Instructions to use tuhink/hacking-rewards-code-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tuhink/hacking-rewards-code-train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tuhink/hacking-rewards-code-train")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tuhink/hacking-rewards-code-train") model = AutoModelForSequenceClassification.from_pretrained("tuhink/hacking-rewards-code-train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0902a34c3acba0a7425d480df49e9a4b11fcc46022156a9caba4ebf7ae5e3a8
- Size of remote file:
- 5.37 kB
- SHA256:
- 97d8d922de52e6bcfe448a006ef4fa0e6ccacbd25f4b17acbece761565b4800b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.