We have released various pre-trained Cross Encoder models via our Cross Encoder Hugging Face organization. Additionally, numerous community Cross Encoder models have been publicly released on the Hugging Face Hub.

* **Original models**: `Cross Encoder Hugging Face organization <https://huggingface.co/models?library=sentence-transformers&author=cross-encoder>`_.
* **Community models**: `All Cross Encoder models on Hugging Face <https://huggingface.co/models?library=sentence-transformers&pipeline_tag=text-ranking>`_.

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

from sentence_transformers import CrossEncoder
import torch

# Load https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2
model = CrossEncoder("cross-encoder/ms-marco-MiniLM-L6-v2", activation_fn=torch.nn.Sigmoid())
scores = model.predict([
    ("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
    ("How many people live in Berlin?", "Berlin is well known for its museums."),
])
# => array([0.9998173 , 0.01312432], dtype=float32)

.. note::
    You can initialize these models with ``activation_fn=torch.nn.Sigmoid()`` to force the model to return scores between 0 and 1. Otherwise, the raw value can reasonably range between -10 and 10.

from sentence_transformers import CrossEncoder

model = CrossEncoder("cross-encoder/stsb-roberta-base")
scores = model.predict([("It's a wonderful day outside.", "It's so sunny today!"), ("It's a wonderful day outside.", "He drove to work earlier.")])
# => array([0.60443085, 0.00240758], dtype=float32)

.. note::
    The model don't work for question similarity. The question "How to learn Java?" and "How to learn Python?" will get a low score, as these questions are not duplicates. For question similarity, a :class:`~sentence_transformers.sentence_transformer.model.SentenceTransformer` trained on the Quora dataset will yield much more meaningful results.

from sentence_transformers import CrossEncoder

model = CrossEncoder("cross-encoder/nli-deberta-v3-base")
scores = model.predict([
    ("A man is eating pizza", "A man eats something"),
    ("A black race car starts up in front of a crowd of people.", "A man is driving down a lonely road."),
])

# Convert scores to labels
label_mapping = ["contradiction", "entailment", "neutral"]
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
# => ['entailment', 'contradiction']

Multimodal rerankers can score pairs involving different modalities such as images, video, audio, and text. These models use the same :class:`~sentence_transformers.base.modules.Transformer` + :class:`~sentence_transformers.cross_encoder.modules.LogitScore` architecture as text-only decoder rerankers, but with a multimodal backbone that can process non-text inputs. You can check whether a model supports a given modality using :attr:`~sentence_transformers.cross_encoder.model.CrossEncoder.modalities` and :meth:`~sentence_transformers.cross_encoder.model.CrossEncoder.supports`.