Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Gamdalf/threat_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gamdalf/threat_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gamdalf/threat_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Gamdalf/threat_model") model = AutoModelForSequenceClassification.from_pretrained("Gamdalf/threat_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cc888c3741408adbabc8beb797c7851effbb0abb5baeb6418dda34d97a9819f6
- Size of remote file:
- 4.54 kB
- SHA256:
- 303893dffb60d303cf62139073852dad06904f690618a3fb88a92694d3f4adf3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.