Instructions to use egerber1/sap_predictions_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use egerber1/sap_predictions_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="egerber1/sap_predictions_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("egerber1/sap_predictions_model") model = AutoModelForSequenceClassification.from_pretrained("egerber1/sap_predictions_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7606009da037e931d854b8dbd23f2f5859b6d5e57cd6eb331788a2db8c8c4fbe
- Size of remote file:
- 5.3 kB
- SHA256:
- e9fb4d0e2bef670efb32a4ee3ab4ed995c3bebdb6c0c39836556cc2afb641a45
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.