Instructions to use transZ/BiBERT-ViBa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use transZ/BiBERT-ViBa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="transZ/BiBERT-ViBa")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("transZ/BiBERT-ViBa") model = AutoModel.from_pretrained("transZ/BiBERT-ViBa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85fcdb5dc78cd8a8c9552da628f16ec93c744aac4b77742f7b60c4f6c0061b42
- Size of remote file:
- 540 MB
- SHA256:
- a46a9a249afb414c40da7e219b9890162c1cb433f62cdf7ebf2b5d58dab70d20
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