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-r4a0d0.15
    results: []

nerugm-lora-r4a0d0.15

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: 0.1301
Precision: 0.7357
Recall: 0.8652
F1: 0.7952
Accuracy: 0.9577

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
0.7663	1.0	528	0.4380	0.3934	0.1116	0.1738	0.8659
0.3481	2.0	1056	0.2220	0.6018	0.7403	0.6639	0.9339
0.2139	3.0	1584	0.1790	0.6561	0.8327	0.7339	0.9400
0.1777	4.0	2112	0.1535	0.7164	0.8559	0.7800	0.9512
0.1578	5.0	2640	0.1445	0.7367	0.8698	0.7978	0.9535
0.1469	6.0	3168	0.1441	0.7139	0.8745	0.7861	0.9535
0.1399	7.0	3696	0.1453	0.7175	0.8838	0.7920	0.9524
0.1333	8.0	4224	0.1403	0.7298	0.8838	0.7995	0.9547
0.1273	9.0	4752	0.1368	0.7387	0.8722	0.7999	0.9563
0.1246	10.0	5280	0.1342	0.7426	0.8768	0.8042	0.9569
0.1195	11.0	5808	0.1351	0.7359	0.8791	0.8012	0.9571
0.1172	12.0	6336	0.1349	0.7373	0.8791	0.8020	0.9573
0.1155	13.0	6864	0.1296	0.7441	0.8768	0.8050	0.9581
0.1118	14.0	7392	0.1302	0.7367	0.8698	0.7978	0.9577
0.1111	15.0	7920	0.1322	0.7426	0.8768	0.8042	0.9577
0.1097	16.0	8448	0.1303	0.7353	0.8698	0.7969	0.9577
0.1094	17.0	8976	0.1306	0.7343	0.8722	0.7973	0.9573
0.1077	18.0	9504	0.1319	0.7372	0.8722	0.7990	0.9577
0.1065	19.0	10032	0.1296	0.7376	0.8675	0.7973	0.9577
0.1078	20.0	10560	0.1301	0.7357	0.8652	0.7952	0.9577

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2