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.
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Model tree for Final-year-grp24/bge-log-embeddings
Base model
BAAI/bge-large-en-v1.5
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Final-year-grp24/bge-log-embeddings") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]