Instructions to use CLMBR/existential-there-quantifier-lstm-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/existential-there-quantifier-lstm-2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/existential-there-quantifier-lstm-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-1297440/training_args.bin from CLMBR/existential-there-quantifier-lstm-2: direct link, hf CLI and curl.
- Browser
- Download file 4.28 kB
-
https://huggingface.co/CLMBR/existential-there-quantifier-lstm-2/resolve/main/checkpoint-1297440/training_args.bin
- Command line
-
hf download hf://CLMBR/existential-there-quantifier-lstm-2/checkpoint-1297440/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CLMBR/existential-there-quantifier-lstm-2/resolve/main/checkpoint-1297440/training_args.bin
4.28 kB
- Xet hash:
- 517e367748ee8e98c8f59d9b55621cb6f4c0c2f5f69e06a187077230b5764a64
- Size of remote file:
- 4.28 kB
- SHA256:
- 0536558e47191943e36eb8240fb7b5fe6af6659faf0265ffccf4d6e2abeec16b
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