Instructions to use CodeNinja1126/bert-p-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CodeNinja1126/bert-p-encoder with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CodeNinja1126/bert-p-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CodeNinja1126/bert-p-encoder: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/CodeNinja1126/bert-p-encoder/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://CodeNinja1126/bert-p-encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CodeNinja1126/bert-p-encoder/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- 1afa4f060d1be21ae2d3510451322fcaca1821ef3dad887e6b832cef2a7f3299
- Size of remote file:
- 712 MB
- SHA256:
- 75d72009741e0ca734305f7d21381967c37efc85ab0464b48920fb339a6eca1b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.