Instructions to use KevSun/Engessay_grading_ML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KevSun/Engessay_grading_ML with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KevSun/Engessay_grading_ML")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KevSun/Engessay_grading_ML") model = AutoModelForSequenceClassification.from_pretrained("KevSun/Engessay_grading_ML") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ab794357a5e7ebd817b195b307dc946e9d5e4e22580bd9f0d364b55b770c9a2
- Size of remote file:
- 4.34 kB
- SHA256:
- 7aa3fa727002b89992e461b2bfe56dc29566cc8ae33a2d02b68b0ec1494cdca0
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