This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on the opennyaiorg/InLegalNER dataset. It achieves the following results on the evaluation set:

Loss: 0.1057
Precision: 0.8370
Recall: 0.8742
F1: 0.8552
Accuracy: 0.9741

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: 12
eval_batch_size: 12
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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.4633	1.0	917	0.1226	0.7624	0.8040	0.7827	0.9642
0.1039	2.0	1834	0.1077	0.7996	0.8583	0.8279	0.9702
0.0686	3.0	2751	0.1021	0.8054	0.8695	0.8362	0.9714
0.048	4.0	3668	0.1084	0.8309	0.8706	0.8503	0.9732
0.0368	5.0	4585	0.1057	0.8370	0.8742	0.8552	0.9741

Framework versions

Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.3.2
Tokenizers 0.21.0

Amitava25/legal_ai_India_ner_results

legal_ai_India_ner_results

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Amitava25/
legal_ai_India_ner_results