File size: 1,673 Bytes
660dde6 | 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 | from __future__ import annotations
from sentence_transformers import (
SentenceTransformer
)
import numpy as np
class LegalEmbedder:
def __init__(
self,
model_name: str =
"BAAI/bge-large-en-v1.5"
):
self.model = (
SentenceTransformer(
model_name
)
)
# ==========================================
# SINGLE TEXT
# ==========================================
def embed_query(
self,
text: str
) -> np.ndarray:
return self.model.encode(
text,
normalize_embeddings=True,
convert_to_numpy=True
)
# ==========================================
# BATCH TEXTS
# ==========================================
def embed(
self,
texts: list[str]
) -> np.ndarray:
return self.model.encode(
texts,
normalize_embeddings=True,
convert_to_numpy=True,
show_progress_bar=False
)
# ==========================================
# VECTOR DIMENSION
# ==========================================
@property
def dimension(
self
) -> int:
return (
self.model
.get_sentence_embedding_dimension()
)
# =====================================================
# TEST
# =====================================================
if __name__ == "__main__":
embedder = LegalEmbedder()
vec = embedder.embed_query(
"punishment for trafficking"
)
print(
"Dimension:",
len(vec)
)
print(
vec[:10]
) |