Text Classification
Transformers
Safetensors
English
longformer
legal
document-classification
multi-class-classification
msme
dispute-resolution
indian-legal
Instructions to use abhinavdread/msme-legal-dispute-classifier-longformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abhinavdread/msme-legal-dispute-classifier-longformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="abhinavdread/msme-legal-dispute-classifier-longformer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("abhinavdread/msme-legal-dispute-classifier-longformer") model = AutoModelForSequenceClassification.from_pretrained("abhinavdread/msme-legal-dispute-classifier-longformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"eval_loss": 0.8037771940231323, "eval_accuracy": 0.7281573409593698, "eval_macro_precision": 0.713679805954321, "eval_macro_recall": 0.70344535454386, "eval_macro_f1": 0.7054752805223894, "eval_runtime": 93.33408, "eval_samples_per_second": 4.6126000000000005, "eval_steps_per_second": 2.3084000000000002, "epoch": 3.0} |