Instructions to use sakrah/TwiBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sakrah/TwiBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sakrah/TwiBERT", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sakrah/TwiBERT") model = AutoModelForMaskedLM.from_pretrained("sakrah/TwiBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ae0bb4d3d13d42479e01a3e4af98cdcbf90dad273e555240afe08473481ce0b
- Size of remote file:
- 243 MB
- SHA256:
- 37193667b1e34623c35067415b4f0e5bd5068b2f75d592e7e323024dd0233e6d
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