Instructions to use jfrick/scholarly-code-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jfrick/scholarly-code-tokenizer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jfrick/scholarly-code-tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download variants/structured-code-v1-32768/tokenizer_config.json from jfrick/scholarly-code-tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 316 Bytes
-
https://huggingface.co/jfrick/scholarly-code-tokenizer/resolve/main/variants/structured-code-v1-32768/tokenizer_config.json
- Command line
-
hf download hf://jfrick/scholarly-code-tokenizer/variants/structured-code-v1-32768/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/jfrick/scholarly-code-tokenizer/resolve/main/variants/structured-code-v1-32768/tokenizer_config.json
316 Bytes
| { | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "clean_up_tokenization_spaces": false, | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "add_bos_token": false, | |
| "add_eos_token": false, | |
| "pad_token": "<|pad|>", | |
| "bos_token": "<|bos|>", | |
| "eos_token": "<|eos|>", | |
| "unk_token": "<|unk|>" | |
| } | |