Sentence transformers

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.