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*This model was released on 2021-05-02 and added to Hugging Face Transformers on 2022-01-29.*

<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-XL

[XLM-RoBERTa-XL](https://huggingface.co/papers/2105.00572) is a 3.5B parameter multilingual masked language model pretrained on 100 languages. It shows that by scaling model capacity, multilingual models demonstrates strong performance on high-resource languages and can even zero-shot low-resource languages.

You can find all the original XLM-RoBERTa-XL checkpoints under the [AI at Meta](https://huggingface.co/facebook?search_models=xlm) organization.

> [!TIP]
> Click on the XLM-RoBERTa-XL models in the right sidebar for more examples of how to apply XLM-RoBERTa-XL 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="facebook/xlm-roberta-xl",  
    dtype=torch.float16,  
    device=0  
)  
pipeline("Bonjour, je suis un modèle <mask>.")  
```

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

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

tokenizer = AutoTokenizer.from_pretrained(  
    "facebook/xlm-roberta-xl",  
)  
model = AutoModelForMaskedLM.from_pretrained(  
    "facebook/xlm-roberta-xl",  
    dtype=torch.float16,  
    device_map="auto",  
    attn_implementation="sdpa"  
)  
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-cli run --task fill-mask --model facebook/xlm-roberta-xl --device 0
```

</hfoption>
</hfoptions>

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

The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.

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

quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
tokenizer = AutoTokenizer.from_pretrained(
    "facebook/xlm-roberta-xl",
)
model = AutoModelForMaskedLM.from_pretrained(
    "facebook/xlm-roberta-xl",
    dtype=torch.float16,
    device_map="auto",
    attn_implementation="sdpa",
    quantization_config=quantization_config
)
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}")
```

## Notes

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

## XLMRobertaXLConfig

[[autodoc]] XLMRobertaXLConfig

## XLMRobertaXLModel

[[autodoc]] XLMRobertaXLModel
    - forward

## XLMRobertaXLForCausalLM

[[autodoc]] XLMRobertaXLForCausalLM
    - forward

## XLMRobertaXLForMaskedLM

[[autodoc]] XLMRobertaXLForMaskedLM
    - forward

## XLMRobertaXLForSequenceClassification

[[autodoc]] XLMRobertaXLForSequenceClassification
    - forward

## XLMRobertaXLForMultipleChoice

[[autodoc]] XLMRobertaXLForMultipleChoice
    - forward

## XLMRobertaXLForTokenClassification

[[autodoc]] XLMRobertaXLForTokenClassification
    - forward

## XLMRobertaXLForQuestionAnswering

[[autodoc]] XLMRobertaXLForQuestionAnswering
    - forward
