Instructions to use thu-sail-lab/Time-RCD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thu-sail-lab/Time-RCD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="thu-sail-lab/Time-RCD", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("thu-sail-lab/Time-RCD", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 743 Bytes
0880420 | 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 | #!/usr/bin/env python3
"""Minimal Time-RCD inference example on synthetic data."""
from __future__ import annotations
import sys
from pathlib import Path
import numpy as np
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from time_rcd import TimeRCDDetector
def main() -> None:
rng = np.random.default_rng(42)
length = 2048
data = rng.normal(size=length)
# Inject a simple anomaly spike.
data[1000:1010] += 8.0
detector = TimeRCDDetector.from_pretrained(variant="uni")
scores = detector.predict(data)
print(f"Input shape: {data.shape}")
print(f"Score shape: {scores.shape}")
print(f"Top-5 anomaly indices: {np.argsort(scores)[-5:][::-1]}")
if __name__ == "__main__":
main()
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