metadata

language:
  - id
license: mit
base_model: indolem/indobert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: nerugm-lora-r16-2
    results: []

nerugm-lora-r16-2

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

Loss: 0.1626
Precision: 0.6848
Recall: 0.8525
F1: 0.7595
Accuracy: 0.9472

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: 16
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
1.1483	1.0	106	0.6900	0.0	0.0	0.0	0.8449
0.6875	2.0	212	0.5737	0.0	0.0	0.0	0.8464
0.5874	3.0	318	0.4661	0.2692	0.0619	0.1007	0.8634
0.4729	4.0	424	0.3599	0.4753	0.3127	0.3772	0.8982
0.3692	5.0	530	0.2940	0.5714	0.6136	0.5917	0.9247
0.3058	6.0	636	0.2527	0.6110	0.7227	0.6622	0.9335
0.2636	7.0	742	0.2246	0.6402	0.7611	0.6954	0.9375
0.24	8.0	848	0.2091	0.6578	0.8053	0.7241	0.9417
0.2228	9.0	954	0.1986	0.6404	0.8142	0.7169	0.9402
0.2105	10.0	1060	0.1821	0.6611	0.8230	0.7332	0.9417
0.2007	11.0	1166	0.1794	0.6675	0.8289	0.7395	0.9432
0.195	12.0	1272	0.1808	0.6597	0.8407	0.7393	0.9430
0.19	13.0	1378	0.1690	0.6787	0.8289	0.7463	0.9460
0.1835	14.0	1484	0.1631	0.6870	0.8289	0.7513	0.9477
0.1821	15.0	1590	0.1671	0.6835	0.8407	0.7540	0.9472
0.1774	16.0	1696	0.1668	0.6896	0.8584	0.7648	0.9472
0.1764	17.0	1802	0.1635	0.6899	0.8466	0.7603	0.9477
0.1729	18.0	1908	0.1654	0.6856	0.8555	0.7612	0.9472
0.1726	19.0	2014	0.1628	0.6872	0.8555	0.7622	0.9477
0.1684	20.0	2120	0.1626	0.6848	0.8525	0.7595	0.9472

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2