Text Classification
Transformers
Safetensors
English
bert
legal-ai
inlegalbert
nlp
law
legal-outcome-prediction
indian-legal
text-embeddings-inference
Instructions to use vikas-maurya/justifi-inlegalbert-outcome-predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikas-maurya/justifi-inlegalbert-outcome-predictor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vikas-maurya/justifi-inlegalbert-outcome-predictor")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vikas-maurya/justifi-inlegalbert-outcome-predictor") model = AutoModelForSequenceClassification.from_pretrained("vikas-maurya/justifi-inlegalbert-outcome-predictor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa34e28e284d21bf10913f25ad2e4ecc7829615baae6a75ca001a1765e73bf09
- Size of remote file:
- 5.37 kB
- SHA256:
- 92991d30a77259770e49efca53ee92431c30b5bd93d4e24e79751301e5386fa7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.