bge m3 korean EU AI Act Compliance Profile

upskyy/bge-m3-korean

This model is korsts and kornli finetuning model from BAAI/bge-m3. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: BAAI/bge-m3 <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1024 tokens
• Similarity Function: Cosine Similarity

Full Model Architecture

Usage

Usage (Sentence-Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("upskyy/bge-m3-korean")

# Run inference
sentences = [
    '아이를 가진 엄마가 해변을 걷는다.',
    '두 사람이 해변을 걷는다.',
    '한 남자가 해변에서 개를 산책시킨다.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
print(similarities)
# tensor([[1.0000, 0.6173, 0.3672],
#         [0.6173, 1.0000, 0.4775],
#

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