Instructions to use bashar-talafha/multi-dialect-bert-base-arabic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bashar-talafha/multi-dialect-bert-base-arabic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="bashar-talafha/multi-dialect-bert-base-arabic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("bashar-talafha/multi-dialect-bert-base-arabic") model = AutoModelForMaskedLM.from_pretrained("bashar-talafha/multi-dialect-bert-base-arabic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from bashar-talafha/multi-dialect-bert-base-arabic: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/bashar-talafha/multi-dialect-bert-base-arabic/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://bashar-talafha/multi-dialect-bert-base-arabic/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/bashar-talafha/multi-dialect-bert-base-arabic/resolve/main/flax_model.msgpack
443 MB
- Xet hash:
- 7c720725691cf37a05379dff607bae5025c06ee3cc5f11cd6ac28dacaf0aed80
- Size of remote file:
- 443 MB
- SHA256:
- 000002cb8700e087dae582a978984763b92bff37385b4345c2906dd45b2feab0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.