Instructions to use dsfsi/BantuBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dsfsi/BantuBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dsfsi/BantuBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dsfsi/BantuBERTa") model = AutoModelForMaskedLM.from_pretrained("dsfsi/BantuBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0aa7fc41cd4338fe76db5b62cb7975f9eaae7e6359f4fca04a711eba2d538ac
- Size of remote file:
- 446 MB
- SHA256:
- 7f9ec5d7cc6db031544f19e3834b04e066c8e6688e97002c26c31d921d43417e
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