Instructions to use gngpostalsrvc/BERiT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gngpostalsrvc/BERiT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="gngpostalsrvc/BERiT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("gngpostalsrvc/BERiT") model = AutoModelForMaskedLM.from_pretrained("gngpostalsrvc/BERiT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba4feb47a4093a8cc641e40df005f8a0b8c39d295984b1cf860da4c0dee6145a
- Size of remote file:
- 3.58 kB
- SHA256:
- cc6409be1de9b73f80e693e973101a8ecb2d7d6fbc5de144f57420bd8f5a0dfa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.