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
Add model binary and training arguments
Browse files- pytorch_model.bin +3 -0
- training_args.bin +3 -0
pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:2592b370d23be03f85e5251999164d189724be2c6507ad85b7ab5445920f18f6
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size 498674866
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:7aa3fa727002b89992e461b2bfe56dc29566cc8ae33a2d02b68b0ec1494cdca0
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size 4344
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