Instructions to use nidhi9/sql_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nidhi9/sql_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nidhi9/sql_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nidhi9/sql_classifier") model = AutoModelForSequenceClassification.from_pretrained("nidhi9/sql_classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f19169262cdd88df89a207bfd5a67c06f939c7a772bd7e9ab74bae7fcd54c4f
- Size of remote file:
- 438 MB
- SHA256:
- e5cd83deda8775591ab89e57afbcc4060aaaa5c754e35ce60949f00abd0c7a5c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.