Instructions to use moha/arabert_c19 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moha/arabert_c19 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="moha/arabert_c19")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("moha/arabert_c19") model = AutoModelForMaskedLM.from_pretrained("moha/arabert_c19") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 197f9eea615b01650db7d7cf2206ded4d43bc305549ef102d81d3f8df1c23387
- Size of remote file:
- 541 MB
- SHA256:
- 79d7e9296793f3e7719de6dd646d8f428c2337df77c0cf5fefd77efe31be0db6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.