Instructions to use StanfordAIMI/covid-radbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StanfordAIMI/covid-radbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="StanfordAIMI/covid-radbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("StanfordAIMI/covid-radbert") model = AutoModel.from_pretrained("StanfordAIMI/covid-radbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from StanfordAIMI/covid-radbert: direct link, hf CLI and curl.
- Browser
- Download file 62 Bytes
-
https://huggingface.co/StanfordAIMI/covid-radbert/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://StanfordAIMI/covid-radbert/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/StanfordAIMI/covid-radbert/resolve/main/tokenizer_config.json
62 Bytes
| {"special_tokens_map_file": null, "full_tokenizer_file": null} |