Instructions to use CLMBR/existential-there-quantifier-lstm-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/existential-there-quantifier-lstm-1 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/existential-there-quantifier-lstm-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c2f962a85472063fa9a527bcdfcebf6106a7e4e0f83fcbc601dbbff9e737eff
- Size of remote file:
- 4.28 kB
- SHA256:
- 728f7fa0667b2730828892d0ce1368fe309577ac19d3772687fbe54786467950
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.