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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 27d9750e591c0e000af8db2fa737a2d109cd0c3be00192a4dcf6d6c44bd860f9
- Size of remote file:
- 1.11 GB
- SHA256:
- 724ca8d790b2487dbe5c8ddef7daa88451e2b231752aa82081aae27b74724053
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