metadata
language: en
license: mit
tags:
- log-analysis
- anomaly-detection
- embeddings
- sentence-transformers
- bge
base_model: BAAI/bge-large-en-v1.5
pipeline_tag: sentence-similarity
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.