metadata

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

sentiment-unipelt

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: 0.2928
Accuracy: 0.9023
Precision: 0.8842
Recall: 0.8783
F1: 0.8812

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
0.5535	1.0	122	0.4992	0.7293	0.6646	0.6285	0.6373
0.444	2.0	244	0.4053	0.8170	0.7847	0.8256	0.7961
0.3464	3.0	366	0.3425	0.8421	0.8345	0.7683	0.7905
0.2852	4.0	488	0.3136	0.8722	0.8445	0.8496	0.8470
0.2608	5.0	610	0.3060	0.8722	0.8445	0.8496	0.8470
0.2415	6.0	732	0.3100	0.8647	0.8325	0.8642	0.8447
0.2329	7.0	854	0.2860	0.8847	0.8567	0.8734	0.8642
0.199	8.0	976	0.2879	0.8872	0.8672	0.8577	0.8622
0.1939	9.0	1098	0.2826	0.8897	0.8659	0.8695	0.8676
0.1806	10.0	1220	0.2982	0.8797	0.8795	0.8224	0.8439
0.1674	11.0	1342	0.2735	0.8947	0.8730	0.8730	0.8730
0.1553	12.0	1464	0.2753	0.8947	0.8757	0.8680	0.8717
0.1431	13.0	1586	0.2937	0.8922	0.8785	0.8562	0.8662
0.1417	14.0	1708	0.2911	0.9073	0.8823	0.9019	0.8910
0.1236	15.0	1830	0.2956	0.9023	0.8828	0.8808	0.8818
0.1304	16.0	1952	0.3011	0.9023	0.8773	0.8933	0.8846
0.1164	17.0	2074	0.2943	0.8997	0.8778	0.8816	0.8797
0.1144	18.0	2196	0.2937	0.8972	0.8732	0.8823	0.8776
0.1198	19.0	2318	0.2985	0.8972	0.8812	0.8673	0.8738
0.1104	20.0	2440	0.2928	0.9023	0.8842	0.8783	0.8812

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2