Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
text-embeddings-inference
Instructions to use mariadg/AttackVectorClassifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mariadg/AttackVectorClassifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mariadg/AttackVectorClassifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mariadg/AttackVectorClassifier") model = AutoModelForSequenceClassification.from_pretrained("mariadg/AttackVectorClassifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from mariadg/AttackVectorClassifier: direct link, hf CLI and curl.
- Browser
- Download file 997 MB
-
https://huggingface.co/mariadg/AttackVectorClassifier/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://mariadg/AttackVectorClassifier/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/mariadg/AttackVectorClassifier/resolve/main/last-checkpoint/optimizer.pt
997 MB
- Xet hash:
- c992f673efb56ae5d8b1d0eaa23a549360d434c362ea1d3d6bddc2df5ac114ad
- Size of remote file:
- 997 MB
- SHA256:
- bee11593ba60756f0666257e18353644213d779ad62bd4e06b8dd79bf580ca6d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.