Instructions to use arvisioncode/lilt_ls_re_en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arvisioncode/lilt_ls_re_en with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import LiLTRobertaLikeForRelationExtraction model = LiLTRobertaLikeForRelationExtraction.from_pretrained("arvisioncode/lilt_ls_re_en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from arvisioncode/lilt_ls_re_en: direct link, hf CLI and curl.
- Browser
- Download file 1.15 GB
-
https://huggingface.co/arvisioncode/lilt_ls_re_en/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://arvisioncode/lilt_ls_re_en/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/arvisioncode/lilt_ls_re_en/resolve/main/pytorch_model.bin
1.15 GB
- Xet hash:
- af724c2d96365e146c9d864d8e30324b70a3cad2a97c05b57ba5a5237f959e42
- Size of remote file:
- 1.15 GB
- SHA256:
- e6f19968805d559a0d51db6b70daacd722dedae99d8008167ade4f6235843998
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