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
Upload 2 files
Browse files- xgb_student_features.json +1 -0
- xgb_student_model.pkl +3 -0
xgb_student_features.json
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["age", "grade level", "gpa", "homework average", "quiz average", "previous test scores", "class participation", "attendance rate", "quality points", "gender", "class type", "class level", "subject category"]
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xgb_student_model.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:5d7cfa3c83ec625b1b202d81f9745832db775e35cb5e052704b50f552f2218c8
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size 414559
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