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:
# pip install -U transformers accelerate # Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/superlative-quantifier-lstm-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-915840/training_args.bin from CLMBR/superlative-quantifier-lstm-2: direct link, hf CLI and curl.
- Browser
- Download file 4.28 kB
-
https://huggingface.co/CLMBR/superlative-quantifier-lstm-2/resolve/main/checkpoint-915840/training_args.bin
- Command line
-
hf download hf://CLMBR/superlative-quantifier-lstm-2/checkpoint-915840/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CLMBR/superlative-quantifier-lstm-2/resolve/main/checkpoint-915840/training_args.bin
4.28 kB
- Xet hash:
- 970a3a4e4162d2ccb3a4dad113961d02f52e485b2ec7f89be0ff73524678f352
- Size of remote file:
- 4.28 kB
- SHA256:
- 762f946a9775ec8026a6eb5c20b16c886c25ff4086e16a7c1a1173b741c107d2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.