Fill-Mask
Transformers
PyTorch
xlm-roberta
Dialectal Arabic
Arabic
sequence labeling
Named entity recognition
Part-of-speech tagging
Zero-shot transfer learning
bert
Instructions to use 3ebdola/Dialectal-Arabic-XLM-R-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 3ebdola/Dialectal-Arabic-XLM-R-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="3ebdola/Dialectal-Arabic-XLM-R-Base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("3ebdola/Dialectal-Arabic-XLM-R-Base") model = AutoModelForMaskedLM.from_pretrained("3ebdola/Dialectal-Arabic-XLM-R-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad3e6aac23a8a87f79c4164f1ce17d9d5bbbd51e8edd05bd00e3d06dd9e802a8
- Size of remote file:
- 5.07 MB
- SHA256:
- cfc8146abe2a0488e9e2a0c56de7952f7c11ab059eca145a0a727afce0db2865
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