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*This model was released on 2019-11-05 and added to Hugging Face Transformers on 2020-11-16 and contributed by [stefan-it](https://huggingface.co/stefan-it).*

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

[XLM-R](https://huggingface.co/papers/1911.02116) is a Transformer-based masked language model trained on over two terabytes of filtered CommonCrawl data across one hundred languages. It outperforms multilingual BERT (mBERT) on various cross-lingual benchmarks, showing improvements such as +13.8% average accuracy on XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R particularly excels in low-resource languages, enhancing XNLI accuracy by 11.8% for Swahili and 9.2% for Urdu compared to the previous XLM model. The model demonstrates the feasibility of multilingual modeling without compromising per-language performance, competing effectively with strong monolingual models on GLUE and XNLI benchmarks.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="fill-mask", model="FacebookAI/xlm-roberta-base", dtype="auto")
pipeline("Les plantes créent <mask> grâce à un processus appelé photosynthèse.")
```

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

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

model = AutoModelForMaskedLM.from_pretrained("FacebookAI/xlm-roberta-base", dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("FacebookAI/xlm-roberta-base")

inputs = tokenizer("Les plantes créent <mask> grâce à un processus appelé photosynthèse.", return_tensors="pt")
outputs = model(**inputs)
mask_token_id = tokenizer.mask_token_id
mask_position = (inputs.input_ids == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
predicted_word = tokenizer.decode(outputs.logits[0, mask_position].argmax(dim=-1))
print(f"Predicted word: {predicted_word}")
```

</hfoption>
</hfoptions>

## Usage tips

- XLM-RoBERTa doesn't require `lang` tensors to understand the input language. It automatically detects the language from `input_ids`.

## 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

