Model2Vec는 모든 sentence transformer를 매우 작은 정적 모델로 변환하는 기술입니다. model2vec는 embedding을 생성하는 데 사용할 수 있습니다.

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Setup

pip install -U langchain-community

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Instantiation

model2vec가 설치되어 있는지 확인하세요 
pip install -U model2vec

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Indexing and Retrieval

from langchain_community.embeddings import Model2vecEmbeddings

embeddings = Model2vecEmbeddings("minishlab/potion-base-8M")

query_text = "This is a test query."
query_result = embeddings.embed_query(query_text)

document_text = "This is a test document."
document_result = embeddings.embed_documents([document_text])

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Direct Usage

다음은 model2vec를 직접 사용하는 방법입니다 
from model2vec import StaticModel

# Load a model from the HuggingFace hub (in this case the potion-base-8M model)
model = StaticModel.from_pretrained("minishlab/potion-base-8M")

# Make embeddings
embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."])

# Make sequences of token embeddings
token_embeddings = model.encode_as_sequence(["It's dangerous to go alone!", "It's a secret to everybody."])

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API reference

자세한 정보는 model2vec github repo를 확인하세요
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