metadata

language:
  - id
license: mit
base_model: indolem/indobert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: sentiment-pt-pl10-4
    results: []

sentiment-pt-pl10-4

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: 0.2951
Accuracy: 0.8847
Precision: 0.8609
Recall: 0.8609
F1: 0.8609

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
0.5448	1.0	122	0.5047	0.7243	0.6629	0.6524	0.6568
0.4527	2.0	244	0.4320	0.7945	0.7667	0.8121	0.7752
0.3603	3.0	366	0.3370	0.8471	0.8393	0.7768	0.7985
0.3081	4.0	488	0.2995	0.8722	0.8453	0.8471	0.8462
0.2793	5.0	610	0.3008	0.8747	0.8537	0.8388	0.8457
0.2526	6.0	732	0.2987	0.8697	0.8449	0.8378	0.8412
0.2478	7.0	854	0.3030	0.8772	0.8609	0.8356	0.8467
0.2337	8.0	976	0.2974	0.8672	0.8463	0.8260	0.8351
0.217	9.0	1098	0.2774	0.8722	0.8562	0.8271	0.8395
0.1966	10.0	1220	0.2846	0.8697	0.8411	0.8478	0.8443
0.199	11.0	1342	0.2910	0.8822	0.8639	0.8467	0.8545
0.187	12.0	1464	0.2871	0.8772	0.8609	0.8356	0.8467
0.1812	13.0	1586	0.2813	0.8797	0.8585	0.8474	0.8526
0.1633	14.0	1708	0.2957	0.8822	0.8555	0.8642	0.8596
0.1607	15.0	1830	0.2875	0.8922	0.8706	0.8687	0.8697
0.1584	16.0	1952	0.2859	0.8822	0.8610	0.8517	0.8561
0.1535	17.0	2074	0.2924	0.8847	0.8609	0.8609	0.8609
0.1432	18.0	2196	0.2966	0.8847	0.8599	0.8634	0.8616
0.1466	19.0	2318	0.2947	0.8822	0.8596	0.8542	0.8568
0.1411	20.0	2440	0.2951	0.8847	0.8609	0.8609	0.8609

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2