bert-base-cased_legal_ner_finetuned

khalidrajan/bert-base-cased_legal_ner_finetuned

bert-base-cased_legal_ner_finetuned is a machine learning model from khalidrajan. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

License: apache-2.0
Model type: bert
Access: Public
Created: Sep 6, 2024
Updated: Sep 6, 2024
SHA: aca3c9b8

Base models

google-bert/bert-base-cased

Model card

From the Hugging Face model README

bert-base-cased_legal_ner_finetuned

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

Loss: 0.3018
Law Precision: 0.7364
Law Recall: 0.8261
Law F1: 0.7787
Law Number: 115
Violated by Precision: 0.8525
Violated by Recall: 0.6933
Violated by F1: 0.7647
Violated by Number: 75
Violated on Precision: 0.4688
Violated on Recall: 0.4286
Violated on F1: 0.4478
Violated on Number: 70
Violation Precision: 0.6323
Violation Recall: 0.7251
Violation F1: 0.6755
Violation Number: 491
Overall Precision: 0.6524
Overall Recall: 0.7097
Overall F1: 0.6798
Overall Accuracy: 0.9439

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	0.8046	0.0	0.0	0.0	115	0.0	0.0	0.0	75	0.0	0.0	0.0	70	0.0	0.0	0.0	491	0.0	0.0	0.0	0.7619
No log	2.0	170	0.4050	0.0	0.0	0.0	115	0.0	0.0	0.0	75	0.0	0.0	0.0	70	0.1835	0.2037	0.1931	491	0.1835	0.1332	0.1543	0.8819
No log	3.0	255	0.2861	0.6111	0.4783	0.5366	115	0.1818	0.0533	0.0825	75	0.4	0.0571	0.1000	70	0.4345	0.5540	0.4870	491	0.4479	0.4461	0.4470	0.9130
No log	4.0	340	0.2552	0.75	0.7043	0.7265	115	0.5625	0.36	0.4390	75	0.3429	0.1714	0.2286	70	0.4924	0.5927	0.5379	491	0.5256	0.5473	0.5362	0.9257
No log	5.0	425	0.2676	0.7154	0.7652	0.7395	115	0.7308	0.5067	0.5984	75	0.2778	0.1429	0.1887	70	0.5368	0.6090	0.5706	491	0.5664	0.5792	0.5727	0.9300
0.4786	6.0	510	0.2663	0.6767	0.7826	0.7258	115	0.7903	0.6533	0.7153	75	0.3684	0.4	0.3836	70	0.6155	0.7271	0.6667	491	0.6157	0.6977	0.6542	0.9366
0.4786	7.0	595	0.2352	0.6957	0.8348	0.7589	115	0.7941	0.72	0.7552	75	0.4242	0.4	0.4118	70	0.5799	0.7169	0.6412	491	0.6030	0.7057	0.6503	0.9412
0.4786	8.0	680	0.2728	0.6835	0.8261	0.7480	115	0.7857	0.7333	0.7586	75	0.3596	0.4571	0.4025	70	0.5916	0.7434	0.6588	491	0.5978	0.7284	0.6567	0.9415
0.4786	9.0	765	0.2952	0.7385	0.8348	0.7837	115	0.8088	0.7333	0.7692	75	0.5	0.5	0.5	70	0.6246	0.7352	0.6754	491	0.6466	0.7284	0.6850	0.9433
0.4786	10.0	850	0.3018	0.7364	0.8261	0.7787	115	0.8525	0.6933	0.7647	75	0.4688	0.4286	0.4478	70	0.6323	0.7251	0.6755	491	0.6524	0.7097	0.6798	0.9439

Framework versions

Transformers 4.44.0
Pytorch 2.4.0
Datasets 2.21.0
Tokenizers 0.19.1

Type: bert
License: apache-2.0
Created: Sep 6, 2024
Updated: Sep 6, 2024