Selecting embedding models for non-English languages requires careful consideration of language support, training data, and task-specific performance. Start by evaluating whether the model was explicitly trained on data in your target language. Many popular embedding models, like BERT or RoBERTa variants, have multilingual versions (e.g., multilingual BERT, XLM-R) that cover dozens or hundreds of languages. However, their performance can vary significantly between languages due to differences in training data quantity and quality. For example, XLM-RoBERTa (XLM-R) is trained on CommonCrawl data across 100+ languages, but languages with smaller web footprints (e.g., Icelandic or Swahili) may receive less robust representations. Always check the model’s documentation for language coverage details and validation benchmarks.
Next, consider the model’s architecture and alignment with your use case. For languages with complex morphology or non-Latin scripts (e.g., Arabic, Thai, or Korean), models that use subword tokenization (like SentencePiece) or character-level embeddings often perform better. For instance, LASER (Language-Agnostic SEntence Representations) uses a shared encoder across languages and handles scripts like Cyrillic or Devanagari effectively. If your task involves semantic similarity or clustering, test whether the model captures nuances in your target language. For example, if you’re working with Japanese, compare models like Studio-Ousia’s Japanese BERT or the multilingual E5 embeddings by Microsoft. Use simple evaluation tasks, such as translating a set of English benchmark phrases into your target language and checking if the embeddings reflect similar relationships (e.g., “king – man + woman = queen” analogies).
Finally, prioritize models that offer community support or fine-tuning capabilities. Open-source frameworks like Hugging Face Transformers provide pretrained models for many languages, and platforms like Sentence-Transformers allow easy fine-tuning with custom data. For low-resource languages, consider supplementing pretrained embeddings with domain-specific data. For example, if you’re building a search system for Vietnamese news, fine-tune a multilingual model on Vietnamese articles to improve relevance. Also, verify computational efficiency: models like LaBSE (Language-Agnostic BERT Sentence Embedding) are powerful but large, while distilled versions (e.g., TinyBERT) trade some accuracy for faster inference. Always test multiple models on a subset of your data—tools like the MTEB (Massive Text Embedding Benchmark) or custom cosine similarity checks can help quantify performance differences before committing to a solution.