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:
- 7bd30c9234c9ed78113ff9df3afbb1d0b015a749c001cb8e599dffb0b047bfc5
- Size of remote file:
- 504 MB
- SHA256:
- 1aa3553477b7a7d8adf3b903763689c9e88790a57a874462ab8c6302a2d85882
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