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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 35460784cbc27bd2c568fee003711569bda5e9c0495891d21803a24d6256231d
- Size of remote file:
- 499 MB
- SHA256:
- 340a9bd4418377d153fd4b09cb5c47c46b56c22273f24c06ad81840eb8f913fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.