Instructions to use CLMBR/superlative-quantifier-lstm-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/superlative-quantifier-lstm-4 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/superlative-quantifier-lstm-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e8d175aba38cd3d710ca91616b8d8602ad133adb205a5092fdbec325a4ff34d
- Size of remote file:
- 272 MB
- SHA256:
- 13c886deb50a039b6baff745412e6d48e80d4f96e87e2f7ccb80cdab9f2af826
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.