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# XLM

[XLM](https://huggingface.co/papers/1901.07291) demonstrates cross-lingual pretraining with two approaches, unsupervised training on a single language and supervised training on more than one language with a cross-lingual language model objective. The XLM model supports the causal language modeling objective, masked language modeling, and translation language modeling (an extension of the [BERT](./bert)) masked language modeling objective to multiple language inputs).

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

> [!TIP]
> Click on the XLM models in the right sidebar for more examples of how to apply XLM 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",  
    torch_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(  
    "FacebookAI/xlm-mlm-en-2048",  
)  
model = AutoModelForMaskedLM.from_pretrained(  
    "FacebookAI/xlm-mlm-en-2048",  
    torch_dtype=torch.float16,  
    device_map="auto",  
)  
inputs = tokenizer("Hello, I'm a <mask> model.", return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model(**inputs)
    predictions = outputs.logits.argmax(dim=-1)

predicted_token = tokenizer.decode(predictions[0][inputs["input_ids"][0] == tokenizer.mask_token_id])
print(f"Predicted token: {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/xlm-mlm-en-2048 --device 0
```
</hfoption>
</hfoptions>

## XLMConfig

[[autodoc]] XLMConfig

## XLMTokenizer

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

## XLM specific outputs

[[autodoc]] models.xlm.modeling_xlm.XLMForQuestionAnsweringOutput

<frameworkcontent>
<pt>

## XLMModel

[[autodoc]] XLMModel
    - forward

## XLMWithLMHeadModel

[[autodoc]] XLMWithLMHeadModel
    - forward

## XLMForSequenceClassification

[[autodoc]] XLMForSequenceClassification
    - forward

## XLMForMultipleChoice

[[autodoc]] XLMForMultipleChoice
    - forward

## XLMForTokenClassification

[[autodoc]] XLMForTokenClassification
    - forward

## XLMForQuestionAnsweringSimple

[[autodoc]] XLMForQuestionAnsweringSimple
    - forward

## XLMForQuestionAnswering

[[autodoc]] XLMForQuestionAnswering
    - forward

</pt>
<tf>

## TFXLMModel

[[autodoc]] TFXLMModel
    - call

## TFXLMWithLMHeadModel

[[autodoc]] TFXLMWithLMHeadModel
    - call

## TFXLMForSequenceClassification

[[autodoc]] TFXLMForSequenceClassification
    - call

## TFXLMForMultipleChoice

[[autodoc]] TFXLMForMultipleChoice
    - call

## TFXLMForTokenClassification

[[autodoc]] TFXLMForTokenClassification
    - call

## TFXLMForQuestionAnsweringSimple

[[autodoc]] TFXLMForQuestionAnsweringSimple
    - call

</tf>
</frameworkcontent>


