Instructions to use JunHyung1206/sig_p_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunHyung1206/sig_p_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="JunHyung1206/sig_p_encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("JunHyung1206/sig_p_encoder") model = AutoModel.from_pretrained("JunHyung1206/sig_p_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from JunHyung1206/sig_p_encoder: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/JunHyung1206/sig_p_encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://JunHyung1206/sig_p_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/JunHyung1206/sig_p_encoder/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- affe4b16ec0386864d86160fc85501b4a961fae2380ce377ca810790c5f44196
- Size of remote file:
- 443 MB
- SHA256:
- 0c5bd67a79e1fbdccd29279d12f75f2d185cbd366741bb69b9b6959f0385843e
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