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-r8a1d0.05
    results: []

nerugm-lora-r8a1d0.05

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: 0.1266
Precision: 0.7622
Recall: 0.8698
F1: 0.8125
Accuracy: 0.9591

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
0.7039	1.0	528	0.3293	0.5553	0.4962	0.5241	0.9123
0.2536	2.0	1056	0.1835	0.6530	0.8210	0.7274	0.9424
0.1831	3.0	1584	0.1832	0.6678	0.8210	0.7365	0.9440
0.1623	4.0	2112	0.1463	0.7213	0.8466	0.7789	0.9535
0.1439	5.0	2640	0.1387	0.7173	0.8420	0.7747	0.9541
0.1348	6.0	3168	0.1383	0.7256	0.8652	0.7893	0.9553
0.1293	7.0	3696	0.1394	0.7242	0.8652	0.7885	0.9545
0.124	8.0	4224	0.1351	0.7353	0.8698	0.7969	0.9569
0.1176	9.0	4752	0.1304	0.7404	0.8536	0.7930	0.9561
0.1153	10.0	5280	0.1278	0.7582	0.8582	0.8051	0.9585
0.111	11.0	5808	0.1304	0.7386	0.8652	0.7969	0.9579
0.109	12.0	6336	0.1323	0.7415	0.8652	0.7986	0.9565
0.1077	13.0	6864	0.1253	0.7649	0.8675	0.8130	0.9597
0.1032	14.0	7392	0.1243	0.7639	0.8629	0.8104	0.9593
0.1035	15.0	7920	0.1261	0.7664	0.8675	0.8138	0.9597
0.1017	16.0	8448	0.1258	0.7470	0.8559	0.7977	0.9577
0.1004	17.0	8976	0.1278	0.7576	0.8698	0.8098	0.9589
0.099	18.0	9504	0.1284	0.7510	0.8675	0.8051	0.9585
0.0991	19.0	10032	0.1256	0.7572	0.8605	0.8055	0.9581
0.0984	20.0	10560	0.1266	0.7622	0.8698	0.8125	0.9591

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2