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 pytorch_model.bin from arvisioncode/lilt_mt_re_all: direct link, hf CLI and curl.
- Browser
- Download file 1.15 GB
-
https://huggingface.co/arvisioncode/lilt_mt_re_all/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://arvisioncode/lilt_mt_re_all/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/arvisioncode/lilt_mt_re_all/resolve/main/pytorch_model.bin
1.15 GB
- Xet hash:
- 805f61ef6b81a40a19f1db7f9dfa77ed13025547fd3744b405f8c1a172b93e3a
- Size of remote file:
- 1.15 GB
- SHA256:
- e3ea3ff3e562584ae591d1dd31010159844887602a6e4cb31fb80032882266db
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