Instructions to use binhquoc/vi_deberta_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binhquoc/vi_deberta_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="binhquoc/vi_deberta_base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("binhquoc/vi_deberta_base") model = AutoModelForMaskedLM.from_pretrained("binhquoc/vi_deberta_base") - Notebooks
- Google Colab
- Kaggle
| { | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "mask_token": { | |
| "__type": "AddedToken", | |
| "content": "<mask>", | |
| "lstrip": true, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "model_max_length": 1024, | |
| "name_or_path": "vinai/bartpho-syllable", | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "special_tokens_map_file": null, | |
| "tokenizer_class": "BartphoTokenizer", | |
| "unk_token": "<unk>" | |
| } | |