Instructions to use CLMBR/existential-there-quantifier-lstm-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/existential-there-quantifier-lstm-4 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/existential-there-quantifier-lstm-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90d93b24de50d3ff6c26ecc0766769faab30e4f4247795d2c39b9027f7e70d25
- Size of remote file:
- 627 Bytes
- SHA256:
- 4f80d2f3bf72ac78c1a213e205a029ef187d319fbdf3afe20bfd4555f33f2bba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.