legal_deberta

This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2674
• Law Precision: 0.6932
• Law Recall: 0.8133
• Law F1: 0.7485
• Law Number: 75
• Violated by Precision: 0.8684
• Violated by Recall: 0.88
• Violated by F1: 0.8742
• Violated by Number: 75
• Violated on Precision: 0.5882
• Violated on Recall: 0.6667
• Violated on F1: 0.625
• Violated on Number: 75
• Violation Precision: 0.5287
• Violation Recall: 0.6429
• Violation F1: 0.5802
• Violation Number: 616
• Overall Precision: 0.5741
• Overall Recall: 0.6813
• Overall F1: 0.6232
• Overall Accuracy: 0.9461

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: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Law Precision	Law Recall	Law F1	Law Number	Violated by Precision	Violated by Recall	Violated by F1	Violated by Number	Violated on Precision	Violated on Recall	Violated on F1	Violated on Number	Violation Precision	Violation Recall	Violation F1	Violation Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.9748	1.0	45	1.1555	0.0	0.0	0.0	75	0.0	0.0	0.0	75	0.0	0.0	0.0	75	0.0	0.0	0.0	616	0.0	0.0	0.0	0.7437
0.4536	2.0	90	0.3670	0.0	0.0	0.0	75	0.0	0.0	0.0	75	0.0	0.0	0.0	75	0.1704	0.2955	0.2162	616	0.1704	0.2164	0.1907	0.8901
0.2704	3.0	135	0.2199	0.7059	0.64	0.6713	75	0.3095	0.1733	0.2222	75	0.0909	0.0133	0.0233	75	0.3291	0.5097	0.4000	616	0.3498	0.4471	0.3925	0.9277
0.1475	4.0	180	0.1959	0.6263	0.8267	0.7126	75	0.9153	0.72	0.8060	75	0.3182	0.3733	0.3436	75	0.4641	0.5974	0.5224	616	0.4928	0.6088	0.5447	0.9407
0.0879	5.0	225	0.2038	0.5909	0.8667	0.7027	75	0.7590	0.84	0.7975	75	0.3982	0.6	0.4787	75	0.4692	0.6055	0.5287	616	0.4959	0.6492	0.5623	0.9434
0.0499	6.0	270	0.2466	0.5913	0.9067	0.7158	75	0.7674	0.88	0.8199	75	0.4412	0.6	0.5085	75	0.4832	0.6071	0.5381	616	0.5135	0.6576	0.5766	0.9425
0.0291	7.0	315	0.2980	0.5755	0.8133	0.6740	75	0.7976	0.8933	0.8428	75	0.3802	0.6133	0.4694	75	0.4929	0.5617	0.5250	616	0.5133	0.6183	0.5609	0.9389
0.0341	8.0	360	0.2660	0.5739	0.88	0.6947	75	0.8193	0.9067	0.8608	75	0.48	0.64	0.5486	75	0.4800	0.6445	0.5502	616	0.5147	0.6885	0.5890	0.9366
0.0228	9.0	405	0.3186	0.3505	0.9067	0.5056	75	0.6126	0.9067	0.7312	75	0.3216	0.7333	0.4472	75	0.4365	0.5519	0.4875	616	0.4231	0.6314	0.5067	0.9301
0.0173	10.0	450	0.2674	0.6932	0.8133	0.7485	75	0.8684	0.88	0.8742	75	0.5882	0.6667	0.625	75	0.5287	0.6429	0.5802	616	0.5741	0.6813	0.6232	0.9461

Framework versions

• Transformers 4.31.0
• Pytorch 2.4.0+cu121
• Datasets 2.15.0
• Tokenizers 0.13.3