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
| {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": "<mask>", "model_max_length": 256, "special_tokens_map_file": null, "name_or_path": "./tst-mlm", "tokenizer_file": null, "tokenizer_class": "PhobertTokenizer"} |