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 optimizer.pt from demdecuong/stroke_simcse: direct link, hf CLI and curl.
- Browser
- Download file 876 MB
-
https://huggingface.co/demdecuong/stroke_simcse/resolve/main/optimizer.pt
- Command line
-
hf download hf://demdecuong/stroke_simcse/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/demdecuong/stroke_simcse/resolve/main/optimizer.pt
876 MB
- Xet hash:
- 0735296049e31b912daef408da63695038924f96f050d33064c475f2befe0d43
- Size of remote file:
- 876 MB
- SHA256:
- 2880a41c4eb9a080da0c034f8df451798181a3ea8ee839cb33f75289df3417d8
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