Instructions to use CLMBR/superlative-quantifier-lstm-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/superlative-quantifier-lstm-2 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/superlative-quantifier-lstm-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ecacae1f792f328ea9c42c569bdc4fcea71cab1c5c2433be344fc30c6e006a3d
- Size of remote file:
- 544 MB
- SHA256:
- 6345b8a24dbef2cdfc7713a901fcb65c043f215e24221ca5737c1b98b73ff832
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.