---
language:
- zh
base_model:
- BAAI/bge-base-zh-v1.5
---

## Usage
```bash
pip install -U FlagEmbedding
```

## Generate embedding for text (only Dense)
```bash
import torch
from FlagEmbedding import FlagModel

model_name = "puppyyyo/larceny-base-law-knowledge-v1"
devices = "cuda:0" if torch.cuda.is_available() else "cpu"

model = FlagModel(
    model_name,
    devices=devices,
    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)

# base-v1
# [[0.72338223 0.7122297 ], [0.5691198  0.78866345]]
# base-v2
# [[0.6811399  0.5206243 ], [0.50919324 0.676651  ]]
# base-v3
# [[0.6299723  0.5048096 ], [0.45474052 0.63200176]]
```