Instructions to use T0KII/MASRIBERTV4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use T0KII/MASRIBERTV4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="T0KII/MASRIBERTV4")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("T0KII/MASRIBERTV4") model = AutoModelForMaskedLM.from_pretrained("T0KII/MASRIBERTV4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b919b9211385a40797d7cc69431ea022fcad15b72b1d9007816c23db8a6e82d
- Size of remote file:
- 1.92 GB
- SHA256:
- a85d4320de5385fa792f1561dce0a697afaa71f85bb5cbba0883ee86752729b3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.