Text Classification
Transformers
Safetensors
English
bert
legal
sequence-classification
indian-law
text-embeddings-inference
Instructions to use negi2725/LegalBertNew with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use negi2725/LegalBertNew with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="negi2725/LegalBertNew")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("negi2725/LegalBertNew") model = AutoModelForSequenceClassification.from_pretrained("negi2725/LegalBertNew") - Notebooks
- Google Colab
- Kaggle
LegalBertNew โ Indian Legal Judgment Classification Model
This is a fine-tuned BERT model for binary legal judgment prediction (Guilty / Not Guilty) based on Indian case facts.
The model takes raw case text as input and outputs a classification label along with confidence scores.
## How to Use
from transformers import pipeline
classifier = pipeline("text-classification", model="negi2725/LegalBertNew")
result = classifier("The accused was found with stolen property.")
print(result)
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