Sentence Transformers Embedding Model Allows you to set parameters when registering a sentence-transformers object. Info Sentence transformer embeddings are normalized by default. It is recommended to use normalized embeddings for similarity search. Parameter Type Default Value Description name str all-MiniLM-L6-v2 The name of the model device str cpu The device to run the model on (can be cpu or gpu) normalize bool True Whether to normalize the input text before feeding it to the model trust_remote_code bool False Whether to trust and execute remote code from the model's Huggingface repository Check out available sentence-transformer models here! - sentence-transformers/all-MiniLM-L12-v2 - sentence-transformers/paraphrase-mpnet-base-v2 - sentence-transformers/gtr-t5-base - sentence-transformers/LaBSE - sentence-transformers/all-MiniLM-L6-v2 - sentence-transformers/bert-base-nli-max-tokens - sentence-transformers/bert-base-nli-mean-tokens - sentence-transformers/bert-base-nli-stsb-mean-tokens - sentence-transformers/bert-base-wikipedia-sections-mean-tokens - sentence-transformers/bert-large-nli-cls-token - sentence-transformers/bert-large-nli-max-tokens - sentence-transformers/bert-large-nli-mean-tokens - sentence-transformers/bert-large-nli-stsb-mean-tokens - sentence-transformers/distilbert-base-nli-max-tokens - sentence-transformers/distilbert-base-nli-mean-tokens - sentence-transformers/distilbert-base-nli-stsb-mean-tokens - sentence-transformers/distilroberta-base-msmarco-v1 - sentence-transformers/distilroberta-base-msmarco-v2 - sentence-transformers/nli-bert-base-cls-pooling - sentence-transformers/nli-bert-base-max-pooling - sentence-transformers/nli-bert-base - sentence-transformers/nli-bert-large-cls-pooling - sentence-transformers/nli-bert-large-max-pooling - sentence-transformers/nli-bert-large - sentence-transformers/nli-distilbert-base-max-pooling - sentence-transformers/nli-distilbert-base - sentence-transformers/nli-roberta-base - sentence-transformers/nli-roberta-large - sentence-transformers/roberta-base-nli-mean-tokens - sentence-transformers/roberta-base-nli-stsb-mean-tokens - sentence-transformers/roberta-large-nli-mean-tokens - sentence-transformers/roberta-large-nli-stsb-mean-tokens - sentence-transformers/stsb-bert-base - sentence-transformers/stsb-bert-large - sentence-transformers/stsb-distilbert-base - sentence-transformers/stsb-roberta-base - sentence-transformers/stsb-roberta-large - sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens - sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens - sentence-transformers/xlm-r-base-en-ko-nli-ststb - sentence-transformers/xlm-r-bert-base-nli-mean-tokens - sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens - sentence-transformers/xlm-r-large-en-ko-nli-ststb - sentence-transformers/bert-base-nli-cls-token - sentence-transformers/all-distilroberta-v1 - sentence-transformers/multi-qa-MiniLM-L6-dot-v1 - sentence-transformers/multi-qa-distilbert-cos-v1 - sentence-transformers/multi-qa-distilbert-dot-v1 - sentence-transformers/multi-qa-mpnet-base-cos-v1 - sentence-transformers/multi-qa-mpnet-base-dot-v1 - sentence-transformers/nli-distilroberta-base-v2 - sentence-transformers/all-MiniLM-L6-v1 - sentence-transformers/all-mpnet-base-v1 - sentence-transformers/all-mpnet-base-v2 - sentence-transformers/all-roberta-large-v1 - sentence-transformers/allenai-specter - sentence-transformers/average_word_embeddings_glove.6B.300d - sentence-transformers/average_word_embeddings_glove.840B.300d - sentence-transformers/average_word_embeddings_komninos - sentence-transformers/average_word_embeddings_levy_dependency - sentence-transformers/clip-ViT-B-32-multilingual-v1 - sentence-transformers/clip-ViT-B-32 - sentence-transformers/distilbert-base-nli-stsb-quora-ranking - sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking - sentence-transformers/distilroberta-base-paraphrase-v1 - sentence-transformers/distiluse-base-multilingual-cased-v1 - sentence-transformers/distiluse-base-multilingual-cased-v2 - sentence-transformers/distiluse-base-multilingual-cased - sentence-transformers/facebook-dpr-ctx_encoder-multiset-base - sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base - sentence-transformers/facebook-dpr-question_encoder-multiset-base - sentence-transformers/facebook-dpr-question_encoder-single-nq-base - sentence-transformers/gtr-t5-large - sentence-transformers/gtr-t5-xl - sentence-transformers/gtr-t5-xxl - sentence-transformers/msmarco-MiniLM-L-12-v3 - sentence-transformers/msmarco-MiniLM-L-6-v3 - sentence-transformers/msmarco-MiniLM-L12-cos-v5 - sentence-transformers/msmarco-MiniLM-L6-cos-v5 - sentence-transformers/msmarco-bert-base-dot-v5 - sentence-transformers/msmarco-bert-co-condensor - sentence-transformers/msmarco-distilbert-base-dot-prod-v3 - sentence-transformers/msmarco-distilbert-base-tas-b - sentence-transformers/msmarco-distilbert-base-v2 - sentence-transformers/msmarco-distilbert-base-v3 - sentence-transformers/msmarco-distilbert-base-v4 - sentence-transformers/msmarco-distilbert-cos-v5 - sentence-transformers/msmarco-distilbert-dot-v5 - sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned - sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch - sentence-transformers/msmarco-distilroberta-base-v2 - sentence-transformers/msmarco-roberta-base-ance-firstp - sentence-transformers/msmarco-roberta-base-v2 - sentence-transformers/msmarco-roberta-base-v3 - sentence-transformers/multi-qa-MiniLM-L6-cos-v1 - sentence-transformers/nli-mpnet-base-v2 - sentence-transformers/nli-roberta-base-v2 - sentence-transformers/nq-distilbert-base-v1 - sentence-transformers/paraphrase-MiniLM-L12-v2 - sentence-transformers/paraphrase-MiniLM-L3-v2 - sentence-transformers/paraphrase-MiniLM-L6-v2 - sentence-transformers/paraphrase-TinyBERT-L6-v2 - sentence-transformers/paraphrase-albert-base-v2 - sentence-transformers/paraphrase-albert-small-v2 - sentence-transformers/paraphrase-distilroberta-base-v1 - sentence-transformers/paraphrase-distilroberta-base-v2 - sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 - sentence-transformers/paraphrase-multilingual-mpnet-base-v2 - sentence-transformers/paraphrase-xlm-r-multilingual-v1 - sentence-transformers/quora-distilbert-base - sentence-transformers/quora-distilbert-multilingual - sentence-transformers/sentence-t5-base - sentence-transformers/sentence-t5-large - sentence-transformers/sentence-t5-xxl - sentence-transformers/sentence-t5-xl - sentence-transformers/stsb-distilroberta-base-v2 - sentence-transformers/stsb-mpnet-base-v2 - sentence-transformers/stsb-roberta-base-v2 - sentence-transformers/stsb-xlm-r-multilingual - sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1 - sentence-transformers/clip-ViT-L-14 - sentence-transformers/clip-ViT-B-16 - sentence-transformers/use-cmlm-multilingual - sentence-transformers/all-MiniLM-L12-v1 Info You can also load many other model architectures from the library. For example models from sources such as BAAI, nomic, salesforce research, etc. See this HF hub page for all supported models. BAAI Embeddings example Here is an example that uses BAAI embedding model from the HuggingFace Hub supported models import lancedb from lancedb.pydantic import LanceModel, Vector from lancedb.embeddings import get_registry db = lancedb.connect("/tmp/db") model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", device="cpu") class Words(LanceModel): text: str = model.SourceField() vector: Vector(model.ndims()) = model.VectorField() table = db.create_table("words", schema=Words) table.add( [ {"text": "hello world"}, {"text": "goodbye world"} ] ) query = "greetings" actual = table.search(query).limit(1).to_pydantic(Words)[0] print(actual.text) Visit sentence-transformers HuggingFace HUB page for more information on the available models.