Instructions to use moha/mbert_ar_c19 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moha/mbert_ar_c19 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="moha/mbert_ar_c19")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("moha/mbert_ar_c19") model = AutoModelForMaskedLM.from_pretrained("moha/mbert_ar_c19", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from moha/mbert_ar_c19: direct link, hf CLI and curl.
- Browser
- Download file 670 MB
-
https://huggingface.co/moha/mbert_ar_c19/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://moha/mbert_ar_c19@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/moha/mbert_ar_c19/resolve/refs%2Fpr%2F1/flax_model.msgpack
670 MB
- Xet hash:
- 4ab9e29e558dacd5209e4d6169cba22b2c640fe6ac79306aad72e208b1093fe1
- Size of remote file:
- 670 MB
- SHA256:
- 160fef5ed6a10bf8d19ffeac9e0e0cbefca890d2878e2a8ea00324141785a6c9
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