Instructions to use arvisioncode/lilt_mt_re_all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arvisioncode/lilt_mt_re_all with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import LiLTRobertaLikeForRelationExtraction model = LiLTRobertaLikeForRelationExtraction.from_pretrained("arvisioncode/lilt_mt_re_all", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from arvisioncode/lilt_mt_re_all: direct link, hf CLI and curl.
- Browser
- Download file 2.35 kB
-
https://huggingface.co/arvisioncode/lilt_mt_re_all/resolve/main/training_args.bin
- Command line
-
hf download hf://arvisioncode/lilt_mt_re_all/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/arvisioncode/lilt_mt_re_all/resolve/main/training_args.bin
2.35 kB
- Xet hash:
- 733de5de0507c5253c6511704a4188df3e2ccb07d454975c77bf98bf54cce953
- Size of remote file:
- 2.35 kB
- SHA256:
- 31bc9e41da61d6d975b235b5c5a03bdee8d0314d85b8f5b964574ec3200aaa76
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