from FlagEmbedding import FlagModel
model_name = "puppyyyo/larceny-m3-law-knowledge-v3"
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)
# m3-v1# [[0.64422363 0.41711098], [0.5521891 0.75232375]]# m3-v2# [[0.5995765 0.26392284], [0.27698418 0.6981832 ]]# m3-v3# [[0.5663224 0.24284379], [0.26776457 0.68114114]]
Runs of puppyyyo larceny-m3-ICT_v3 on huggingface.co
5
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0
24-hour runs
0
3-day runs
-1
7-day runs
1
30-day runs
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