We provide various pre-trained Sentence Transformers models via our Sentence Transformers Hugging Face organization. Additionally, over 6,000 community Sentence Transformers models have been publicly released on the Hugging Face Hub. All models can be found here:

* **Original models**: `Sentence Transformers Hugging Face organization <https://huggingface.co/models?library=sentence-transformers&author=sentence-transformers>`_.
* **Community models**: `All Sentence Transformer models on Hugging Face <https://huggingface.co/models?library=sentence-transformers>`_.

Each of these models can be easily downloaded and used like so:

.. sidebar:: Original Models

    For the original models from the `Sentence Transformers Hugging Face organization <https://huggingface.co/models?library=sentence-transformers&author=sentence-transformers>`_, it is not necessary to include the model author or organization prefix. For example, this snippet loads `sentence-transformers/all-mpnet-base-v2 <https://huggingface.co/sentence-transformers/all-mpnet-base-v2>`_.

from sentence_transformers import SentenceTransformer

# Load https://huggingface.co/sentence-transformers/all-mpnet-base-v2
model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2")
embeddings = model.encode([
    "The weather is lovely today.",
    "It's so sunny outside!",
    "He drove to the stadium.",
])
similarities = model.similarity(embeddings, embeddings)

.. note::
    Consider using the `Massive Textual Embedding Benchmark leaderboard <https://huggingface.co/spaces/mteb/leaderboard>`_ as an inspiration of strong Sentence Transformer models. Be wary:

    - **Model sizes**: it is recommended to filter away the large models that might not be feasible without excessive hardware.
    - **Experimentation is key**: models that perform well on the leaderboard do not necessarily do well on your tasks, it is **crucial** to experiment with various promising models.

.. tip::

    Read `Sentence Transformer > Usage > Speeding up Inference <./usage/efficiency.html>`_ for tips on how to speed up inference of models by up to 2x-3x.

.. sidebar:: Documentation

   #. `sentence-transformers/multi-qa-mpnet-base-cos-v1 <https://huggingface.co/sentence-transformers/multi-qa-mpnet-base-cos-v1>`_
   #. :class:`SentenceTransformer <sentence_transformers.sentence_transformer.model.SentenceTransformer>`
   #. :meth:`SentenceTransformer.encode <sentence_transformers.sentence_transformer.model.SentenceTransformer.encode>`
   #. :meth:`SentenceTransformer.similarity <sentence_transformers.sentence_transformer.model.SentenceTransformer.similarity>`

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("sentence-transformers/multi-qa-mpnet-base-cos-v1")

query_embedding = model.encode("How big is London")
passage_embeddings = model.encode([
    "London is known for its financial district",
    "London has 9,787,426 inhabitants at the 2011 census",
    "The United Kingdom is the fourth largest exporter of goods in the world",
])

similarity = model.similarity(query_embedding, passage_embeddings)
# => tensor([[0.4659, 0.6142, 0.2697]])

.. tip::

   Multimodal models require additional dependencies. Install them with e.g. ``pip install -U "sentence-transformers[image]"`` for image support. See `Installation <../installation.html>`_ for all options.

You can check which modalities a model supports using the :attr:`~sentence_transformers.sentence_transformer.model.SentenceTransformer.modalities` property and :meth:`~sentence_transformers.sentence_transformer.model.SentenceTransformer.supports` method:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Qwen/Qwen3-VL-Embedding-2B", revision="refs/pr/23")
print(model.modalities)
# => ['text', 'image', 'video', 'message']
print(model.supports("image"))
# => True
print(model.supports("audio"))
# => False

Newer multimodal models use the unified :class:`~sentence_transformers.base.modules.Transformer` module, which automatically detects supported modalities from the underlying model and processor. These models typically support richer input formats, including interleaved image-text inputs via chat messages. Notable examples include:

VLM-based models support additional modalities and input formats compared to CLIP models. You can verify this with :attr:`~sentence_transformers.sentence_transformer.model.SentenceTransformer.modalities`:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Qwen/Qwen3-VL-Embedding-2B", revision="refs/pr/23")
print(model.modalities)
# => ['text', 'image', 'video', 'message']

The :class:`~sentence_transformers.base.modules.Transformer` module also supports audio and video modalities when the underlying model and processor provide them. Audio models accept file paths, numpy/torch arrays, dicts with ``"array"`` and ``"sampling_rate"`` keys, or ``torchcodec.AudioDecoder`` instances, while video models accept file paths, numpy/torch arrays, dicts with ``"array"`` and ``"video_metadata"`` keys, or ``torchcodec.VideoDecoder`` instances. As pretrained audio and video embedding models become available on the Hugging Face Hub, they can be loaded with :class:`~sentence_transformers.sentence_transformer.model.SentenceTransformer` just like any other model.

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("hkunlp/instructor-large")
embeddings = model.encode(
    [
        "Dynamical Scalar Degree of Freedom in Horava-Lifshitz Gravity",
        "Comparison of Atmospheric Neutrino Flux Calculations at Low Energies",
        "Fermion Bags in the Massive Gross-Neveu Model",
        "QCD corrections to Associated t-tbar-H production at the Tevatron",
    ],
    prompt="Represent the Medicine sentence for clustering: ",
)
print(embeddings.shape)
# => (4, 768)

from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

model = SentenceTransformer("hkunlp/instructor-large")
query = "where is the food stored in a yam plant"
query_instruction = (
    "Represent the Wikipedia question for retrieving supporting documents: "
)
corpus = [
    'Yams are perennial herbaceous vines native to Africa, Asia, and the Americas and cultivated for the consumption of their starchy tubers in many temperate and tropical regions. The tubers themselves, also called "yams", come in a variety of forms owing to numerous cultivars and related species.',
    "The disparate impact theory is especially controversial under the Fair Housing Act because the Act regulates many activities relating to housing, insurance, and mortgage loans—and some scholars have argued that the theory's use under the Fair Housing Act, combined with extensions of the Community Reinvestment Act, contributed to rise of sub-prime lending and the crash of the U.S. housing market and ensuing global economic recession",
    "Disparate impact in United States labor law refers to practices in employment, housing, and other areas that adversely affect one group of people of a protected characteristic more than another, even though rules applied by employers or landlords are formally neutral. Although the protected classes vary by statute, most federal civil rights laws protect based on race, color, religion, national origin, and sex as protected traits, and some laws include disability status and other traits as well.",
]
corpus_instruction = "Represent the Wikipedia document for retrieval: "

query_embedding = model.encode(query, prompt=query_instruction)
corpus_embeddings = model.encode(corpus, prompt=corpus_instruction)
similarities = cos_sim(query_embedding, corpus_embeddings)
print(similarities)
# => tensor([[0.8835, 0.7037, 0.6970]])