yjoonjang

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colbert-ko-v1

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Hello!

Starting with the next Sentence Transformers release (v6.0.0, planned for around the 18th), this checkpoint loads directly as a multi-vector (ColBERT-style late interaction) retriever through the new MultiVectorEncoder, alongside its existing PyLate usage. This PR adds a Sentence Transformers usage section to the model card and a multi-vector tag. The weights and the existing usage are untouched.

I'd love to feature this model in that release's blog post and documentation, especially once it loads without the revision pin (that is, once this PR is merged).

pip install "sentence-transformers @ git+https://github.com/huggingface/sentence-transformers.git"

from sentence_transformers import MultiVectorEncoder

model = MultiVectorEncoder("yjoonjang/colbert-ko-v1", revision="refs/pr/2")

query = "붉은 행성으로 알려진 행성은 무엇인가요?"
documents = [
    "금성은 크기와 근접성이 비슷하여 종종 지구의 쌍둥이라고 불린다.",
    "화성은 붉은 겉모습 때문에 종종 붉은 행성이라고 불린다.",
    "태양계에서 가장 큰 행성인 목성에는 뚜렷한 붉은 반점이 있다.",
    "고리로 유명한 토성은 때때로 붉은 행성으로 오인된다.",
]

query_embeddings = model.encode_query(query)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings[0].shape)
# (32, 128) (19, 128)

# MaxSim late-interaction scoring (higher is more relevant)
scores = model.similarity(query_embeddings, document_embeddings)
print(scores)
# tensor([[10.9423, 22.7836, 19.7410, 22.4239]])

• Tom Aarsen