Instructions to use HuggingFaceTB/python-edu-scorer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceTB/python-edu-scorer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HuggingFaceTB/python-edu-scorer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/python-edu-scorer") model = AutoModelForSequenceClassification.from_pretrained("HuggingFaceTB/python-edu-scorer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from HuggingFaceTB/python-edu-scorer: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/HuggingFaceTB/python-edu-scorer/resolve/main/training_args.bin
- Command line
-
hf download hf://HuggingFaceTB/python-edu-scorer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/HuggingFaceTB/python-edu-scorer/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- 2ee37f3b19b66dc461794bbca720d8534014f21c4a70aee987dafb25cff809dd
- Size of remote file:
- 5.05 kB
- SHA256:
- b672b5ef7540dbeb34cfe2d7a27c10448c4f1aed185a491f340a3cf0f78b671b
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