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:
- 69c202d173ea9905509d365924ddbf7e40772bf69360af439beecb3d3cad1d7d
- Size of remote file:
- 272 MB
- SHA256:
- 933a353adb51a94cb2c4f4b2ada879f3ffdcc4ad4bd4b8d28ed2844ed282c011
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.