Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use ecwk/starencoder-vulnfix-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ecwk/starencoder-vulnfix-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ecwk/starencoder-vulnfix-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ecwk/starencoder-vulnfix-classification") model = AutoModelForSequenceClassification.from_pretrained("ecwk/starencoder-vulnfix-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5216075eed86c7c788888e321207f2835861092236f0ccb637042d334eba8862
- Size of remote file:
- 268 MB
- SHA256:
- e2a32a9e250cfacac691b7e31dbc31ab06bb6d41bac4f8b9a38775228069cd8a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.