legal-ner-roberta – AI Model by AyushADitya | AlphaNeural AI

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AyushADitya/
legal-ner-roberta

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token-classification

generated_from_trainer

FacebookAI/roberta-base

endpoints_compatible

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legal-ner-roberta

This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1077
Overall Precision: 0.7753
Overall Recall: 0.8653
Overall F1: 0.8178
Overall Accuracy: 0.9768
F1 Case Number: 0.7298
F1 Court: 0.8661
F1 Date: 0.9777
F1 Gpe: 0.7186
F1 Judge: 0.9127
F1 Lawyer: 0.8840
F1 Org: 0.5699
F1 Other Person: 0.8598
F1 Petitioner: 0.7920
F1 Precedent: 0.6789
F1 Provision: 0.8964
F1 Respondent: 0.7281
F1 Statute: 0.8662
F1 Witness: 0.8837

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy	F1 Case Number	F1 Court	F1 Date	F1 Gpe	F1 Judge	F1 Lawyer	F1 Org	F1 Other Person	F1 Petitioner	F1 Precedent	F1 Provision	F1 Respondent	F1 Statute	F1 Witness
0.4031	1.0	344	0.1562	0.6489	0.7623	0.7010	0.9625	0.6507	0.8539	0.9644	0.5871	0.8140	0.8629	0.432	0.7338	0.5209	0.5802	0.7653	0.4011	0.8201	0.0
0.1274	2.0	688	0.1167	0.7342	0.8412	0.7841	0.9732	0.7047	0.8511	0.9733	0.6425	0.9243	0.8662	0.5013	0.8647	0.7278	0.6228	0.8757	0.6277	0.8565	0.7544
0.0869	3.0	1032	0.1077	0.7753	0.8653	0.8178	0.9768	0.7298	0.8661	0.9777	0.7186	0.9127	0.8840	0.5699	0.8598	0.7920	0.6789	0.8964	0.7281	0.8662	0.8837

Framework versions

Transformers 4.57.1
Pytorch 2.6.0+cu124
Datasets 4.4.0
Tokenizers 0.22.1