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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)… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/example-documents.

sourceHugging Faceupdated 1mo agoView on Hugging Face
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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

FileModality
doc1.jpgimage (document page)
doc2.jpgimage (document page)
doc3.jpgimage (document page)
doc4.jpgimage (document page)
llama4_hgf.pngimage
qwen2.5omni_hgf.pngimage
jay_chou_superman_cant_fly.mp3audio (music)
joe_hisaishi_summer.mp3audio (music)
conversation1.mp3audio (speech)
conversation2.mp3audio (speech)
conversation3.mp3audio (speech)
mapo_tofu.mp4video
zhajiang_noodle.mp4video

Usage

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

python
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:

python
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.