Instructions to use ehsanaghaei/SecureBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ehsanaghaei/SecureBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ehsanaghaei/SecureBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ehsanaghaei/SecureBERT") model = AutoModelForMaskedLM.from_pretrained("ehsanaghaei/SecureBERT", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ehsanaghaei/SecureBERT: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/ehsanaghaei/SecureBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ehsanaghaei/SecureBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ehsanaghaei/SecureBERT/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 0b44099ade116cc361e6aa5379c18f0457aff764daa8c04ebefef29cbe9f593c
- Size of remote file:
- 499 MB
- SHA256:
- e0b5108e88957e7524dd1eb3dc4ed772275c2df7362840903df8cdb7750bb238
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.