Instructions to use beva/beavers-dam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use beva/beavers-dam with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("beva/beavers-dam", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from beva/beavers-dam: direct link, hf CLI and curl.
- Browser
- Download file 394 Bytes
-
https://huggingface.co/beva/beavers-dam/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://beva/beavers-dam/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/beva/beavers-dam/resolve/main/tokenizer_config.json
394 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |