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
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Add TF weights
Joao GanteModel converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). All converted model outputs and hidden layers were validated against its Pytorch counterpart. Maximum crossload output difference=3.433e-05; Maximum converted output difference=3.433e-05.fc66b8e
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upload flax model
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allow flax
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Merge branch 'main' of https://huggingface.co/onlplab/alephbert-base into main
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First version of tokenizer and basic pytorch model.
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initial commit
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