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-pl10-2
    results: []

sentiment-pt-pl10-2

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: 0.3248
Accuracy: 0.8872
Precision: 0.8672
Recall: 0.8577
F1: 0.8622

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
0.5533	1.0	122	0.5134	0.7268	0.6606	0.6242	0.6327
0.4779	2.0	244	0.4950	0.7419	0.7054	0.7349	0.7122
0.4097	3.0	366	0.3772	0.8246	0.8093	0.7459	0.7665
0.3451	4.0	488	0.3511	0.8446	0.8105	0.8251	0.8170
0.2959	5.0	610	0.3201	0.8546	0.8239	0.8272	0.8255
0.2727	6.0	732	0.3176	0.8647	0.8325	0.8642	0.8447
0.2595	7.0	854	0.2959	0.8747	0.8451	0.8613	0.8524
0.2409	8.0	976	0.2833	0.8897	0.8710	0.8595	0.8649
0.2298	9.0	1098	0.2894	0.8772	0.8535	0.8481	0.8507
0.2221	10.0	1220	0.2884	0.8872	0.8687	0.8552	0.8615
0.1986	11.0	1342	0.2855	0.8847	0.8648	0.8534	0.8588
0.1964	12.0	1464	0.2921	0.8822	0.8694	0.8392	0.8521
0.1783	13.0	1586	0.3104	0.8897	0.8710	0.8595	0.8649
0.1788	14.0	1708	0.3015	0.8897	0.8640	0.8745	0.8689
0.172	15.0	1830	0.3012	0.8847	0.8634	0.8559	0.8595
0.1563	16.0	1952	0.3159	0.8897	0.8632	0.8770	0.8695
0.1512	17.0	2074	0.3249	0.8847	0.8679	0.8484	0.8573
0.151	18.0	2196	0.3245	0.8822	0.8624	0.8492	0.8553
0.1461	19.0	2318	0.3282	0.8872	0.8687	0.8552	0.8615
0.1555	20.0	2440	0.3248	0.8872	0.8672	0.8577	0.8622

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2