Instructions to use jambo/microsoftBio-renet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jambo/microsoftBio-renet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jambo/microsoftBio-renet")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jambo/microsoftBio-renet") model = AutoModelForSequenceClassification.from_pretrained("jambo/microsoftBio-renet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jambo/microsoftBio-renet: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jambo/microsoftBio-renet/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://jambo/microsoftBio-renet@refs/pr/2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jambo/microsoftBio-renet/resolve/refs%2Fpr%2F2/pytorch_model.bin
438 MB
- Xet hash:
- 3af04ad056199189d288faf0aecd902829175c02006666b2baeb2af997d789be
- Size of remote file:
- 438 MB
- SHA256:
- a7483a95a256452553bc86eee50ce42561038dc3c428a38de8eaecf056253592
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