enginex-mlu370-any2any/transformers/docs/source/en/model_doc/xlm-roberta.md

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*This model was released on 2019-11-05 and added to Hugging Face Transformers on 2020-11-16.*

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

# XLM-RoBERTa

[XLM-RoBERTa](https://huggingface.co/papers/1911.02116) is a large multilingual masked language model trained on 2.5TB of filtered CommonCrawl data across 100 languages. It shows that scaling the model provides strong performance gains on high-resource and low-resource languages. The model uses the [RoBERTa](./roberta) pretraining objectives on the [XLM](./xlm) model.

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

> [!TIP]
> Click on the XLM-RoBERTa models in the right sidebar for more examples of how to apply XLM-RoBERTa to different cross-lingual tasks like classification, translation, and question answering.

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
import torch
from transformers import pipeline

pipeline = pipeline(
    task="fill-mask",
    model="FacebookAI/xlm-roberta-base",
    dtype=torch.float16,
    device=0
)
# Example in French
pipeline("Bonjour, je suis un modèle <mask>.")
```

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

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

tokenizer = AutoTokenizer.from_pretrained(
    "FacebookAI/xlm-roberta-base"
)
model = AutoModelForMaskedLM.from_pretrained(
    "FacebookAI/xlm-roberta-base",
    dtype=torch.float16,
    device_map="auto",
    attn_implementation="sdpa"
)

# Prepare input
inputs = tokenizer("Bonjour, je suis un modèle <mask>.", 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>
<hfoption id="transformers CLI">

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

</hfoption>
</hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [quantization guide](../quantization) overview for more available quantization backends.

The example below uses [bitsandbytes](../quantization/bitsandbytes) the quantive the weights to 4 bits

```python
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16
    bnb_4bit_quant_type="nf4",  # or "fp4" for float 4-bit quantization
    bnb_4bit_use_double_quant=True,  # use double quantization for better performance
)
tokenizer = AutoTokenizer.from_pretrained("facebook/xlm-roberta-large")
model = AutoModelForMaskedLM.from_pretrained(
    "facebook/xlm-roberta-large",
    dtype=torch.float16,
    device_map="auto",
    attn_implementation="flash_attention_2",
    quantization_config=quantization_config
)

inputs = tokenizer("Bonjour, je suis un modèle <mask>.", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Notes

- Unlike some XLM models, XLM-RoBERTa doesn't require `lang` tensors to understand what language is being used. It automatically determines the language from the input IDs

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with XLM-RoBERTa. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

<PipelineTag pipeline="text-classification"/>

- A blog post on how to [finetune XLM RoBERTa for multiclass classification with Habana Gaudi on AWS](https://www.philschmid.de/habana-distributed-training)
- [`XLMRobertaForSequenceClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb)..
- [Text classification](https://huggingface.co/docs/transformers/tasks/sequence_classification) chapter of the 🤗 Hugging Face Task Guides.
- [Text classification task guide](../tasks/sequence_classification)

<PipelineTag pipeline="token-classification"/>

- [`XLMRobertaForTokenClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/token-classification) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb).
- [Token classification](https://huggingface.co/course/chapter7/2?fw=pt) chapter of the 🤗 Hugging Face Course.
- [Token classification task guide](../tasks/token_classification)

<PipelineTag pipeline="text-generation"/>

- [`XLMRobertaForCausalLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb).
- [Causal language modeling](https://huggingface.co/docs/transformers/tasks/language_modeling) chapter of the 🤗 Hugging Face Task Guides.
- [Causal language modeling task guide](../tasks/language_modeling)

<PipelineTag pipeline="fill-mask"/>

- [`XLMRobertaForMaskedLM`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#robertabertdistilbert-and-masked-language-modeling) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb).
- [Masked language modeling](https://huggingface.co/course/chapter7/3?fw=pt) chapter of the 🤗 Hugging Face Course.
- [Masked language modeling](../tasks/masked_language_modeling)

<PipelineTag pipeline="question-answering"/>

- [`XLMRobertaForQuestionAnswering`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb).
- [Question answering](https://huggingface.co/course/chapter7/7?fw=pt) chapter of the 🤗 Hugging Face Course.
- [Question answering task guide](../tasks/question_answering)

**Multiple choice**

- [`XLMRobertaForMultipleChoice`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/multiple-choice) and [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb).
- [Multiple choice task guide](../tasks/multiple_choice)

🚀 Deploy

- A blog post on how to [Deploy Serverless XLM RoBERTa on AWS Lambda](https://www.philschmid.de/multilingual-serverless-xlm-roberta-with-huggingface).

<Tip>

This implementation is the same as RoBERTa. Refer to the [documentation of RoBERTa](roberta) for usage examples as well as the information relative to the inputs and outputs.
</Tip>

## XLMRobertaConfig

[[autodoc]] XLMRobertaConfig

## XLMRobertaTokenizer

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

## XLMRobertaTokenizerFast

[[autodoc]] XLMRobertaTokenizerFast

## XLMRobertaModel

[[autodoc]] XLMRobertaModel
    - forward

## XLMRobertaForCausalLM

[[autodoc]] XLMRobertaForCausalLM
    - forward

## XLMRobertaForMaskedLM

[[autodoc]] XLMRobertaForMaskedLM
    - forward

## XLMRobertaForSequenceClassification

[[autodoc]] XLMRobertaForSequenceClassification
    - forward

## XLMRobertaForMultipleChoice

[[autodoc]] XLMRobertaForMultipleChoice
    - forward

## XLMRobertaForTokenClassification

[[autodoc]] XLMRobertaForTokenClassification
    - forward

## XLMRobertaForQuestionAnswering

[[autodoc]] XLMRobertaForQuestionAnswering
    - forward