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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KevSun/Engessay_grading_ML") model = AutoModelForSequenceClassification.from_pretrained("KevSun/Engessay_grading_ML", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from KevSun/Engessay_grading_ML: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/KevSun/Engessay_grading_ML/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://KevSun/Engessay_grading_ML/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/KevSun/Engessay_grading_ML/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 3545b7dbdffca83c4c91236b775d2bece30f03bd517259727e38c3390169002f
- Size of remote file:
- 499 MB
- SHA256:
- 2592b370d23be03f85e5251999164d189724be2c6507ad85b7ab5445920f18f6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.