The simplest way to encode a list of sentences into embeddings using a pre-trained Sentence Transformer model involves three key steps: installing the library, loading a model, and applying its encode method. Sentence Transformers, a Python library built on PyTorch and Hugging Face Transformers, provides easy-to-use tools for generating dense vector representations (embeddings) of text. Pre-trained models like all-MiniLM-L6-v2 or paraphrase-multilingual-Mpnet-base-v2 are optimized for tasks like semantic search or clustering and can be loaded with minimal setup. The encode method handles tokenization, model inference, and output formatting automatically, converting sentences into fixed-length embeddings.
To get started, install the library using pip install sentence-transformers. Next, import the SentenceTransformer class and load a pre-trained model. For example, model = SentenceTransformer('all-MiniLM-L6-v2') initializes a lightweight model suitable for general-purpose use. If you’re working with GPU-enabled environments, adding device='cuda' speeds up processing. The model’s encode method accepts a list of strings and returns a NumPy array (or PyTorch tensor, if specified) where each row corresponds to a sentence’s embedding. For instance, embeddings = model.encode(['Hello world', 'How are you?']) generates two 384-dimensional vectors (with all-MiniLM-L6-v2). This approach requires no manual configuration of tokenizers or post-processing, making it accessible even for developers new to NLP.
Practical considerations include batch processing for large datasets and normalizing embeddings for similarity comparisons. By default, encode processes all sentences at once, but you can specify batch_size to manage memory usage. For example, embeddings = model.encode(sentences, batch_size=32) processes 32 sentences per batch. If you plan to compute cosine similarity between embeddings, set normalize_embeddings=True to ensure vectors are unit length. The output format can be controlled with convert_to_tensor=True (for PyTorch) or left as a NumPy array. This method balances simplicity with flexibility, allowing developers to integrate semantic embeddings into applications like search engines or recommendation systems with minimal code.