Instructions to use sijpapi/my-awesome-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sijpapi/my-awesome-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sijpapi/my-awesome-model")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("sijpapi/my-awesome-model") model = AutoModelForSequenceClassification.from_pretrained("sijpapi/my-awesome-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1313b9feb9845369c8b854304540021937645abf44f1b518c2da0c9fa8207fb0
- Size of remote file:
- 802 MB
- SHA256:
- d7a477833baf9d55c73de30bba9f59c0403fd4dbc5ec16a14c14211e6828435d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.