Usage

Characteristics of Cross Encoder (a.k.a reranker) models:

Calculates a similarity score given pairs of inputs (typically text pairs, but also image-text or other modalities).
Generally provides superior performance compared to a Sentence Transformer (a.k.a. bi-encoder) model.
Often slower than a Sentence Transformer model, as it requires computation for each pair rather than each text.
Due to the previous 2 characteristics, Cross Encoders are often used to re-rank the top-k results from a Sentence Transformer model.

Once you have installed Sentence Transformers, you can easily use Cross Encoder models:

from sentence_transformers import CrossEncoder

# 1. Load a pre-trained CrossEncoder model
model = CrossEncoder("cross-encoder/ms-marco-MiniLM-L6-v2")

# 2. Predict scores for a pair of sentences
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([ 8.607138 , -4.3200774], dtype=float32)

# 3. Rank a list of passages for a query
query = "How many people live in Berlin?"
passages = [
    "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.",
    "Berlin is well known for its museums.",
    "In 2014, the city state Berlin had 37,368 live births (+6.6%), a record number since 1991.",
    "The urban area of Berlin comprised about 4.1 million people in 2014, making it the seventh most populous urban area in the European Union.",
    "The city of Paris had a population of 2,165,423 people within its administrative city limits as of January 1, 2019",
    "An estimated 300,000-420,000 Muslims reside in Berlin, making up about 8-11 percent of the population.",
    "Berlin is subdivided into 12 boroughs or districts (Bezirke).",
    "In 2015, the total labour force in Berlin was 1.85 million.",
    "In 2013 around 600,000 Berliners were registered in one of the more than 2,300 sport and fitness clubs.",
    "Berlin has a yearly total of about 135 million day visitors, which puts it in third place among the most-visited city destinations in the European Union.",
]
ranks = model.rank(query, passages)

# Print the scores
print("Query:", query)
for rank in ranks:
    print(f"{rank['score']:.2f}\t{passages[rank['corpus_id']]}")
"""
Query: How many people live in Berlin?
8.92    The urban area of Berlin comprised about 4.1 million people in 2014, making it the seventh most populous urban area in the European Union.
8.61    Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.
8.24    An estimated 300,000-420,000 Muslims reside in Berlin, making up about 8-11 percent of the population.
7.60    In 2014, the city state Berlin had 37,368 live births (+6.6%), a record number since 1991.
6.35    In 2013 around 600,000 Berliners were registered in one of the more than 2,300 sport and fitness clubs.
5.42    Berlin has a yearly total of about 135 million day visitors, which puts it in third place among the most-visited city destinations in the European Union.
3.45    In 2015, the total labour force in Berlin was 1.85 million.
0.33    Berlin is subdivided into 12 boroughs or districts (Bezirke).
-4.24   The city of Paris had a population of 2,165,423 people within its administrative city limits as of January 1, 2019
-4.32   Berlin is well known for its museums.
"""

Some CrossEncoder models also support multimodal inputs, allowing you to score pairs that include images, not just text. You can check which modalities a model supports using the modalities property and the supports() method. Each element in a pair can be any of the following:

Tip

Multimodal models require additional dependencies. Install them with e.g. pip install -U "sentence-transformers[image]" for image support. See Installation for all options.

Text: strings.
Image: PIL images, file paths, URLs, or numpy/torch arrays.
Audio: file paths, numpy/torch arrays, dicts with "array" and "sampling_rate" keys, or (if torchcodec installed) torchcodec.AudioDecoder instances.
Video: file paths, numpy/torch arrays, dicts with "array" and "video_metadata" keys, or (if torchcodec installed) torchcodec.VideoDecoder instances.
Multimodal dicts: a dict mapping modality names to values, e.g. {"text": ..., "image": ...}. The keys must be "text", "image", "audio", or "video".

The two elements in a pair can have different modalities (e.g. a text query with an image document), depending on the underlying model architecture:

from sentence_transformers import CrossEncoder

model = CrossEncoder("Qwen/Qwen3-VL-Reranker-2B", revision="refs/pr/11")

query = "A green car parked in front of a yellow building"
documents = [
    # Image documents (URL or local file path)
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg",
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
    # Text document
    "A vintage Volkswagen Beetle painted in bright green sits in a driveway.",
    # Combined text + image document
    {
        "text": "A car in a European city",
        "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg",
    },
]

rankings = model.rank(query, documents)
for rank in rankings:
    print(f"{rank['score']:.4f}\t(document {rank['corpus_id']})")
"""
0.9375  (document 0)
0.5000  (document 3)
-1.2500 (document 2)
-2.4375 (document 1)
"""

In this example, the multimodal CrossEncoder uses the same modular architecture as a text-only CausalLM CrossEncoder (Transformer + LogitScore), but the Transformer module is backed by a vision-language model that can process both images and text through its chat template.

Tasks and Advanced Usage