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 train_results.json from arvisioncode/lilt_mt_re_all: direct link, hf CLI and curl.
- Browser
- Download file 122 Bytes
-
https://huggingface.co/arvisioncode/lilt_mt_re_all/resolve/main/train_results.json
- Command line
-
hf download hf://arvisioncode/lilt_mt_re_all/train_results.json
-
curl -L -o train_results.json https://huggingface.co/arvisioncode/lilt_mt_re_all/resolve/main/train_results.json
122 Bytes
| { | |
| "epoch": 191.39, | |
| "train_runtime": 19555.7785, | |
| "train_samples": 1665, | |
| "train_samples_per_second": 2.045 | |
| } |