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")# pip install -U transformers accelerate # 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
Download tokenizer.json from 3ebdola/Dialectal-Arabic-XLM-R-Base: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/3ebdola/Dialectal-Arabic-XLM-R-Base/resolve/main/tokenizer.json
- Command line
-
hf download hf://3ebdola/Dialectal-Arabic-XLM-R-Base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/3ebdola/Dialectal-Arabic-XLM-R-Base/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- cee9d08601e20a6b8db3d4a20ba2f71b1aa0faae30f195e9b8fd7b14b6114de2
- Size of remote file:
- 17.1 MB
- SHA256:
- de3788bb2f349135f2ad2e2b10dff3cee7f23fab906a5fbdadf20bc963b05b4c
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