metadata

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

sentiment-base-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: 0.7536
Accuracy: 0.9048
Precision: 0.8798
Recall: 0.8976
F1: 0.8878

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: 1
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
0.4355	1.0	122	0.3243	0.8697	0.8538	0.8228	0.8359
0.2295	2.0	244	0.3047	0.8897	0.8625	0.8795	0.8701
0.1337	3.0	366	0.3747	0.8997	0.8778	0.8816	0.8797
0.1038	4.0	488	0.4188	0.8822	0.8518	0.8867	0.8651
0.072	5.0	610	0.6271	0.8872	0.8672	0.8577	0.8622
0.0462	6.0	732	0.6129	0.8897	0.8632	0.8770	0.8695
0.0459	7.0	854	0.5891	0.8897	0.8710	0.8595	0.8649
0.0391	8.0	976	0.5973	0.8872	0.8587	0.8802	0.8681
0.0307	9.0	1098	0.7087	0.8747	0.8441	0.8863	0.8585
0.0199	10.0	1220	0.7264	0.8972	0.8869	0.8598	0.8717
0.0105	11.0	1342	0.6738	0.8972	0.8767	0.8748	0.8757
0.0131	12.0	1464	0.7488	0.8997	0.8733	0.8941	0.8825
0.0102	13.0	1586	0.7155	0.8972	0.8708	0.8898	0.8793
0.0061	14.0	1708	0.7196	0.9073	0.8851	0.8944	0.8895
0.0138	15.0	1830	0.7618	0.9023	0.8773	0.8933	0.8846
0.0075	16.0	1952	0.7253	0.9048	0.8806	0.8951	0.8873
0.0063	17.0	2074	0.7560	0.9023	0.8782	0.8908	0.8841
0.0066	18.0	2196	0.7483	0.9023	0.8758	0.8983	0.8857
0.0023	19.0	2318	0.7535	0.9023	0.8773	0.8933	0.8846
0.0021	20.0	2440	0.7536	0.9048	0.8798	0.8976	0.8878

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2