sbasu2512/legal-router

LawAI Baseline Classification Dataset

This repository hosts the dataset used to fine-tune a text classification model under the LawAI project. The baseline run achieved highly optimized classification scores using a modern encoder architecture.

Dataset Summary

The dataset consists of structured legal text samples mapped to classification labels across multiple categories.

• Total Training Examples: 78,358
• Total Validation Examples: 19,590
• Total Schema Columns: ['text', 'label']
• Target Classes: Multi-class classification (including up to index label 18 or greater).

Schema

Column Name	Data Type	Description
text	string	The raw legal text segment or case document excerpt.
label	int64	The class identifier mapping to the legal category or provision.

Baseline Model Performance

The dataset was validated by fine-tuning a microsoft/deberta-v3-base topology (DebertaV2ForSequenceClassification) using a linear learning rate warmup and decay over 6 epochs.

Convergence Metrics

The model reached optimal generalization convergence within the first epoch, remaining highly stable with no signs of overfitting throughout the training cycle:

• Validation Macro F1: 0.99255
• Validation Micro F1: 0.99208
• Final Evaluation Loss: 0.0116
• Training Throughput: ~232 samples/sec on a single GPU environment.

Usage & Loading

You can load this dataset directly via the Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset("YOUR_USERNAME/YOUR_DATASET_NAME")

# Inspecting a split
print(dataset['train'][0])