Instructions to use demdecuong/stroke_sup_simcse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use demdecuong/stroke_sup_simcse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="demdecuong/stroke_sup_simcse")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("demdecuong/stroke_sup_simcse") model = AutoModel.from_pretrained("demdecuong/stroke_sup_simcse", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download scheduler.pt from demdecuong/stroke_sup_simcse: direct link, hf CLI and curl.
- Browser
- Download file 623 Bytes
-
https://huggingface.co/demdecuong/stroke_sup_simcse/resolve/main/scheduler.pt
- Command line
-
hf download hf://demdecuong/stroke_sup_simcse/scheduler.pt
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curl -L -o scheduler.pt https://huggingface.co/demdecuong/stroke_sup_simcse/resolve/main/scheduler.pt
623 Bytes
- Xet hash:
- 1978cb5d53dcd83434a32451917dc4e2bb535b164a4ff3692c02183487095ed4
- Size of remote file:
- 623 Bytes
- SHA256:
- a31db339da4025c7d348eaa0271b4dd805c3c369c78ba55a1569580cbb0e0945
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