Instructions to use castorini/afriberta_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/afriberta_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="castorini/afriberta_base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("castorini/afriberta_base") model = AutoModelForMaskedLM.from_pretrained("castorini/afriberta_base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from castorini/afriberta_base: direct link, hf CLI and curl.
- Browser
- Download file 446 MB
-
https://huggingface.co/castorini/afriberta_base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://castorini/afriberta_base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/castorini/afriberta_base/resolve/main/pytorch_model.bin
446 MB
- Xet hash:
- 305371d55b12127c920c078033d82c0d8e149f633b3f7981a56b8a011ed55259
- Size of remote file:
- 446 MB
- SHA256:
- a1b5507320b2175f5aa01cfbb9cd853fdf92dee491dde2359eae8919eb583a8a
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