Instructions to use flagship/sqlclassification_ten with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flagship/sqlclassification_ten with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="flagship/sqlclassification_ten")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("flagship/sqlclassification_ten") model = AutoModelForSequenceClassification.from_pretrained("flagship/sqlclassification_ten") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7c806711687238413e43349631c8d5424fbd0143d43504a5db2a8f39aa612da
- Size of remote file:
- 268 MB
- SHA256:
- cba7e62bcb2038e196bf439c19da28a281e13b17be7a04b226172faa74ffee2f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.