InLegalBERT-lora

xshubhamx/InLegalBERT-lora

InLegalBERT-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 mit.

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

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Public

Updated Apr 17, 2024

Repo size

1.9 GB

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At a glance

License: mit
Access: Public
Created: Apr 17, 2024
Updated: Apr 17, 2024
SHA: f3111118

Base models

law-ai/InLegalBERT

Model card

From the Hugging Face model README

InLegalBERT-lora

This model is a fine-tuned version of law-ai/InLegalBERT on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6344
Accuracy: 0.8203
Precision: 0.8092
Recall: 0.8203
Precision Macro: 0.6487
Recall Macro: 0.6625
Macro Fpr: 0.0160
Weighted Fpr: 0.0154
Weighted Specificity: 0.9771
Macro Specificity: 0.9865
Weighted Sensitivity: 0.8203
Macro Sensitivity: 0.6625
F1 Micro: 0.8203
F1 Macro: 0.6461
F1 Weighted: 0.8125

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	1.2013	0.6553	0.6007	0.6553	0.3279	0.3903	0.0365	0.0362	0.9556	0.9741	0.6553	0.3903	0.6553	0.3420	0.6147
No log	2.0	321	0.8279	0.7382	0.7211	0.7382	0.4092	0.4658	0.0248	0.0247	0.9713	0.9806	0.7382	0.4658	0.7382	0.4250	0.7237
No log	3.0	482	0.7130	0.7545	0.7255	0.7545	0.4800	0.4770	0.0233	0.0227	0.9701	0.9816	0.7545	0.4770	0.7545	0.4431	0.7305
1.1985	4.0	643	0.6922	0.7823	0.7594	0.7823	0.5188	0.5283	0.0200	0.0195	0.9740	0.9838	0.7823	0.5283	0.7823	0.5048	0.7660
1.1985	5.0	803	0.6710	0.7940	0.7734	0.7940	0.5450	0.5571	0.0190	0.0182	0.9739	0.9845	0.7940	0.5571	0.7940	0.5257	0.7718
1.1985	6.0	964	0.6455	0.7971	0.7757	0.7971	0.5353	0.5622	0.0184	0.0179	0.9754	0.9848	0.7971	0.5622	0.7971	0.5316	0.7790
0.5721	7.0	1125	0.6395	0.8002	0.7801	0.8002	0.5443	0.5784	0.0181	0.0175	0.9762	0.9851	0.8002	0.5784	0.8002	0.5486	0.7845
0.5721	8.0	1286	0.6317	0.8025	0.7833	0.8025	0.5439	0.5773	0.0178	0.0173	0.9765	0.9853	0.8025	0.5773	0.8025	0.5475	0.7874
0.5721	9.0	1446	0.6137	0.8009	0.7828	0.8009	0.5593	0.5842	0.0179	0.0174	0.9765	0.9852	0.8009	0.5842	0.8009	0.5609	0.7875
0.4166	10.0	1607	0.6249	0.8156	0.8055	0.8156	0.6398	0.6430	0.0165	0.0159	0.9772	0.9862	0.8156	0.6430	0.8156	0.6305	0.8067
0.4166	11.0	1768	0.6426	0.8125	0.8014	0.8125	0.6397	0.6520	0.0169	0.0162	0.9762	0.9859	0.8125	0.6520	0.8125	0.6372	0.8042
0.4166	12.0	1929	0.6305	0.8164	0.8050	0.8164	0.6358	0.6526	0.0164	0.0158	0.9770	0.9862	0.8164	0.6526	0.8164	0.6372	0.8083
0.3406	13.0	2089	0.6276	0.8203	0.8102	0.8203	0.6418	0.6467	0.0160	0.0154	0.9774	0.9865	0.8203	0.6467	0.8203	0.6353	0.8129
0.3406	14.0	2250	0.6428	0.8187	0.8079	0.8187	0.6467	0.6618	0.0162	0.0156	0.9771	0.9864	0.8187	0.6618	0.8187	0.6446	0.8107
0.3406	14.93	2400	0.6344	0.8203	0.8092	0.8203	0.6487	0.6625	0.0160	0.0154	0.9771	0.9865	0.8203	0.6625	0.8203	0.6461	0.8125

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1

License: mit
Created: Apr 17, 2024
Updated: Apr 17, 2024