Instructions to use rn7s2/ml_anomaly with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rn7s2/ml_anomaly with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rn7s2/ml_anomaly")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rn7s2/ml_anomaly") model = AutoModelForSequenceClassification.from_pretrained("rn7s2/ml_anomaly", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rn7s2/ml_anomaly: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/rn7s2/ml_anomaly/resolve/main/training_args.bin
- Command line
-
hf download hf://rn7s2/ml_anomaly/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rn7s2/ml_anomaly/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- bdaa5b7ec64896c9ec66432ea203b0df02ed6c8e50d8a6818bebbbb16c132f2d
- Size of remote file:
- 5.05 kB
- SHA256:
- fd396257b766822fcec3fbf3e16c6cea986a6c313861e7d75b39ad28e0c902c0
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