Instructions to use yusr9/RADAR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yusr9/RADAR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yusr9/RADAR", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("yusr9/RADAR", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 78a99f439888f8e2542efa417124a28e5f643a47611e3dd38f12bb8521db9251
- Size of remote file:
- 3.2 GB
- SHA256:
- d5718f33cc1d8616785773585d1ec90a2182f6f30c165b8ee599f63d8abbf45d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.