Instructions to use castorini/afriberta_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/afriberta_large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="castorini/afriberta_large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("castorini/afriberta_large") model = AutoModelForMaskedLM.from_pretrained("castorini/afriberta_large") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66b1a91067c21dc05ad6b6abf6b7695a55ee97560e3757dce531e000cc173e6f
- Size of remote file:
- 503 MB
- SHA256:
- 0d0a3c04de0206b6ec857cb19f320eb1c8737c67c4b4cd5cb56fd7b64c5d277b
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