Instructions to use cahya/bart-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cahya/bart-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cahya/bart-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cahya/bart-large") model = AutoModel.from_pretrained("cahya/bart-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from cahya/bart-large: direct link, hf CLI and curl.
- Browser
- Download file 361 Bytes
-
https://huggingface.co/cahya/bart-large/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://cahya/bart-large/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/cahya/bart-large/resolve/main/tokenizer_config.json
361 Bytes
| { | |
| "add_prefix_space": false, | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "errors": "replace", | |
| "mask_token": "<mask>", | |
| "name_or_path": "./indonesian-bart-large", | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "special_tokens_map_file": null, | |
| "tokenizer_class": "BartTokenizer", | |
| "trim_offsets": true, | |
| "unk_token": "<unk>" | |
| } | |