legal-bert-lora

xshubhamx/legal-bert-lora

legal-bert-lora is a machine learning model from xshubhamx. 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 cc-by-sa-4.0.

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

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Public

Updated Apr 17, 2024

Repo size

4.5 GB

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Public

Hugging Face

Repo makeup

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.safetensors1.8 GB · 83%

At a glance

License: cc-by-sa-4.0
Access: Public
Created: Apr 16, 2024
Updated: Apr 17, 2024
SHA: c4c3295b

Base models

nlpaueb/legal-bert-base-uncased

Model card

From the Hugging Face model README

legal-bert-lora

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: 0.6841
Accuracy: 0.8048
Precision: 0.7955
Recall: 0.8048
Precision Macro: 0.6332
Recall Macro: 0.6316
Macro Fpr: 0.0177
Weighted Fpr: 0.0170
Weighted Specificity: 0.9753
Macro Specificity: 0.9853
Weighted Sensitivity: 0.8048
Macro Sensitivity: 0.6316
F1 Micro: 0.8048
F1 Macro: 0.6233
F1 Weighted: 0.7978

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
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

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	160	1.2986	0.6421	0.5563	0.6421	0.2826	0.3627	0.0384	0.0383	0.9531	0.9730	0.6421	0.3627	0.6421	0.3114	0.5878
No log	2.0	321	0.8962	0.7273	0.6748	0.7273	0.3629	0.4471	0.0265	0.0261	0.9685	0.9797	0.7273	0.4471	0.7273	0.3889	0.6926
No log	3.0	482	0.7814	0.7413	0.7104	0.7413	0.3985	0.4561	0.0245	0.0243	0.9703	0.9808	0.7413	0.4561	0.7413	0.4041	0.7109
1.2548	4.0	643	0.7648	0.7382	0.7158	0.7382	0.4273	0.4496	0.0254	0.0247	0.9662	0.9803	0.7382	0.4496	0.7382	0.4122	0.7112
1.2548	5.0	803	0.7329	0.7452	0.7105	0.7452	0.4162	0.4569	0.0248	0.0238	0.9668	0.9808	0.7452	0.4569	0.7452	0.4120	0.7133
1.2548	6.0	964	0.7430	0.7568	0.7547	0.7568	0.4627	0.4868	0.0229	0.0224	0.9710	0.9819	0.7568	0.4868	0.7568	0.4504	0.7424
0.6432	7.0	1125	0.7300	0.7723	0.7524	0.7723	0.5180	0.5411	0.0213	0.0206	0.9724	0.9830	0.7723	0.5411	0.7723	0.5175	0.7578
0.6432	8.0	1286	0.7212	0.7699	0.7514	0.7699	0.5096	0.5397	0.0216	0.0209	0.9727	0.9828	0.7699	0.5397	0.7699	0.5123	0.7556
0.6432	9.0	1446	0.6910	0.7839	0.7634	0.7839	0.5217	0.5566	0.0200	0.0193	0.9728	0.9838	0.7839	0.5566	0.7839	0.5280	0.7690
0.4841	10.0	1607	0.7122	0.7878	0.7732	0.7878	0.5355	0.5777	0.0195	0.0189	0.9748	0.9842	0.7878	0.5777	0.7878	0.5495	0.7776
0.4841	11.0	1768	0.6813	0.7916	0.7782	0.7916	0.5712	0.5765	0.0191	0.0185	0.9744	0.9844	0.7916	0.5765	0.7916	0.5563	0.7805
0.4841	12.0	1929	0.6845	0.7978	0.7922	0.7978	0.6111	0.6226	0.0184	0.0178	0.9759	0.9849	0.7978	0.6226	0.7978	0.6092	0.7927
0.3838	13.0	2089	0.6929	0.7986	0.7947	0.7986	0.6347	0.6038	0.0184	0.0177	0.9743	0.9849	0.7986	0.6038	0.7986	0.5954	0.7903
0.3838	14.0	2250	0.6929	0.8017	0.7960	0.8017	0.6369	0.6270	0.0180	0.0174	0.9754	0.9851	0.8017	0.6270	0.8017	0.6174	0.7952
0.3838	14.93	2400	0.6841	0.8048	0.7955	0.8048	0.6332	0.6316	0.0177	0.0170	0.9753	0.9853	0.8048	0.6316	0.8048	0.6233	0.7978

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1

License: cc-by-sa-4.0
Created: Apr 16, 2024
Updated: Apr 17, 2024