Instructions to use GleghornLab/cvd_single with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GleghornLab/cvd_single with Transformers:
# Load model directly from transformers import AutoTokenizer, BertForSentenceSimilarity tokenizer = AutoTokenizer.from_pretrained("GleghornLab/cvd_single") model = BertForSentenceSimilarity.from_pretrained("GleghornLab/cvd_single") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 977c5477cc9c154890fa4f499cabd771ae44e81435a0ed88ba6bf27b3b25030d
- Size of remote file:
- 440 MB
- SHA256:
- d746faad21c63877bd5349b4af46c1d5c078682844ec467f34fff68842c3be6d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.