case-analysis-legal-bert-base-uncased

cite-text-analysis/case-analysis-legal-bert-base-uncased

case-analysis-legal-bert-base-uncased is a text classification model from cite-text-analysis. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as cc-by-sa-4.0.

- loss: 1.0628 - accuracy: 0.8708 - precision: 0.8661 - recall: 0.8708 - precisionmacro: 0.8180 - recallmacro: 0.6890 - macrofpr: 0.0681 - weightedfpr: 0.0471 - weightedspecificity: 0.8788 - macrospecificity: 0.9374 -…

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At a glance

Task: Text Classification
Library: transformers
License: cc-by-sa-4.0
Model type: bert
Access: Public
Created: May 10, 2024
Updated: May 10, 2024
SHA: 04ed2caa

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Base models

nlpaueb/legal-bert-base-uncased

Model card

From the Hugging Face model README

Metrics

loss: 1.0628
accuracy: 0.8708
precision: 0.8661
recall: 0.8708
precision_macro: 0.8180
recall_macro: 0.6890
macro_fpr: 0.0681
weighted_fpr: 0.0471
weighted_specificity: 0.8788
macro_specificity: 0.9374
weighted_sensitivity: 0.8708
macro_sensitivity: 0.6890
f1_micro: 0.8708
f1_macro: 0.7165
f1_weighted: 0.8586
runtime: 13.9241
samples_per_second: 32.2460
steps_per_second: 4.0940

case-analysis-legal-bert-base-uncased

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

Loss: 1.3822
Accuracy: 0.8263
Precision: 0.8205
Recall: 0.8263
Precision Macro: 0.6455
Recall Macro: 0.6413
Macro Fpr: 0.0910
Weighted Fpr: 0.0732
Weighted Specificity: 0.8622
Macro Specificity: 0.9177
Weighted Sensitivity: 0.8085
Macro Sensitivity: 0.6413
F1 Micro: 0.8085
F1 Macro: 0.6429
F1 Weighted: 0.8061

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: 30
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
No log	1.0	224	1.0114	0.6570	0.6179	0.6570	0.4664	0.4075	0.1948	0.1482	0.6727	0.8324	0.6570	0.4075	0.6570	0.4081	0.6166
No log	2.0	448	0.7650	0.7751	0.7425	0.7751	0.5566	0.5806	0.1094	0.0882	0.8406	0.9039	0.7751	0.5806	0.7751	0.5659	0.7564
0.7677	3.0	672	0.7342	0.7817	0.7695	0.7817	0.5674	0.5967	0.1041	0.0851	0.8515	0.9083	0.7817	0.5967	0.7817	0.5707	0.7678
0.7677	4.0	896	0.7968	0.8174	0.7766	0.8174	0.6036	0.5893	0.0965	0.0693	0.8368	0.9136	0.8174	0.5893	0.8174	0.5904	0.7921
0.482	5.0	1120	0.8171	0.8085	0.7853	0.8085	0.6366	0.6038	0.0990	0.0732	0.8346	0.9108	0.8085	0.6038	0.8085	0.6141	0.7940
0.482	6.0	1344	0.8910	0.8241	0.8028	0.8241	0.6660	0.6359	0.0875	0.0664	0.8606	0.9212	0.8241	0.6359	0.8241	0.6315	0.8084
0.2993	7.0	1568	1.0094	0.8040	0.8109	0.8040	0.6742	0.6774	0.0891	0.0751	0.8829	0.9217	0.8040	0.6774	0.8040	0.6742	0.8067
0.2993	8.0	1792	1.1504	0.8107	0.7968	0.8107	0.6228	0.6330	0.0897	0.0722	0.8708	0.9204	0.8107	0.6330	0.8107	0.6229	0.8016
0.1367	9.0	2016	1.2533	0.8062	0.8059	0.8062	0.6537	0.6225	0.0948	0.0742	0.8595	0.9164	0.8062	0.6225	0.8062	0.6360	0.8045
0.1367	10.0	2240	1.2516	0.8174	0.8107	0.8174	0.6621	0.6499	0.0873	0.0693	0.8701	0.9219	0.8174	0.6499	0.8174	0.6554	0.8137
0.1367	11.0	2464	1.3822	0.8263	0.8205	0.8263	0.7085	0.6696	0.0833	0.0655	0.8711	0.9243	0.8263	0.6696	0.8263	0.6764	0.8195
0.055	12.0	2688	1.4574	0.8018	0.8127	0.8018	0.6369	0.6443	0.0883	0.0761	0.8844	0.9216	0.8018	0.6443	0.8018	0.6399	0.8068
0.055	13.0	2912	1.6634	0.7884	0.7810	0.7884	0.6090	0.6042	0.1002	0.0821	0.8619	0.9126	0.7884	0.6042	0.7884	0.6042	0.7831
0.0431	14.0	3136	1.5085	0.8285	0.8077	0.8285	0.6476	0.6367	0.0850	0.0645	0.8633	0.9229	0.8285	0.6367	0.8285	0.6382	0.8166
0.0431	15.0	3360	1.6411	0.8107	0.7936	0.8107	0.6243	0.6262	0.0914	0.0722	0.8626	0.9183	0.8107	0.6262	0.8107	0.6229	0.8014
0.0135	16.0	3584	1.7483	0.8062	0.7925	0.8062	0.6201	0.6271	0.0923	0.0742	0.8647	0.9177	0.8062	0.6271	0.8062	0.6221	0.7988
0.0135	17.0	3808	1.7233	0.7973	0.7897	0.7973	0.6148	0.6263	0.0942	0.0781	0.8682	0.9164	0.7973	0.6263	0.7973	0.6201	0.7933
0.0066	18.0	4032	1.6457	0.8241	0.8042	0.8241	0.6515	0.6388	0.0879	0.0664	0.8522	0.9191	0.8241	0.6388	0.8241	0.6391	0.8115
0.0066	19.0	4256	1.6614	0.8174	0.7976	0.8174	0.6324	0.6420	0.0865	0.0693	0.8703	0.9219	0.8174	0.6420	0.8174	0.6318	0.8061
0.0066	20.0	4480	1.6997	0.8129	0.8023	0.8129	0.6435	0.6576	0.0860	0.0712	0.8759	0.9222	0.8129	0.6576	0.8129	0.6462	0.8061
0.0067	21.0	4704	1.6540	0.8218	0.8000	0.8218	0.6473	0.6380	0.0880	0.0674	0.8560	0.9195	0.8218	0.6380	0.8218	0.6356	0.8088
0.0067	22.0	4928	1.7329	0.8085	0.7945	0.8085	0.6313	0.6267	0.0930	0.0732	0.8548	0.9158	0.8085	0.6267	0.8085	0.6282	0.8011
0.0028	23.0	5152	1.7949	0.8062	0.8004	0.8062	0.6365	0.6419	0.0902	0.0742	0.8708	0.9193	0.8062	0.6419	0.8062	0.6389	0.8032
0.0028	24.0	5376	1.8086	0.8085	0.8026	0.8085	0.6387	0.6429	0.0893	0.0732	0.8715	0.9200	0.8085	0.6429	0.8085	0.6405	0.8054
0.0001	25.0	5600	1.8326	0.8085	0.7988	0.8085	0.6343	0.6251	0.0934	0.0732	0.8537	0.9155	0.8085	0.6251	0.8085	0.6287	0.8028
0.0001	26.0	5824	1.8395	0.8085	0.7988	0.8085	0.6343	0.6251	0.0934	0.0732	0.8537	0.9155	0.8085	0.6251	0.8085	0.6287	0.8028
0.0003	27.0	6048	1.8816	0.8062	0.8039	0.8062	0.6439	0.6388	0.0920	0.0742	0.8621	0.9171	0.8062	0.6388	0.8062	0.6408	0.8046
0.0003	28.0	6272	1.8956	0.8062	0.8039	0.8062	0.6439	0.6388	0.0920	0.0742	0.8621	0.9171	0.8062	0.6388	0.8062	0.6408	0.8046
0.0003	29.0	6496	1.8986	0.8062	0.8039	0.8062	0.6439	0.6388	0.0920	0.0742	0.8621	0.9171	0.8062	0.6388	0.8062	0.6408	0.8046
0.0	30.0	6720	1.8999	0.8085	0.8045	0.8085	0.6455	0.6413	0.0910	0.0732	0.8622	0.9177	0.8085	0.6413	0.8085	0.6429	0.8061

Framework versions

Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Task: Text Classification
Library: transformers
Type: bert
License: cc-by-sa-4.0
Created: May 10, 2024
Updated: May 10, 2024