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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("transZ/BiBERT-ViBa") model = AutoModel.from_pretrained("transZ/BiBERT-ViBa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download added_tokens.json from transZ/BiBERT-ViBa: direct link, hf CLI and curl.
- Browser
- Download file 17 Bytes
-
https://huggingface.co/transZ/BiBERT-ViBa/resolve/refs%2Fpr%2F1/added_tokens.json
- Command line
-
hf download hf://transZ/BiBERT-ViBa@refs/pr/1/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/transZ/BiBERT-ViBa/resolve/refs%2Fpr%2F1/added_tokens.json
17 Bytes
| {"<mask>": 64000} |