Metrics

• loss: 1.0342
• accuracy: 0.8359
• precision: 0.8409
• recall: 0.8359
• precision_macro: 0.8136
• recall_macro: 0.8000
• macro_fpr: 0.0142
• weighted_fpr: 0.0138
• weighted_specificity: 0.9792
• macro_specificity: 0.9877
• weighted_sensitivity: 0.8359
• macro_sensitivity: 0.8000
• f1_micro: 0.8359
• f1_macro: 0.8010
• f1_weighted: 0.8352
• runtime: 19.9583
• samples_per_second: 64.4340
• steps_per_second: 8.0670

Metrics

• loss: 1.0345
• accuracy: 0.8358
• precision: 0.8408
• recall: 0.8358
• precision_macro: 0.8207
• recall_macro: 0.7957
• macro_fpr: 0.0143
• weighted_fpr: 0.0138
• weighted_specificity: 0.9790
• macro_specificity: 0.9877
• weighted_sensitivity: 0.8358
• macro_sensitivity: 0.7957
• f1_micro: 0.8358
• f1_macro: 0.8020
• f1_weighted: 0.8352
• runtime: 22.0569
• samples_per_second: 58.5300
• steps_per_second: 7.3450

InLegalBERT

This model is a fine-tuned version of law-ai/InLegalBERT on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.0763
• Accuracy: 0.8304
• Precision: 0.8363
• Recall: 0.8304
• Precision Macro: 0.7959
• Recall Macro: 0.8029
• Macro Fpr: 0.0150
• Weighted Fpr: 0.0145
• Weighted Specificity: 0.9774
• Macro Specificity: 0.9871
• Weighted Sensitivity: 0.8296
• Macro Sensitivity: 0.8029
• F1 Micro: 0.8296
• F1 Macro: 0.7954
• F1 Weighted: 0.8283

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: 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: 15
• 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
1.065	1.0	643	0.6395	0.7994	0.7818	0.7994	0.6194	0.6308	0.0185	0.0176	0.9714	0.9847	0.7994	0.6308	0.7994	0.6029	0.7804
0.5866	2.0	1286	0.6907	0.8187	0.8199	0.8187	0.7285	0.7366	0.0161	0.0156	0.9765	0.9864	0.8187	0.7366	0.8187	0.7276	0.8152
0.4622	3.0	1929	0.8056	0.8180	0.8137	0.8180	0.7227	0.7376	0.0162	0.0156	0.9764	0.9863	0.8180	0.7376	0.8180	0.7283	0.8150
0.2398	4.0	2572	0.9310	0.8172	0.8235	0.8172	0.7661	0.7425	0.0161	0.0157	0.9762	0.9862	0.8172	0.7425	0.8172	0.7407	0.8161
0.1611	5.0	3215	1.0763	0.8304	0.8363	0.8304	0.8174	0.7918	0.0148	0.0144	0.9784	0.9873	0.8304	0.7918	0.8304	0.7986	0.8304
0.1055	6.0	3858	1.1377	0.8257	0.8275	0.8257	0.8039	0.7810	0.0154	0.0149	0.9775	0.9869	0.8257	0.7810	0.8257	0.7863	0.8246
0.0463	7.0	4501	1.3215	0.8071	0.8111	0.8071	0.7692	0.7689	0.0172	0.0168	0.9761	0.9856	0.8071	0.7689	0.8071	0.7661	0.8078
0.031	8.0	5144	1.3483	0.8203	0.8170	0.8203	0.7773	0.7727	0.0161	0.0154	0.9751	0.9864	0.8203	0.7727	0.8203	0.7690	0.8175
0.0202	9.0	5787	1.3730	0.8280	0.8263	0.8280	0.7818	0.7803	0.0152	0.0146	0.9779	0.9871	0.8280	0.7803	0.8280	0.7753	0.8256
0.0133	10.0	6430	1.5407	0.8164	0.8163	0.8164	0.7688	0.7779	0.0165	0.0158	0.9751	0.9861	0.8164	0.7779	0.8164	0.7655	0.8135
0.0051	11.0	7073	1.5235	0.8226	0.8265	0.8226	0.7900	0.7680	0.0156	0.0152	0.9769	0.9866	0.8226	0.7680	0.8226	0.7744	0.8234
0.0027	12.0	7716	1.5643	0.8265	0.8259	0.8265	0.7805	0.7841	0.0154	0.0148	0.9772	0.9869	0.8265	0.7841	0.8265	0.7775	0.8245
0.002	13.0	8359	1.5516	0.8280	0.8273	0.8280	0.7882	0.7902	0.0152	0.0146	0.9779	0.9871	0.8280	0.7902	0.8280	0.7860	0.8262
0.0015	14.0	9002	1.5835	0.8273	0.8268	0.8273	0.7943	0.8022	0.0153	0.0147	0.9773	0.9870	0.8273	0.8022	0.8273	0.7943	0.8259
0.0007	15.0	9645	1.5914	0.8296	0.8293	0.8296	0.7959	0.8029	0.0150	0.0145	0.9774	0.9871	0.8296	0.8029	0.8296	0.7954	0.8283

Framework versions

• Transformers 4.38.2
• Pytorch 2.1.2
• Datasets 2.1.0
• Tokenizers 0.15.2