Instructions to use CLMBR/superlative-quantifier-lstm-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/superlative-quantifier-lstm-1 with Transformers:
# Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/superlative-quantifier-lstm-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f2296d9db26d31fecc8310ea0d3cc421a680038c6e7eea74f2e2f96f2ab32b2
- Size of remote file:
- 627 Bytes
- SHA256:
- 93ea77ab2ce5edf934e6c67688d467d8613cbcae4c74ed911218f440ce06f8cb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.