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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("onlplab/alephbert-base") model = AutoModelForMaskedLM.from_pretrained("onlplab/alephbert-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
17331c2
-
Add TF weights (#1)
- Add TF weights (fc66b8ef6de64b33d2a65ef0ba1aa556280f91f3) Co-authored-by: Joao Gante <joaogante@users.noreply.huggingface.co>1745fb3
-
upload flax model
0558aca -
allow flax
b5ed677
-
Update README.md
5cf1afd -
Update README.md
9e947bd
-
Update README.md
2286340 -
Merge branch 'main' of https://huggingface.co/onlplab/alephbert-base into main
eb5dd6b -
Update readme.
f103098 -
Update README.md
2744811 -
Update README.md
99fa890 -
First version of tokenizer and basic pytorch model.
8b36f5b -
initial commit
34e548d