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:
- 00a49dac53a03850733dd624b52f930be77c6b3f2533db0c6c1eaf3a8de2287c
- Size of remote file:
- 540 MB
- SHA256:
- 6fdf445a4244db04fa93521e5410fc4a6e11fe3bc01665259b637798f0591b89
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