外观
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本页内容仍为英文原文，源自官方文档 integrations/embedding/sentence-transformers。中文翻译正在分批进行中。
Sentence Transformers
Allows you to set parameters when registering a sentence-transformers object.
说明
Sentence transformer embeddings are normalized by default. It is recommended to use normalized embeddings for similarity search.
注意
Setting trust_remote_code=True allows the model to execute arbitrary code from its HuggingFace repository during loading. Only enable it for repositories you trust, and pin revision to a commit hash. When a table using such a model is opened in a new process, the model must be approved with get_registry().allow_remote_code(...). See Trusting remote code and custom endpoints.
| 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 |
revision | str | None | The model revision to load, such as a commit hash |
Check out available sentence-transformer models here!
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- 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说明
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.
说明
Here is an example that uses the BAAI embedding model from the HuggingFace Hub supported models.
python
import tempfile
from pathlib import Path
import lancedb
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector
db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "sentence-transformers"))
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 the sentence-transformers HuggingFace Hub page for more information on the available models.