flan-t5-base_legal_ner_finetuned

This model is a fine-tuned version of google/flan-t5-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3290
• Law Precision: 0.5522
• Law Recall: 0.5968
• Law F1: 0.5736
• Law Number: 124
• Violated by Precision: 0.0
• Violated by Recall: 0.0
• Violated by F1: 0.0
• Violated by Number: 77
• Violated on Precision: 0.0845
• Violated on Recall: 0.0845
• Violated on F1: 0.0845
• Violated on Number: 71
• Violation Precision: 0.2370
• Violation Recall: 0.3800
• Violation F1: 0.2919
• Violation Number: 479
• Overall Precision: 0.2352
• Overall Recall: 0.3489
• Overall F1: 0.2810
• Overall Accuracy: 0.9247

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: 8
• eval_batch_size: 8
• 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
No log	1.0	85	3.3698	0.0002	0.0081	0.0005	124	0.0004	0.0260	0.0008	77	0.0010	0.0423	0.0020	71	0.0046	0.0209	0.0076	479	0.0011	0.0213	0.0021	0.2756
No log	2.0	170	1.6151	0.0	0.0	0.0	124	0.0	0.0	0.0	77	0.0	0.0	0.0	71	0.0104	0.0355	0.0161	479	0.0029	0.0226	0.0051	0.6383
No log	3.0	255	0.9385	0.0	0.0	0.0	124	0.0	0.0	0.0	77	0.0	0.0	0.0	71	0.0129	0.0543	0.0209	479	0.0075	0.0346	0.0124	0.7646
No log	4.0	340	0.6876	0.0013	0.0081	0.0022	124	0.0	0.0	0.0	77	0.0	0.0	0.0	71	0.0371	0.1148	0.0561	479	0.0210	0.0746	0.0327	0.8109
No log	5.0	425	0.5094	0.0097	0.0645	0.0168	124	0.0	0.0	0.0	77	0.05	0.0141	0.0220	71	0.0824	0.2589	0.125	479	0.0511	0.1771	0.0793	0.8448
1.8667	6.0	510	0.4201	0.0325	0.25	0.0575	124	0.0	0.0	0.0	77	0.0526	0.0282	0.0367	71	0.1122	0.2985	0.1631	479	0.0726	0.2344	0.1109	0.8651
1.8667	7.0	595	0.3759	0.0525	0.4194	0.0934	124	0.0087	0.0130	0.0104	77	0.0698	0.0845	0.0764	71	0.1441	0.3111	0.1970	479	0.0935	0.2770	0.1398	0.8792
1.8667	8.0	680	0.3463	0.1856	0.5403	0.2763	124	0.0092	0.0130	0.0108	77	0.0510	0.0704	0.0592	71	0.1955	0.3800	0.2582	479	0.1701	0.3395	0.2267	0.9090
1.8667	9.0	765	0.3315	0.4516	0.5645	0.5018	124	0.0	0.0	0.0	77	0.0769	0.0845	0.0805	71	0.2240	0.3779	0.2813	479	0.2176	0.3422	0.2660	0.9221
1.8667	10.0	850	0.3290	0.5522	0.5968	0.5736	124	0.0	0.0	0.0	77	0.0845	0.0845	0.0845	71	0.2370	0.3800	0.2919	479	0.2352	0.3489	0.2810	0.9247

Framework versions

• Transformers 4.44.0
• Pytorch 2.4.0
• Datasets 2.21.0
• Tokenizers 0.19.1