Instructions to use Hieu/scam-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hieu/scam-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hieu/scam-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hieu/scam-detection") model = AutoModelForSequenceClassification.from_pretrained("Hieu/scam-detection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Hieu/scam-detection: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Hieu/scam-detection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Hieu/scam-detection/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Hieu/scam-detection/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 5135ebffab7749ddefe8634c4d9666b5052b42854b4d927540fde7566328bab5
- Size of remote file:
- 499 MB
- SHA256:
- bb5be9962d02bfa6e0e51f2ed826589478e5985b7f5acbacff70d5a0270ccdc1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.