Instructions to use Rogendo/afribert-mlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rogendo/afribert-mlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Rogendo/afribert-mlm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Rogendo/afribert-mlm") model = AutoModelForMaskedLM.from_pretrained("Rogendo/afribert-mlm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e26275b6cb7cf284ca5400be9d955b7d5d762403ff79f8a0413012da6cddafe
- Size of remote file:
- 5.2 kB
- SHA256:
- 70e0c88a8df692a6196f27c41ff1dd6050248ad72f8a5d77df305ea22dfb6bde
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