Instructions to use CLMBR/old-existential-there-quantifier-lstm-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/old-existential-there-quantifier-lstm-2 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/old-existential-there-quantifier-lstm-2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c46b8d5b7262ce313422feccb98d1868530360f229722d7bab239a49e4284a78
- Size of remote file:
- 4.28 kB
- SHA256:
- f9c7b1f5ae040ae4975ba9dfee0f1ea94c7e4796487a79fa4a72ddfce5204776
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.