Instructions to use CLMBR/binding-case-lstm-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/binding-case-lstm-4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import RNNForLanguageModeling model = RNNForLanguageModeling.from_pretrained("CLMBR/binding-case-lstm-4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-2518560/training_args.bin from CLMBR/binding-case-lstm-4: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/CLMBR/binding-case-lstm-4/resolve/main/checkpoint-2518560/training_args.bin
- Command line
-
hf download hf://CLMBR/binding-case-lstm-4/checkpoint-2518560/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CLMBR/binding-case-lstm-4/resolve/main/checkpoint-2518560/training_args.bin
4.22 kB
- Xet hash:
- ba81b22f344d3314180af993b2b5e22a76327ef1cc76074edbb3daf3eff868f8
- Size of remote file:
- 4.22 kB
- SHA256:
- d3bb39a209030babfca2d2d7f1156ef3a612db31e48162a5904b009acbc0704c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.