Feature Extraction
Transformers
Safetensors
English
mpnet
cybersecurity
embeddings
classification
text-embeddings-inference
Instructions to use selfconstruct3d/mpnet-classification-finetuned-cyber-groups with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use selfconstruct3d/mpnet-classification-finetuned-cyber-groups with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="selfconstruct3d/mpnet-classification-finetuned-cyber-groups")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("selfconstruct3d/mpnet-classification-finetuned-cyber-groups") model = AutoModel.from_pretrained("selfconstruct3d/mpnet-classification-finetuned-cyber-groups") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83ed5f5963607bcaa88c20705b01ee5288ded8a79cfaaa67ddf2056fa191381b
- Size of remote file:
- 438 MB
- SHA256:
- deb026eb628be0948c0446889f2bc108ad96b7911ee36c1f2ec5c94662f27d04
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.