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XLM-RoBERTa

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.

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XLM-RoBERTa

XLM-RoBERTa 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 pretraining objectives on the XLM model.

You can find all the original XLM-RoBERTa checkpoints under the Facebook AI community 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
from transformers import pipeline


pipeline = pipeline(
    task="fill-mask",
    model="FacebookAI/xlm-roberta-base",
    device=0
)
# Example in French
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-roberta-base"
)
model = AutoModelForMaskedLM.from_pretrained(
    "FacebookAI/xlm-roberta-base",
    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> </hfoptions>

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

The example below uses 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",
    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"/> <PipelineTag pipeline="token-classification"/> <PipelineTag pipeline="text-generation"/> <PipelineTag pipeline="fill-mask"/> <PipelineTag pipeline="question-answering"/>

Multiple choice

🚀 Deploy

<Tip>

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

XLMRobertaConfig

[[autodoc]] XLMRobertaConfig

XLMRobertaTokenizer

[[autodoc]] XLMRobertaTokenizer - get_special_tokens_mask - 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