# What is the simplest way to encode a list of sentences into embeddings using a pre-trained Sentence Transformer model?

Source: https://zilliz.com/ai-faq/what-is-the-simplest-way-to-encode-a-list-of-sentences-into-embeddings-using-a-pretrained-sentence-transformer-model
Author: zilliz

The simplest way to encode sentences into embeddings with a pre-trained Sentence Transformer model involves three steps: installing the library, loading the model, and calling its `encode()` method. First, ensure the `sentence-transformers` package is installed. Next, import and initialize a model (e.g., `all-MiniLM-L6-v2`, a lightweight default). Finally, pass a list of sentences to the model's `encode()` method, which returns a NumPy array or PyTorch tensor containing the embeddings. This approach abstracts away tokenization, padding, and batching, making it straightforward for common use cases.

Here’s a concrete example using Python:
```python
from sentence_transformers import SentenceTransformer

# Load the pre-trained model
model = SentenceTransformer('all-MiniLM-L6-v2')

# List of input sentences
sentences = ["This is a sample sentence.", "Another example text."]

# Generate embeddings
embeddings = model.encode(sentences)
```
The `encode()` method handles batch processing automatically and returns a 2D array where each row corresponds to a sentence’s embedding. By default, embeddings are normalized to unit length, which is useful for cosine similarity calculations. If you need non-normalized vectors, set `normalize_embeddings=False` in the `encode()` call.

Key considerations include device selection (CPU/GPU) and output format. For GPU acceleration, add `device="cuda"` when loading the model. The output defaults to NumPy arrays, but you can return PyTorch tensors with `convert_to_tensor=True`. For large datasets, use `show_progress_bar=True` to track encoding progress. This method works for most cases, but for specialized needs (e.g., custom pooling or multilingual text), you may need to configure the model or preprocess text differently.