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 pytorch_model.bin from demdecuong/stroke_simcse: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/demdecuong/stroke_simcse/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://demdecuong/stroke_simcse/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/demdecuong/stroke_simcse/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 43f7b105708481c08cda4df55091642872c4c835fd9a94d7e0009b403d91bebb
- Size of remote file:
- 438 MB
- SHA256:
- ac10a7342bcb924fa670cac5d0ce8e53792e48f3dc951922c6b5aa969d0145cb
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