Instructions to use L-NLProc/PredEx_InLegalBert_Pred with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use L-NLProc/PredEx_InLegalBert_Pred with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="L-NLProc/PredEx_InLegalBert_Pred")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("L-NLProc/PredEx_InLegalBert_Pred") model = AutoModel.from_pretrained("L-NLProc/PredEx_InLegalBert_Pred") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d19b3a2f71a1990da0174dd047eae22f0a9de0561f64e7914da0a7053906ae3f
- Size of remote file:
- 438 MB
- SHA256:
- c34c580e20c0d8cad2e5c740649f9bd77f514dcf76d937a9ad140cbfa5e56bc8
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