Open-source legal case analysis model case-analysis-InLegalBERT - Accurately handle various legal text tasks

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Case Analysis InLegalBERT

Developed by cite-text-analysis

A legal case analysis model fine-tuned based on InLegalBERT, specialized for legal text processing tasks

Text Classification

Transformers

Open Source License:MIT #Legal Text Analysis #High Accuracy #Specialized for Indian Law

Downloads 18

Release Time : 5/10/2024

Model Overview

This model is a fine-tuned version of InLegalBERT on legal domain datasets, primarily used for legal text classification and case analysis tasks. It demonstrates high accuracy and recall rates in evaluations.

Model Features

High Accuracy

Achieves 82.18% accuracy and 82.18% recall on the evaluation set

Legal Domain Optimization

Fine-tuned based on InLegalBERT specifically for legal texts

Multi-metric Evaluation

Provides comprehensive evaluation metrics, including macro and weighted metrics

Model Capabilities

Legal Text Classification

Case Analysis

Legal Document Processing

Use Cases

Legal Analysis

Case Classification

Classify and categorize legal cases

82.18% accuracy

Legal Document Analysis

Analyze legal document content and extract key information

Macro F1 score 66.9%

🚀 case-analysis-InLegalBERT

This model is a fine - tuned version of [law - ai/InLegalBERT](https://huggingface.co/law - ai/InLegalBERT) on an unknown dataset. It offers valuable insights and performance metrics for case analysis.

✨ Features

Fine - Tuned: Based on the pre - trained law - ai/InLegalBERT model, optimized for a specific case - analysis task.
Multiple Metrics: Evaluated using a comprehensive set of metrics including accuracy, precision, recall, etc.

📚 Documentation

Metrics

The model achieves the following results on the evaluation set:

Metric	Value
Loss	1.0434
Accuracy	0.8218
Precision	0.8145
Recall	0.8218
Precision Macro	0.6439
Recall Macro	0.6295
Macro Fpr	0.0890
Weighted Fpr	0.0674
Weighted Specificity	0.8544
Macro Specificity	0.9191
Weighted Sensitivity	0.8218
Macro Sensitivity	0.6295
F1 Micro	0.8218
F1 Macro	0.6335
F1 Weighted	0.8106

Training and evaluation data

The documentation does not provide detailed information about the training and evaluation data.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e - 05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	Precision Macro	Recall Macro	Macro Fpr	Weighted Fpr	Weighted Specificity	Macro Specificity	Weighted Sensitivity	Macro Sensitivity	F1 Micro	F1 Macro	F1 Weighted
No log	1.0	224	0.6546	0.8018	0.7632	0.8018	0.5777	0.6106	0.0978	0.0761	0.8432	0.9112	0.8018	0.6106	0.8018	0.5936	0.7820
No log	2.0	448	0.6831	0.8129	0.7732	0.8129	0.5845	0.6154	0.0923	0.0712	0.8554	0.9171	0.8129	0.6154	0.8129	0.5996	0.7926
0.607	3.0	672	0.7626	0.8263	0.8060	0.8263	0.6773	0.6341	0.0885	0.0655	0.8464	0.9182	0.8263	0.6341	0.8263	0.6362	0.8105
0.607	4.0	896	0.7839	0.8085	0.7991	0.8085	0.6391	0.6306	0.0896	0.0732	0.8754	0.9210	0.8085	0.6306	0.8085	0.6314	0.8017
0.316	5.0	1120	0.9381	0.8263	0.8127	0.8263	0.6688	0.6573	0.0822	0.0655	0.8780	0.9261	0.8263	0.6573	0.8263	0.6514	0.8161
0.316	6.0	1344	1.0434	0.8218	0.8145	0.8218	0.6907	0.6533	0.0897	0.0674	0.8528	0.9187	0.8218	0.6533	0.8218	0.6690	0.8159
0.1513	7.0	1568	1.2182	0.8018	0.8066	0.8018	0.6382	0.6399	0.0916	0.0761	0.8802	0.9205	0.8018	0.6399	0.8018	0.6375	0.8030
0.1513	8.0	1792	1.3193	0.8285	0.8070	0.8285	0.6566	0.6280	0.0882	0.0645	0.8521	0.9202	0.8285	0.6280	0.8285	0.6376	0.8152
0.0491	9.0	2016	1.3169	0.8330	0.8180	0.8330	0.6950	0.6555	0.0828	0.0627	0.8653	0.9246	0.8330	0.6555	0.8330	0.6687	0.8235
0.0491	10.0	2240	1.4460	0.8307	0.8109	0.8307	0.6584	0.6291	0.0868	0.0636	0.8533	0.9210	0.8307	0.6291	0.8307	0.6398	0.8184
0.0491	11.0	2464	1.4100	0.8419	0.8166	0.8419	0.6718	0.6399	0.0806	0.0589	0.8642	0.9265	0.8419	0.6399	0.8419	0.6464	0.8263
0.0148	12.0	2688	1.5364	0.8218	0.8105	0.8218	0.6661	0.6340	0.0903	0.0674	0.8505	0.9181	0.8218	0.6340	0.8218	0.6469	0.8137
0.0148	13.0	2912	1.5380	0.8307	0.8118	0.8307	0.6596	0.6304	0.0870	0.0636	0.8512	0.9205	0.8307	0.6304	0.8307	0.6409	0.8185
0.0031	14.0	3136	1.6139	0.8218	0.8108	0.8218	0.6451	0.6353	0.0860	0.0674	0.8685	0.9226	0.8218	0.6353	0.8218	0.6396	0.8159
0.0031	15.0	3360	1.6356	0.8263	0.8117	0.8263	0.6626	0.6477	0.0842	0.0655	0.8700	0.9241	0.8263	0.6477	0.8263	0.6529	0.8183
0.0043	16.0	3584	1.6745	0.8241	0.7994	0.8241	0.6244	0.6229	0.0884	0.0664	0.8543	0.9196	0.8241	0.6229	0.8241	0.6231	0.8108
0.0043	17.0	3808	1.7867	0.8085	0.7946	0.8085	0.6221	0.6336	0.0906	0.0732	0.8678	0.9191	0.8085	0.6336	0.8085	0.6229	0.7996
0.0008	18.0	4032	1.7511	0.8151	0.7971	0.8151	0.6110	0.6216	0.0901	0.0703	0.8644	0.9199	0.8151	0.6216	0.8151	0.6145	0.8046
0.0008	19.0	4256	1.5909	0.8441	0.8079	0.8441	0.6260	0.6374	0.0792	0.0580	0.8670	0.9278	0.8441	0.6374	0.8441	0.6311	0.8249
0.0008	20.0	4480	1.5721	0.8463	0.8212	0.8463	0.6727	0.6546	0.0761	0.0571	0.8753	0.9304	0.8463	0.6546	0.8463	0.6547	0.8316
0.0039	21.0	4704	1.5819	0.8396	0.8054	0.8396	0.6337	0.6200	0.0843	0.0599	0.8527	0.9231	0.8396	0.6200	0.8396	0.6245	0.8199
0.0039	22.0	4928	1.5906	0.8486	0.8236	0.8486	0.6814	0.6512	0.0770	0.0562	0.8680	0.9291	0.8486	0.6512	0.8486	0.6570	0.8333
0.0005	23.0	5152	1.7133	0.8263	0.8047	0.8263	0.6403	0.6431	0.0831	0.0655	0.8745	0.9252	0.8263	0.6431	0.8263	0.6367	0.8143
0.0005	24.0	5376	1.7813	0.8241	0.8022	0.8241	0.6515	0.6290	0.0894	0.0664	0.8490	0.9183	0.8241	0.6290	0.8241	0.6348	0.8108
0.0033	25.0	5600	1.7983	0.8218	0.8001	0.8218	0.6485	0.6281	0.0902	0.0674	0.8486	0.9176	0.8218	0.6281	0.8218	0.6328	0.8088
0.0033	26.0	5824	1.8070	0.8218	0.8001	0.8218	0.6485	0.6281	0.0902	0.0674	0.8486	0.9176	0.8218	0.6281	0.8218	0.6328	0.8088
0.0	27.0	6048	1.8198	0.8218	0.8024	0.8218	0.6439	0.6295	0.0890	0.0674	0.8544	0.9191	0.8218	0.6295	0.8218	0.6335	0.8106
0.0	28.0	6272	1.8243	0.8218	0.8024	0.8218	0.6439	0.6295	0.0890	0.0674	0.8544	0.9191	0.8218	0.6295	0.8218	0.6335	0.8106
0.0	29.0	6496	1.8277	0.8218	0.8024	0.8218	0.6439	0.6295	0.0890	0.0674	0.8544	0.9191	0.8218	0.6295	0.8218	0.6335	0.8106
0.0003	30.0	6720	1.8292	0.8218	0.8024	0.8218	0.6439	0.6295	0.0890	0.0674	0.8544	0.9191	0.8218	0.6295	0.8218	0.6335	0.8106

Framework versions

Transformers 4.39.3
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.15.2

📄 License

This model is released under the MIT license.

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