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:
- 3545b7dbdffca83c4c91236b775d2bece30f03bd517259727e38c3390169002f
- Size of remote file:
- 499 MB
- SHA256:
- 2592b370d23be03f85e5251999164d189724be2c6507ad85b7ab5445920f18f6
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