Instructions to use DigitalUmuganda/mbaza_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DigitalUmuganda/mbaza_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="DigitalUmuganda/mbaza_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("DigitalUmuganda/mbaza_bert") model = AutoModelForMaskedLM.from_pretrained("DigitalUmuganda/mbaza_bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3cc9f0ec2d34adc37e7768bce8d13fb1229883b994a491254cabe076b706d3e
- Size of remote file:
- 533 MB
- SHA256:
- f27e5a6cd076d6deb5a6d7bd62552333bbdf74fe04295582f9ec3928fa631a32
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