sentence-transformers/example-documents

Example Documents

A small set of example documents across modalities (image, audio, video) for use in Sentence Transformers retrieval snippets and documentation. These are the kinds of files you pass to model.encode_document(...). They can safely be used as examples in your model cards if you don't want to host the example assets in your model repositories themselves.

Contents

File	Modality
doc1.jpg	image (document page)
doc2.jpg	image (document page)
doc3.jpg	image (document page)
doc4.jpg	image (document page)
llama4_hgf.png	image
qwen2.5omni_hgf.png	image
jay_chou_superman_cant_fly.mp3	audio (music)
joe_hisaishi_summer.mp3	audio (music)
conversation1.mp3	audio (speech)
conversation2.mp3	audio (speech)
conversation3.mp3	audio (speech)
mapo_tofu.mp4	video
zhajiang_noodle.mp4	video

Usage

Reference any file by its resolve URL. These documents can be encoded with a multi-vector (late interaction) MultiVectorEncoder:

from sentence_transformers import MultiVectorEncoder

model = MultiVectorEncoder("vidore/colqwen-omni-v0.1")
queries = ["What is the Llama 4 model?"]
documents = [
    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/llama4_hgf.png",
    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/conversation3.mp3",
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(model.similarity(query_embeddings, document_embeddings))

or with a single-vector SentenceTransformer:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("LCO-Embedding/LCO-Embedding-Omni-3B-2605")
queries = ["What is the Llama 4 model?"]
documents = [
    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/llama4_hgf.png",
    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/conversation3.mp3",
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(model.similarity(query_embeddings, document_embeddings))

Credits

• The document page images (doc1.jpg to doc4.jpg) are the first four test documents from vidore/colpali_train_set.
• The images (llama4_hgf.png, qwen2.5omni_hgf.png), music (jay_chou_superman_cant_fly.mp3, joe_hisaishi_summer.mp3), and videos (mapo_tofu.mp4, zhajiang_noodle.mp4) are copied from Tevatron/OmniEmbed-v0.1. Thanks to the Tevatron team.
• The speech clips (conversation1.mp3, conversation2.mp3, conversation3.mp3) are short (about 30 second) excerpts from eustlb/dailytalk-conversations-grouped.