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 research/structured-code-v1/structured-curves.png from jfrick/scholarly-code-tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 164 kB
-
https://huggingface.co/jfrick/scholarly-code-tokenizer/resolve/main/research/structured-code-v1/structured-curves.png
- Command line
-
hf download hf://jfrick/scholarly-code-tokenizer/research/structured-code-v1/structured-curves.png
-
curl -L -o structured-curves.png https://huggingface.co/jfrick/scholarly-code-tokenizer/resolve/main/research/structured-code-v1/structured-curves.png
164 kB

- Xet hash:
- dde4fe27a55224a4d81a31762b2bbc02951cfbc8ff7bdf490685f90d8cc2844e
- Size of remote file:
- 164 kB
- SHA256:
- dbb69ccc8a486b41fa99ef1acb535d6204a168ca68cd29395ad59e428ec90d62
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