Legal_NER_Support_Model Open-Source Legal Entity Recognition Model - Accurately Identify Specific Entities in Legal Texts

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Legal NER Support Model

Developed by Sidziesama

A legal named entity recognition model fine-tuned on Legal-BERT for identifying specific entities in legal texts

Sequence Labeling

Transformers

#Legal Entity Recognition #BERT Fine-tuning #Indian Legal Texts

Downloads 52

Release Time : 5/14/2024

Model Overview

This model is a named entity recognition model fine-tuned on the InLegalNER dataset based on the Legal-BERT foundation model, specifically designed for entity recognition tasks in legal texts.

Model Features

Specialized for Legal Domain

Fine-tuned on Legal-BERT and optimized specifically for legal texts

High Performance

Achieves an F1 score of 0.9001 and accuracy of 0.9757 on the validation set

Efficient Training

Requires only 4 training epochs to achieve good performance

Model Capabilities

Legal text analysis

Named entity recognition

Legal document processing

Use Cases

Legal Document Processing

Contract Analysis

Automatically identifies key entities in contracts such as parties, dates, amounts, etc.

Can accurately identify various entities in legal texts

Judicial Document Processing

Extracts case-related entity information from court judgments

🚀 Sidziesama/Legal_NER_Support_Model

This model is a fine - tuned version of [nlpaueb/legal - bert - base - uncased](https://huggingface.co/nlpaueb/legal - bert - base - uncased) on opennyaiorg/InLegalNER, which can achieve good performance in legal NER tasks.

🚀 Quick Start

This model is a fine - tuned version of [nlpaueb/legal - bert - base - uncased](https://huggingface.co/nlpaueb/legal - bert - base - uncased) on opennyaiorg/InLegalNER. It achieves the following results on the evaluation set:

Train Loss: 0.0501
Validation Loss: 0.0883
Train Precision: 0.8848
Train Recall: 0.9160
Train F1: 0.9001
Train Accuracy: 0.9757
Epoch: 4

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e - 05, 'decay_steps': 2945, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e - 08, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Training results

Train Loss	Validation Loss	Train Precision	Train Recall	Train F1	Train Accuracy	Epoch
0.3771	0.1228	0.8400	0.8644	0.8520	0.9655	0
0.1172	0.0962	0.8715	0.9001	0.8856	0.9725	1
0.0801	0.0895	0.8805	0.9112	0.8956	0.9745	2
0.0597	0.0881	0.8840	0.9112	0.8974	0.9751	3
0.0501	0.0883	0.8848	0.9160	0.9001	0.9757	4

Framework versions

Transformers 4.39.3
TensorFlow 2.15.0
Datasets 2.18.0
Tokenizers 0.15.2

📄 License

This model is released under the CC - BY - SA 4.0 license.

Property	Details
Model Type	Fine - tuned version of nlpaueb/legal - bert - base - uncased
Training Data	opennyaiorg/InLegalNER

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