File size: 2,436 Bytes
dbabef2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | from __future__ import annotations
from sentence_transformers import (
SentenceTransformer
)
class LegalEmbedder:
def __init__(
self,
model_name: str =
"BAAI/bge-large-en-v1.5"
):
print(
f"Loading embedding model: "
f"{model_name}"
)
self.model = (
SentenceTransformer(
model_name
)
)
# =====================================================
# DOCUMENT EMBEDDINGS
# =====================================================
def embed(
self,
texts: list[str]
):
return self.model.encode(
texts,
normalize_embeddings=True,
convert_to_numpy=True,
batch_size=16,
show_progress_bar=True
)
# =====================================================
# QUERY EMBEDDING
# =====================================================
def embed_query(
self,
query: str
):
query = (
"Represent this sentence "
"for searching relevant "
f"passages: {query}"
)
return self.model.encode(
query,
normalize_embeddings=True,
convert_to_numpy=True
)
# =====================================================
# VECTOR SIZE
# =====================================================
def vector_size(
self
) -> int:
return (
self.model
.get_sentence_embedding_dimension()
)
# =========================================================
# TEST
# =========================================================
if __name__ == "__main__":
embedder = (
LegalEmbedder()
)
texts = [
"Section 52. Facts of which Court shall take judicial notice.",
"Article 21. Protection of life and personal liberty."
]
vectors = (
embedder.embed(
texts
)
)
print()
print(
"Vectors:",
len(vectors)
)
print(
"Dimension:",
len(vectors[0])
)
query_vector = (
embedder.embed_query(
"facts judicially noticed by court"
)
)
print(
"Query Dimension:",
len(query_vector)
)
print(
"Model Dimension:",
embedder.vector_size()
) |