Instructions to use JLake310/bert-q-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JLake310/bert-q-encoder with Transformers:
# Load model directly from transformers import AutoTokenizer, HFBertEncoder tokenizer = AutoTokenizer.from_pretrained("JLake310/bert-q-encoder") model = HFBertEncoder.from_pretrained("JLake310/bert-q-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2bfee842830544bfeb5f88188fd61159eceae1c54f96124b52583ad3d092ef1
- Size of remote file:
- 443 MB
- SHA256:
- 2d0a24b576c5c7922d96b633b09ecc80f5f76738798730453c0eee824216e737
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.