Final-year-grp24/bge-log-embeddings

BGE Log Embedding Model

Fine-tuned BAAI/bge-large-en-v1.5 for log anomaly detection.

Usage

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Swapnanil09/bge-log-embeddings")
logs = ["INFO: login ok", "ERROR: DB timeout"]
embeddings = model.encode(logs, normalize_embeddings=True)
print(embeddings.shape)  # (2, 1024)

Training Details

Property	Value
Base Model	BAAI/bge-large-en-v1.5
Embedding Dim	1024
Loss	TripletLoss
Epochs	3
Batch Size	32
Hardware	Kaggle T4 x2

How It Works

Normal logs cluster together; ERROR/CRITICAL anomalies are pushed apart via triplet loss. Use the embeddings with DBSCAN or KMeans for zero-shot anomaly detection.