Multimodal Embedding & Reranker Models with Sentence Transformers

Back to Articles Multimodal Embedding & Reranker Models with Sentence Transformers Published April 9, 2026 Update on GitHub Tom Aarsen tomaarsen Follow Sentence Transformers is a Python library for using and training embedding and reranker models for applications like retrieval augmented generation, semantic search, and more. With the v5.4 update, you can now encode and compare texts, images, audio, and videos using the same familiar API. In this blogpost, I'll show you how to use these new multimodal capabilities for both embedding and reranking. Multimodal embedding models map inputs from different modalities into a shared embedding space, while multimodal reranker models score the relevance of mixed-modality pairs. This opens up use cases like visual document retrieval, cross-modal search, and multimodal RAG pipelines. Table of Contents What are Multimodal Models? Installation Multimodal Embedding Models Loading a Model Encoding Images Cross-Modal Similarity Encoding Queries and Documents Multimodal Reranker Models Ranking Mixed-Modality Documents Predicting Pair Scores Retrieve and Rerank Input Formats and Configuration Supported Input Types Checking Modality Support Processor and Model kwargs Supported Models Additional Resources What are Multimodal Models? Traditional embedding models convert text into fixed-size vectors. Multimodal embedding models extend this by mapping inputs from different modalities (text, images, audio, or video) into a shared embedding space. This means you can compare a text query against image documents (or vice versa) using the same similarity functions you're already familiar with. Similarly, traditional reranker (Cross Encoder) models compute relevance scores between pairs of texts. Multimodal rerankers can score pairs where one or both elements are images, combined text-image documents, or other modalities. For example, you can compare a text query against image documents, find video clips matching a description, or build RAG pipelines that work across modalities. Installation Multimodal models require some extra dependencies. Install the extras for the modalities you need (see Installation for more details): # For image support pip install -U "sentence-transformers[image]" # For audio support pip install -U "sentence-transformers[audio]" # For video support pip install -U "sentence-transformers[video]" # Mix and match as needed pip install -U "sentence-transformers[image,video,train]" VLM-based models like Qwen3-VL-2B require a GPU with at least ~8 GB of VRAM. For the 8B variants, expect ~20 GB. If you don't have a local GPU, consider using a cloud GPU service or Google Colab. On CPU, these models will be extremely slow; text-only or CLIP models are better suited for CPU inference. Multimodal Embedding Models Loading a Model Loading a multimodal embedding model works exactly like loading a text-only model: from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qwen/Qwen3-VL-Embedding-2B", revision="refs/pr/23") The revision argument is required for now because the integration pull requests for these models are still pending. Once they're merged, you'll be able to load them without specifying a revision. The model automatically detects which modalities it supports, so there's nothing extra to configure. See Processor and Model kwargs if you want to control things like image resolution or model precision. Encoding Images With a multimodal model loaded, model.encode() accepts images alongside text. Images can be provided as URLs, local file paths, or PIL Image objects (see Supported Input Types for all accepted formats): from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qwen/Qwen3-VL-Embedding-2B", revision="refs/pr/23") # Encode images from URLs img_embeddings = model.encode([ "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg", "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg", ]) print(img_embeddings.shape) # (2, 2048) Cross-Modal Similarity You can compute similarities between text embeddings and image embeddings, since the model maps both into the same space: from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qwen/Qwen3-VL-Embedding-2B", revision="refs/pr/23") # Encode images img_embeddings = model.encode([ "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg", "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg", ]) # Encode text queries (one matching + one hard negative per image) text_embeddings = model.encode([ "A green car parked in front of a yellow building", "A red car driving on a highway", "A bee on a pink flower", "A wasp on a wooden table", ]) # Compute cross-modal similarities similarities = model.similarity(text_embeddings, img_embeddings) print(similarities) # tensor([[0.5115, 0.1078], # [0.1999, 0.1108], # [0.1255, 0.6749], # [0.1283, 0.2704]]) As expected, "A green car parked in front of a yellow building" is most similar to the car image (0.51), and "A bee on a pink flower" is most similar to the bee image (0.67). The hard negatives ("A red car driving on a highway", "A wasp on a wooden table") correctly receive lower scores. You might notice that even the best matching scores (0.51, 0.67) aren't very close to 1.0. This is due to the modality gap: embeddings from different modalities tend to cluster in separate regions of the space. Cross-modal similarities are typically lower than within-modal ones (e.g., text-to-text), but the relative ordering is preserved, so retrieval still works well. Encoding Queries and Documents For retrieval tasks, encode_query() and encode_document() are the recommended methods. Many retrieval models prepend different instruction prompts depending on whether the input is a query or a document, similar to how chat models might apply different system prompts depending on the goal. Model authors can specify their prompts in the model config, and encode_query() / encode_document() automatically load and apply the correct one: encode_query() uses the model's "query" prompt (if available) and sets task="query". encode_document() uses the first available prompt from "document", "passage", or "corpus", and sets task="document". Under the hood, both are thin wrappers around encode(), they just handle prompt selection for you. Here's what cross-modal retrieval looks like: from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qwen/Qwen3-VL-Embedding-2B", revision="refs/pr/23") # Encode text queries with the query prompt query_embeddings = model.encode_query([ "Find me a photo of a vehicle parked near a building", "Show me an image of a pollinating insect", ]) # Encode document screenshots with the document prompt doc_embeddings = model.encode_document([ "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg", "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg", ]) # Compute similarities similarities = model.similarity(query_embeddings, doc_embeddings) print(similarities) # tensor([[0.3907, 0.1490], # [0.1235, 0.4872]]) These methods accept the same input types as encode() (images, URLs, multimodal dicts, etc.) and pass through the same parameters. For models without specialized query/document prompts, they behave identically to encode(). Multimodal Reranker Models Multimodal reranker (CrossEncoder) models score the relevance between pairs of inputs, where each element can be text, an image, audio, video, or a combination. They tend to outperform embedding models in terms of quality, but are slower since they process each pair individually. The currently available pretrained multimodal rerankers focus on text and image inputs, but the architecture supports any modality that the underlying model can handle. Ranking Mixed-Modality Documents The rank() method scores and ranks a list of documents against a query, supporting mixed modalities: 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) """ The reranker correctly identifies the car image (document 0) as the most relevant result, followed by the combined text+image document about a car in a European city (document 3). The bee image (document 1) scores lowest. Keep in mind that the modality gap can influence absolute scores: text-image pair scores may occupy a different range than text-text or image-image pair scores. You can also check which modalities a reranker supports using modalities and supports(), just like with embedding models: print(model.modalities) # ['text', 'image', 'video', 'message'] print(model.supports("image")) # True # Check if the model supports a specific pair of modalities print(model.supports(("image", "text"))) # True Predicting Pair Scores You can also use predict() to get raw relevance scores for specific pairs of inputs: from sentence_transformers import CrossEncoder model = CrossEncoder("jinaai/jina-reranker-m0", trust_remote_code=True) scores = model.predict([ ("A green car", "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"), ("A bee on a flower", "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"), ("A green car", "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"), ]) print(scores) # [0.9389156 0.96922314 0.46063158] Retrieve and Rerank A common pattern is to use an embedding model for fast initial retrieval, then refine the top results with a reranker: from sentence_transformers import SentenceTransformer, CrossEncoder # Step 1: Retrieve with an embedding model embedder = SentenceTransformer("Qwen/Qwen3-VL-Embedding-2B", revision="refs/pr/23") query = "revenue growth chart" query_embedding = embedder.encode_query(query) # Pre-compute corpus embeddings (do this once, then store them) document_screenshots = [ "path/to/doc1.png", "path/to/doc2.png", # ... potentially millions of document screenshots ] corpus_embeddings = embedder.encode_document(document_screenshots, show_progress_bar=True) # Simple cosine similarity retrieval, viable as long as embeddings fit in memory similarities = embedder.similarity(query_embedding, corpus_embeddings) top_k_indices = similarities.argsort(descending=True)[0][:10] # Step 2: Rerank the top-k results with a reranker model reranker = CrossEncoder("nvidia/llama-nemotron-rerank-vl-1b-v2", trust_remote_code=True) top_k_documents = [document_screenshots[i] for i in top_k_indices] rankings = reranker.rank(query, top_k_documents) for rank in rankings: print(f"{rank['score']:.4f}\t{top_k_documents[rank['corpus_id']]}") Since the corpus embeddings are pre-computed, the initial retrieval is fast even over millions of documents. The reranker then provides more accurate scoring over the smaller candidate set. Input Formats and Configuration Supported Input Types Multimodal models accept a variety of input formats. Here's a summary of what you can pass to model.encode(): Modality Accepted Formats Text - Strings Image - PIL.Image.Image objects - File paths (e.g. "./photo.jpg") - URLs (e.g. "https://.../image.jpg") - Numpy arrays, torch tensors Audio - File paths (e.g. "./audio.wav") - URLs (e.g. "https://.../audio.wav") - Numpy/torch arrays - Dicts with "array" and "sampling_rate" keys - torchcodec.AudioDecoder instances Video - File paths (e.g. "./video.mp4") - URLs (e.g. "https://.../video.mp4") - Numpy/torch arrays - Dicts with "array" and "video_metadata" keys - torchcodec.VideoDecoder instances Multimodal - Dicts mapping modality names to values, e.g. {"text": "a caption", "image": "https://.../image.jpg"} Valid keys: "text", "image", "audio", "video" Message - List of message dicts with "role" and "content" keys, e.g. [{"role": "user", "content": [...]}] Checking Modality Support You can check which modalities a model supports using the modalities property and supports() method: from sentence_transformers import SentenceTransformer model = SentenceTransformer("Qwen/Qwen3-VL-Embedding-2B", revision="refs/pr/23") # List all supported modalities print(model.modalities) # ['text', 'image', 'video', 'message'] # Check for a specific modality print(model.supports("image")) # True print(model.supports("audio")) # False The "message" modality indicates that the model accepts chat-style message inputs with interleaved content. In practice, you rarely need to use this directly. When you pass strings, URLs, or multimodal dicts, the model converts them to the appropriate message format internally. Sentence Transformers supports two message formats: Structured (most VLMs, e.g. Qwen3-VL): Content is a list of typed dicts, e.g. [{"type": "text", "text": "..."}, {"type": "image", "image": ...}] Flat (e.g. Deepseek-V3): Content is a direct value, e.g. "some text" The format is auto-detected from the model's chat template. Since all inputs get converted into the same message format internally, you can mix input types in a single encode() call: embeddings = model.encode([ # A text input "A green car parked in front of a yellow building", # An image input (URL) "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg", # A combined text + image input { "text": "A car in a European city", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg", }, ]) Click here if you need to pass raw message inputs Processor and Model kwargs You may want to control image resolution bounds or model precision. Use processor_kwargs and model_kwargs when loading the model: model = SentenceTransformer( "Qwen/Qwen3-VL-Embedding-2B", model_kwargs={"attn_implementation": "flash_attention_2", "torch_dtype": "bfloat16"}, processor_kwargs={"min_pixels": 28 * 28, "max_pixels": 600 * 600}, revision="refs/pr/23", ) processor_kwargs controls how inputs are preprocessed (e.g., image resolution bounds). Higher max_pixels means higher quality but more memory and compute. These are passed directly to AutoProcessor.from_pretrained(...). model_kwargs controls how the underlying model is loaded (e.g., precision, attention implementation). These are passed directly to the appropriate AutoModel.from_pretrained(...) call (e.g., AutoModel, AutoModelForCausalLM, AutoModelForSequenceClassification, etc., depending on the configuration of the model modules). See the SentenceTransformer API Reference documentation for more details on these kwargs. In Sentence Transformers v5.4, tokenizer_kwargs has been renamed to processor_kwargs to reflect that multimodal models use processors rather than just tokenizers. The old name is still accepted but deprecated. Supported Models Here are the multimodal models supported in v5.4, also available in the v5.4 integrations collection: Supported Multimodal Embedding Models Model Parameters Modalities Revision Qwen/Qwen3-VL-Embedding-2B 2B Text, Image, Video revision="refs/pr/23" Qwen/Qwen3-VL-Embedding-8B 8B Text, Image, Video revision="refs/pr/11" nvidia/llama-nemotron-embed-vl-1b-v2 1.7B Text, Image No revision needed nvidia/omni-embed-nemotron-3b 4.7B Text, Image No revision needed Supported Multimodal Reranker Models Model Parameters Modalities Revision Qwen/Qwen3-VL-Reranker-2B 2B Text, Image, Video revision="refs/pr/11" Qwen/Qwen3-VL-Reranker-8B 8B Text, Image, Video revision="refs/pr/9" nvidia/llama-nemotron-rerank-vl-1b-v2 2B Text, Image No revision needed jinaai/jina-reranker-m0 2B Text, Image No revision needed Text-Only Reranker Models (also new in v5.4) Model Parameters Revision Qwen/Qwen3-Reranker-0.6B 0.6B revision="refs/pr/24" Qwen/Qwen3-Reranker-4B 4B revision="refs/pr/11" Qwen/Qwen3-Reranker-8B 8B revision="refs/pr/11" mixedbread-ai/mxbai-rerank-base-v2 0.5B No revision needed mixedbread-ai/mxbai-rerank-large-v2 2B No revision needed Click here for a text-only reranker usage example CLIP Models The older CLIP models continue to be supported: Model ImageNet Zero-Shot Top-1 Accuracy Notes sentence-transformers/clip-ViT-L-14 75.4 sentence-transformers/clip-ViT-B-16 68.1 sentence-transformers/clip-ViT-B-32 63.3 sentence-transformers/clip-ViT-B-32-multilingual-v1 N/A Multilingual text encoder, 50+ languages These simple CLIP models still work well on lower-resource hardware. Click here for a CLIP usage example Additional Resources Documentation Sentence Transformer > Usage Sentence Transformer > Pretrained Models Cross Encoder > Usage Cross Encoder > Pretrained Models Installation Training I'll be releasing a blogpost for training and finetuning multimodal models in the coming weeks, so stay tuned! In the meantime, you can experiment with inference on the pretrained models, or try to experiment with training using the training documentation: Sentence Transformer > Training Overview Sentence Transformer > Training Examples Cross Encoder > Training Overview Cross Encoder > Training Examples Sparse Encoder > Training Overview Sparse Encoder > Training Examples Hugging Face Hub Sentence Transformers models on the Hub Sentence Transformers datasets on the Hub v5.4 Integrations Collection More Articles from our Blog Nlp Community Research Falcon 2: An 11B parameter pretrained language model and VLM, trained on over 5000B tokens and 11 languages +5 28 May 23, 2024 Announcement Nlp Open-Source Sentence Transformers is joining Hugging Face! tomaarsen 88 October 21, 2025 Community Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here. Comment · Sign up or log in to comment Upvote 18 +6 Models mentioned in this article 10 Collections mentioned in this article 1