Instructions to use CLMBR/superlative-quantifier-lstm-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/superlative-quantifier-lstm-3 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/superlative-quantifier-lstm-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd3586f1ddbb4777125bb9954db3920223c54b883a2d26e1ca21c87e95495ff3
- Size of remote file:
- 544 MB
- SHA256:
- bce8931ca257d3745429c73745154b3072c64029100fad85499de4e67f4f7d5d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.