Instructions to use haidequanbu/ESC-RANK with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haidequanbu/ESC-RANK with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("haidequanbu/ESC-RANK", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51e6a588992bd0b806934f881f626d63e869e360e9d9665990ef6bbc0e14e479
- Size of remote file:
- 10.5 MB
- SHA256:
- 6f21334a714cb1059f633aae12d412edc5bf5eb0752672a80010a2aa8e41e0c1
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