facebookresearch/XLM

Health v2 · maintenance only

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2920 stars · 498 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PyTorch implementation of Cross-lingual Language Model Pretraining (XLM) from Facebook AI Research, covering MLM, CLM, and TLM objectives plus XLM-R. It includes pretrained models and fine-tuning code for GLUE, XNLI, and supervised/unsupervised machine translation.

Use cases

• pretrain a cross-lingual language model
• fine-tune BERT-style models on XNLI
• train unsupervised machine translation models
• reproduce the XLM research paper
• run masked language model pretraining on custom corpora
• evaluate cross-lingual text classification

When to choose

• you need the original XLM/XLM-R research code and pretrained weights
• you want to experiment with cross-lingual pretraining objectives like TLM
• you need multi-GPU/multi-node pretraining in PyTorch

When to avoid

• you want a maintained library for production NLP - use Hugging Face Transformers instead
• you need modern tooling or recent PyTorch support (code tested on PyTorch 0.4/1.0)
• you only need inference with pretrained multilingual models

Facets

library · maturity maintenance

machine-learning llm-training nlp machine-learning deep-learning python pytorch cross-lingual pretraining bert machine-translation research-code facebook-research natural-language-processing gpu linux

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markdown · JSON · MCP: product_card(name="facebookresearch/XLM")