Text Classification
Transformers
Safetensors
English
roberta
text-to-SQL
SQL
code-generation
NLQ-to-SQL
text2SQL
Security
Vulnerability detection
text-embeddings-inference
Instructions to use salmane11/SQLQueryShield with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use salmane11/SQLQueryShield with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="salmane11/SQLQueryShield")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("salmane11/SQLQueryShield") model = AutoModelForSequenceClassification.from_pretrained("salmane11/SQLQueryShield") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f63b5be3e068ebd9f10af9794434684818dcd12a5f25f33b96a86cc9db158c0
- Size of remote file:
- 5.05 kB
- SHA256:
- ba956e7c38cd0bd374b6b2b83f9ed14e452397d46c59b405bdb43ce216d7e0d4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.