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")# 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 tf_model.h5 from jakelever/coronabert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jakelever/coronabert/resolve/main/tf_model.h5
- Command line
-
hf download hf://jakelever/coronabert/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/jakelever/coronabert/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 882eb7e92aab95dbeabedc0196a622228554bf2cb286230da8e20fed19da2be2
- Size of remote file:
- 438 MB
- SHA256:
- 69264064805a6ced60232b03c8f2bafaf7f74823e78edbb70530f53d2759c8da
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