Instructions to use VinsmokeMir/SC_OurModel_E10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VinsmokeMir/SC_OurModel_E10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VinsmokeMir/SC_OurModel_E10")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VinsmokeMir/SC_OurModel_E10") model = AutoModelForSequenceClassification.from_pretrained("VinsmokeMir/SC_OurModel_E10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from VinsmokeMir/SC_OurModel_E10: direct link, hf CLI and curl.
- Browser
- Download file 83.3 MB
-
https://huggingface.co/VinsmokeMir/SC_OurModel_E10/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://VinsmokeMir/SC_OurModel_E10/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/VinsmokeMir/SC_OurModel_E10/resolve/main/pytorch_model.bin
83.3 MB
- Xet hash:
- 3c4bb853aa74e8ea8e491b294e63a628170368a9ba0a67e3486590e9d6950d8f
- Size of remote file:
- 83.3 MB
- SHA256:
- 50273e9719a7d65b11f904126866f9977d4c46556e4c15366180b4c39181b90e
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