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 flax_model.msgpack from jakelever/coronabert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jakelever/coronabert/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://jakelever/coronabert/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/jakelever/coronabert/resolve/main/flax_model.msgpack
438 MB
- Xet hash:
- bca4074b0f27f12f135515168ca590693ebeadd4d067b4fde6ff7628a628e507
- Size of remote file:
- 438 MB
- SHA256:
- bf1f6f25b65257a1b3e85330b925964208f0d3efce183f948925d4bc30bd4cee
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