Instructions to use L-NLProc/PredEx_InCaseLaw_Pred with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use L-NLProc/PredEx_InCaseLaw_Pred with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="L-NLProc/PredEx_InCaseLaw_Pred")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("L-NLProc/PredEx_InCaseLaw_Pred") model = AutoModel.from_pretrained("L-NLProc/PredEx_InCaseLaw_Pred") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 733a8ea41e79f47b1426fff71049e270293ab6d4ac4e44c61915bdcb0380a7f2
- Size of remote file:
- 438 MB
- SHA256:
- 42f7ae27cd20af713e3ae08cece88f68d096a0fe698d701b1c982d8b2327877c
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