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 sentencepiece.bpe.model from castorini/afriberta_base: direct link, hf CLI and curl.
- Browser
- Download file 1.55 MB
-
https://huggingface.co/castorini/afriberta_base/resolve/main/sentencepiece.bpe.model
- Command line
-
hf download hf://castorini/afriberta_base/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/castorini/afriberta_base/resolve/main/sentencepiece.bpe.model
1.55 MB
- Xet hash:
- 4e77d261063306a69967f078c09e7d7b6cd33e8183da13c65eb73af84a260fef
- Size of remote file:
- 1.55 MB
- SHA256:
- 6419b3044bff45e94e0553cbb81425fd06046e9294b33555e23fdc69377dba6f
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