Instructions to use CLMBR/existential-there-quantifier-lstm-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/existential-there-quantifier-lstm-1 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/existential-there-quantifier-lstm-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec6e2af7e637e6e706e7c2c155e0542d7202ecd3d5d8601d6b9bbba040faaee4
- Size of remote file:
- 272 MB
- SHA256:
- dffc3e8ede19f52eb699d1f47c397048023901fdd2fceb69f574a250056cc795
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.