Instructions to use demdecuong/stroke_simcse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use demdecuong/stroke_simcse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="demdecuong/stroke_simcse")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("demdecuong/stroke_simcse") model = AutoModel.from_pretrained("demdecuong/stroke_simcse", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scheduler.pt from demdecuong/stroke_simcse: direct link, hf CLI and curl.
- Browser
- Download file 623 Bytes
-
https://huggingface.co/demdecuong/stroke_simcse/resolve/main/scheduler.pt
- Command line
-
hf download hf://demdecuong/stroke_simcse/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/demdecuong/stroke_simcse/resolve/main/scheduler.pt
623 Bytes
- Xet hash:
- ff1c646af69e65c14fdc325aaf986e478943db7efc1170074388bf268b77a683
- Size of remote file:
- 623 Bytes
- SHA256:
- c34cd051aa6ad6fcf6928a695e010571d3c11ed8823b2adfbdc9e20bb28bc4c0
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