Usage

Characteristics of Sentence Transformer (a.k.a bi-encoder) models:

  1. Calculates a fixed-size vector representation (embedding) given texts or images.

  2. Embedding calculation is often efficient, embedding similarity calculation is very fast.

  3. Applicable for a wide range of tasks, such as semantic textual similarity, semantic search, clustering, classification, paraphrase mining, and more.

  4. Often used as a first step in a two-step retrieval process, where a Cross-Encoder (a.k.a. reranker) model is used to re-rank the top-k results from the bi-encoder.

Once you have installed Sentence Transformers, you can easily use Sentence Transformer models:

from sentence_transformers import SentenceTransformer

# 1. Load a pretrained Sentence Transformer model
model = SentenceTransformer("all-MiniLM-L6-v2")

# The sentences to encode
sentences = [
    "The weather is lovely today.",
    "It's so sunny outside!",
    "He drove to the stadium.",
]

# 2. Calculate embeddings by calling model.encode()
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# 3. Calculate the embedding similarities
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6660, 0.1046],
#         [0.6660, 1.0000, 0.1411],
#         [0.1046, 0.1411, 1.0000]])

Tasks and Advanced Usage