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 pytorch_model.bin from demdecuong/stroke_sup_simcse: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/demdecuong/stroke_sup_simcse/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://demdecuong/stroke_sup_simcse/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/demdecuong/stroke_sup_simcse/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 37e600b4315a74e4125b8a1729d5c6527b38302b9b5aaa4474e56ee094f57160
- Size of remote file:
- 438 MB
- SHA256:
- a66e67c01778babd040e657cf6b2a76d294f7d3faec9f620631c2d48890456c2
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