Instructions to use MaryahGreene/Student_Predict_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaryahGreene/Student_Predict_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MaryahGreene/Student_Predict_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MaryahGreene/Student_Predict_Model") model = AutoModelForSequenceClassification.from_pretrained("MaryahGreene/Student_Predict_Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06c072cbd989445ae2bcd8c8017b495d085298c48f4925e14f93b42c08fcc9a0
- Size of remote file:
- 438 MB
- SHA256:
- 69ad7ee56e0b4c6fef650df925f0b66d9991022118b830a5480f0b181823905a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.