Instructions to use jakelever/coronabert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jakelever/coronabert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jakelever/coronabert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jakelever/coronabert") model = AutoModelForSequenceClassification.from_pretrained("jakelever/coronabert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from jakelever/coronabert: direct link, hf CLI and curl.
- Browser
- Download file 62 Bytes
-
https://huggingface.co/jakelever/coronabert/resolve/a92bcd8d5483d527e3cb4ddc1d8b96583a7536b8/tokenizer_config.json
- Command line
-
hf download hf://jakelever/coronabert@a92bcd8d5483d527e3cb4ddc1d8b96583a7536b8/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/jakelever/coronabert/resolve/a92bcd8d5483d527e3cb4ddc1d8b96583a7536b8/tokenizer_config.json
62 Bytes
| {"special_tokens_map_file": null, "full_tokenizer_file": null} |