To use Sentence Transformers in a multilingual setting, you start by selecting a pre-trained multilingual model designed to handle multiple languages. These models, such as paraphrase-multilingual-mpnet-base-v2 or distiluse-base-multilingual-cased-v1, are trained on diverse multilingual datasets to create embeddings that align semantically similar sentences across languages. You load the model using the SentenceTransformer class from the sentence-transformers library. For example, model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-mpnet-base-v2') initializes the model. No additional configuration is needed beyond specifying the model name, as the library handles tokenization and architecture setup automatically. The key advantage here is that the model abstracts language-specific complexities, allowing you to process text in any supported language without manual adjustments.
Once the model is loaded, encoding sentences in different languages follows the same process as monolingual use. You pass a list of text strings (in any supported language) to model.encode(), which returns numerical embeddings. For example, embeddings = model.encode(["Hello!", "Bonjour!", "Hola!"]) generates embeddings for English, French, and Spanish sentences. These embeddings reside in a shared vector space, enabling direct comparison across languages. This means you can compute cosine similarity between an English sentence and its French translation to measure semantic equivalence. The model handles tokenization, subword splitting, and language-specific nuances internally, so you don’t need to detect languages or apply separate preprocessing steps. However, ensure input texts are clean (e.g., free of excessive special characters) to avoid tokenization errors.
Practical considerations include verifying the model’s supported languages and evaluating performance for your use case. While multilingual models are versatile, they may underperform for low-resource languages or specialized domains. For instance, a model trained on Wikipedia data might struggle with informal social media text in certain languages. Additionally, embedding quality can vary across languages, so test tasks like cross-lingual retrieval or clustering to ensure robustness. Batch processing is efficient, but long texts may require truncation (e.g., model.encode(text, truncate=True)). Finally, consider fine-tuning the model on domain-specific multilingual data if generic embeddings don’t suffice, though this requires labeled training data and computational resources.