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:
- e4fba7fb686b860fd7af70f1e7a750d33df513783ff1da2d901660702446cb94
- Size of remote file:
- 1.92 GB
- SHA256:
- 16537889cbb12a55b3a83b57809be622f5eab42ff1d74f940f4d5c9a954655ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.