Instructions to use flagship/sqlclassification_normal_bert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flagship/sqlclassification_normal_bert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="flagship/sqlclassification_normal_bert_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("flagship/sqlclassification_normal_bert_model") model = AutoModelForSequenceClassification.from_pretrained("flagship/sqlclassification_normal_bert_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e6fdb136adfcb75a13995d1b5d85a96525726bc488641e91d5bc0012b6e25d9
- Size of remote file:
- 438 MB
- SHA256:
- dee93d39090efe7f3bd3276f6a5485599d28c2160369bf62beb70bd6d4859687
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.