Instructions to use onlplab/alephbert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onlplab/alephbert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="onlplab/alephbert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("onlplab/alephbert-base") model = AutoModelForMaskedLM.from_pretrained("onlplab/alephbert-base") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e44235fe909ade15f0e8c348fdc231221a56ab141007b5a8759d45b5bb919f5a
- Size of remote file:
- 666 MB
- SHA256:
- c80e942e6e612275ff7072ff2bf1f4e5a2b94371c2dc2811c01d7d32a5ac0caa
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