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:
- 25295670236766e1f355b4c96bfe3e79bcdb7324905b9506a3c95cf9e6358209
- Size of remote file:
- 4.28 kB
- SHA256:
- d7f986ca47d361f36a871bcaec5d8d55b94e6309f3decf5819f1ea789ff51885
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.