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model2vec

概述​

Model2Vec 是一种将任何句子转换器转换为非常小的静态模型的技术。 model2vec 可用于生成嵌入。

设置​

pip install -U langchain-community

实例化​

确保已安装 model2vec

pip install -U model2vec

索引和检索​

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])

直接使用​

以下是如何直接使用 model2vec

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."])

API 参考​

有关更多信息，请查看 model2vec github 存储库

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