sentiment-lora-r8a2d0.1-1

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: 0.3035
• Accuracy: 0.8747
• Precision: 0.8523
• Recall: 0.8413
• F1: 0.8465

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: 30
• eval_batch_size: 8
• 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	Accuracy	Precision	Recall	F1
0.5655	1.0	122	0.5179	0.7243	0.6623	0.6499	0.6548
0.5048	2.0	244	0.4926	0.7519	0.7079	0.7270	0.7147
0.4529	3.0	366	0.4301	0.7995	0.7581	0.7606	0.7593
0.393	4.0	488	0.3863	0.8221	0.7871	0.7766	0.7814
0.3754	5.0	610	0.3868	0.8246	0.7892	0.8209	0.8003
0.3455	6.0	732	0.3605	0.8446	0.8126	0.8126	0.8126
0.3344	7.0	854	0.3396	0.8546	0.8263	0.8196	0.8229
0.3157	8.0	976	0.3319	0.8672	0.8436	0.8310	0.8369
0.3076	9.0	1098	0.3273	0.8546	0.8284	0.8146	0.8210
0.2948	10.0	1220	0.3238	0.8747	0.8552	0.8363	0.8448
0.2737	11.0	1342	0.3199	0.8697	0.8474	0.8328	0.8395
0.2741	12.0	1464	0.3190	0.8596	0.8299	0.8332	0.8315
0.275	13.0	1586	0.3146	0.8772	0.8628	0.8331	0.8458
0.2736	14.0	1708	0.3104	0.8697	0.8460	0.8353	0.8404
0.263	15.0	1830	0.3112	0.8672	0.8393	0.8410	0.8402
0.2583	16.0	1952	0.3086	0.8722	0.8453	0.8471	0.8462
0.2544	17.0	2074	0.3065	0.8722	0.8512	0.8346	0.8422
0.2594	18.0	2196	0.3056	0.8697	0.8449	0.8378	0.8412
0.256	19.0	2318	0.3043	0.8722	0.8512	0.8346	0.8422
0.2515	20.0	2440	0.3035	0.8747	0.8523	0.8413	0.8465

Framework versions

• Transformers 4.39.3
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.15.2