Instructions to use CLMBR/existential-there-quantifier-lstm-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/existential-there-quantifier-lstm-4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/existential-there-quantifier-lstm-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-2900160/training_args.bin from CLMBR/existential-there-quantifier-lstm-4: direct link, hf CLI and curl.
- Browser
- Download file 4.28 kB
-
https://huggingface.co/CLMBR/existential-there-quantifier-lstm-4/resolve/main/checkpoint-2900160/training_args.bin
- Command line
-
hf download hf://CLMBR/existential-there-quantifier-lstm-4/checkpoint-2900160/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CLMBR/existential-there-quantifier-lstm-4/resolve/main/checkpoint-2900160/training_args.bin
4.28 kB
- Xet hash:
- fcd05db69221d2d1d9a4bd2c4cd2618a03dcdbd29565353fc74fd407583b84c7
- Size of remote file:
- 4.28 kB
- SHA256:
- 9f024da30547c4aca1d4ea047fe93106c39905d5b0095de152c5be9b4bf11970
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