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")# pip install -U transformers accelerate # 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
Download training_args.bin from T0KII/MASRIBERTV4: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/T0KII/MASRIBERTV4/resolve/main/training_args.bin
- Command line
-
hf download hf://T0KII/MASRIBERTV4/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/T0KII/MASRIBERTV4/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- 6e267338e24eae6846e0c284d59e441e0bae579050690416bc02ee63e27b47ec
- Size of remote file:
- 5.2 kB
- SHA256:
- 39853abe7c212a05cd37a6042b4d1c2f33b18beabbb723de8075009a188aad0f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.