,
    device=0
)
pipeline("Plants create <mask> through a process known as photosynthesis.")
```

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

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

tokenizer = AutoTokenizer.from_pretrained(
    "FacebookAI/roberta-base",
)
model = AutoModelForMaskedLM.from_pretrained(
    "FacebookAI/roberta-base",
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="sdpa"
)
inputs = tokenizer("Plants create <mask> through a process known as photosynthesis.", return_tensors="pt").to("cuda")

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>
<hfoption id="transformers CLI">

```bash
echo -e "Plants create <mask> through a process known as photosynthesis." | transformers-cli run --task fill-mask --model FacebookAI/roberta-base --device 0
```

</hfoption>
</hfoptions>

## Notes

- RoBERTa doesn't have `token_type_ids` so you don't need to indicate which token belongs to which segment. Separate your segments with the separation token `tokenizer.sep_token` or `</s>`.

## RobertaConfig

[[autodoc]] RobertaConfig

## RobertaTokenizer

[[autodoc]] RobertaTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

## RobertaTokenizerFast

[[autodoc]] RobertaTokenizerFast
    - build_inputs_with_special_tokens

<frameworkcontent>
<pt>

## RobertaModel

[[autodoc]] RobertaModel
    - forward

## RobertaForCausalLM

[[autodoc]] RobertaForCausalLM
    - forward

## RobertaForMaskedLM

[[autodoc]] RobertaForMaskedLM
    - forward

## RobertaForSequenceClassification

[[autodoc]] RobertaForSequenceClassification
    - forward

## RobertaForMultipleChoice

[[autodoc]] RobertaForMultipleChoice
    - forward

## RobertaForTokenClassification

[[autodoc]] RobertaForTokenClassification
    - forward

## RobertaForQuestionAnswering

[[autodoc]] RobertaForQuestionAnswering
    - forward

</pt>
<tf>

## TFRobertaModel

[[autodoc]] TFRobertaModel
    - call

## TFRobertaForCausalLM

[[autodoc]] TFRobertaForCausalLM
    - call

## TFRobertaForMaskedLM

[[autodoc]] TFRobertaForMaskedLM
    - call

## TFRobertaForSequenceClassification

[[autodoc]] TFRobertaForSequenceClassification
    - call

## TFRobertaForMultipleChoice

[[autodoc]] TFRobertaForMultipleChoice
    - call

## TFRobertaForTokenClassification

[[autodoc]] TFRobertaForTokenClassification
    - call

## TFRobertaForQuestionAnswering

[[autodoc]] TFRobertaForQuestionAnswering
    - call

</tf>
<jax>

## FlaxRobertaModel

[[autodoc]] FlaxRobertaModel
    - __call__

## FlaxRobertaForCausalLM

[[autodoc]] FlaxRobertaForCausalLM
    - __call__

## FlaxRobertaForMaskedLM

[[autodoc]] FlaxRobertaForMaskedLM
    - __call__

## FlaxRobertaForSequenceClassification

[[autodoc]] FlaxRobertaForSequenceClassification
    - __call__

## FlaxRobertaForMultipleChoice

[[autodoc]] FlaxRobertaForMultipleChoice
    - __call__

## FlaxRobertaForTokenClassification

[[autodoc]] FlaxRobertaForTokenClassification
    - __call__

## FlaxRobertaForQuestionAnswering

[[autodoc]] FlaxRobertaForQuestionAnswering
    - __call__

</jax>
</frameworkcontent>
