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 model.safetensors from HuggingFaceTB/python-edu-scorer: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/HuggingFaceTB/python-edu-scorer/resolve/main/model.safetensors
- Command line
-
hf download hf://HuggingFaceTB/python-edu-scorer/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/HuggingFaceTB/python-edu-scorer/resolve/main/model.safetensors
438 MB
- Xet hash:
- 0039ec506390beeb53816a9fb0c9bd20abd01b60d955059d9f41fe4ad64a0079
- Size of remote file:
- 438 MB
- SHA256:
- b8fe896582277f741e6b0cc49f49febb06555ff62d9b22c060590dce312049eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.