Instructions to use magicslabnu/gate_OutEffHop_bert_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use magicslabnu/gate_OutEffHop_bert_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="magicslabnu/gate_OutEffHop_bert_base", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("magicslabnu/gate_OutEffHop_bert_base", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("magicslabnu/gate_OutEffHop_bert_base", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5477b28b300eb65f0fc2d94ca3eb47446e530262ee0d0663a715dc69b40ae7fe
- Size of remote file:
- 439 MB
- SHA256:
- 13286889fa2419d1fa7df879aa9f3e63da4b686df0acb2fa67ff27e0274b76f1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.