transformers/docs/source/en/model_doc/xlm-roberta-xl.md

XLM-RoBERTa-XL

XLM-RoBERTa-XL 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 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.

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>.")  

import torch  
from transformers import AutoModelForMaskedLM, AutoTokenizer  

tokenizer = AutoTokenizer.from_pretrained(  
    "facebook/xlm-roberta-xl",  
)  
model = AutoModelForMaskedLM.from_pretrained(  
    "facebook/xlm-roberta-xl",  
    torch_dtype=torch.float16,  
    device_map="auto",  
    attn_implementation="sdpa"  
)  
inputs = tokenizer("Bonjour, je suis un modèle <mask>.", 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}")

echo -e "Plants create <mask> through a process known as photosynthesis." | transformers-cli run --task fill-mask --model facebook/xlm-roberta-xl --device 0

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

The example below uses torchao to only quantize the weights to int4.

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",
    torch_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("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}")

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