metadata

language:
  - id
license: mit
base_model: indolem/indobert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: sentiment-lora-r8a2d0.15-0
    results: []

sentiment-lora-r8a2d0.15-0

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: 0.3217
Accuracy: 0.8622
Precision: 0.8326
Recall: 0.8375
F1: 0.8349

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
0.5593	1.0	122	0.5026	0.7268	0.6658	0.6542	0.6589
0.4995	2.0	244	0.4797	0.7544	0.7149	0.7412	0.7226
0.4612	3.0	366	0.4282	0.7644	0.7199	0.7358	0.7262
0.4019	4.0	488	0.3934	0.8296	0.7949	0.7919	0.7934
0.3665	5.0	610	0.4234	0.7970	0.7618	0.7964	0.7720
0.334	6.0	732	0.3723	0.8195	0.7817	0.7973	0.7884
0.3263	7.0	854	0.3704	0.8346	0.7990	0.8230	0.8086
0.3076	8.0	976	0.3521	0.8471	0.8153	0.8168	0.8160
0.298	9.0	1098	0.3522	0.8471	0.8138	0.8243	0.8187
0.2923	10.0	1220	0.3375	0.8571	0.8289	0.8239	0.8264
0.2689	11.0	1342	0.3392	0.8622	0.8319	0.8400	0.8357
0.2686	12.0	1464	0.3484	0.8622	0.8309	0.8450	0.8373
0.2726	13.0	1586	0.3258	0.8596	0.8316	0.8282	0.8298
0.2713	14.0	1708	0.3246	0.8622	0.8333	0.8350	0.8341
0.2577	15.0	1830	0.3307	0.8596	0.8293	0.8357	0.8324
0.2519	16.0	1952	0.3305	0.8622	0.8314	0.8425	0.8365
0.2488	17.0	2074	0.3234	0.8546	0.8246	0.8246	0.8246
0.2546	18.0	2196	0.3247	0.8647	0.8346	0.8442	0.8391
0.2463	19.0	2318	0.3204	0.8596	0.8307	0.8307	0.8307
0.2458	20.0	2440	0.3217	0.8622	0.8326	0.8375	0.8349

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2