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xlm-roberta-large

XLM-RoBERTa Large, the 560-million-parameter multilingual encoder from Facebook AI, trained on 2.5TB of CommonCrawl data across 100 languages. It offers stronger multilingual language understanding than the base variant across classification, NER, and cross-lingual tasks, at roughly 4x the compute cost. MIT licensed with multi-framework support.

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Use cases

  • High-accuracy multilingual NER and sequence labeling
  • Cross-lingual text classification requiring strong encoder quality
  • Multilingual natural language inference at research quality
  • Sentence embedding for 100-language corpora when accuracy matters more than speed
  • Foundation for multilingual fine-tuned classifiers in production

Pros

  • 560M parameters provide stronger multilingual representations than base
  • MIT license; multi-framework support (PyTorch, TF, JAX, ONNX, safetensors)
  • Widely published cross-lingual benchmark results (XNLI, WikiANN)
  • 100-language coverage from large-scale CommonCrawl training

Cons

  • 4x compute cost vs. XLM-RoBERTa-base for marginal multilingual gains on simpler tasks
  • High-resource languages still outperformed by dedicated monolingual models
  • 512-token context limit for long-document tasks
  • Not suitable for text generation
  • Encoder-only architecture limits use cases vs. modern multilingual LLMs

FAQ

What is xlm-roberta-large used for?

High-accuracy multilingual NER and sequence labeling. Cross-lingual text classification requiring strong encoder quality. Multilingual natural language inference at research quality. Sentence embedding for 100-language corpora when accuracy matters more than speed. Foundation for multilingual fine-tuned classifiers in production.

Is xlm-roberta-large free to use?

xlm-roberta-large is an open-source model published on HuggingFace. License terms vary by model — check the model card for the specific license.

How do I run xlm-roberta-large locally?

Most HuggingFace models can be loaded with transformers or the appropriate framework library. See the model card for framework-specific instructions and hardware requirements.

Tags

transformerspytorchtfjaxonnxsafetensorsxlm-robertafill-maskexbertmultilingualafamarasazbebgbnbrbs