puppyyyo/4_crime_aug-m3-law-knowledge-v2

---

Usage

pip install -U FlagEmbedding

Generate embedding for text (only Dense)

from FlagEmbedding import FlagModel

model_name = "puppyyyo/4_crime_aug-m3-law-knowledge-v2"

model = FlagModel(
    model_name,
    use_fp16=False
    )

sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.", 
               "BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]

embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)