absa_model – AI Model by Dhanang | AlphaNeural AI

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Dhanang/
absa_model

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text-classification

generated_from_trainer

indobenchmark/indobert-base-p2

autotrain_compatible

endpoints_compatible

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absa_model

This model is a fine-tuned version of indobenchmark/indobert-base-p2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6475
Accuracy: 0.8392
F1: 0.8409
Precision: 0.8444
Recall: 0.8431

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	72	0.6475	0.8392	0.8409	0.8444	0.8431
No log	2.0	144	0.8639	0.8252	0.8156	0.8261	0.8177
No log	3.0	216	0.9170	0.7832	0.7800	0.8112	0.7676
No log	4.0	288	0.8206	0.8322	0.8359	0.8346	0.8405
No log	5.0	360	0.8318	0.8392	0.8417	0.8434	0.8404
No log	6.0	432	0.9578	0.8252	0.8255	0.8243	0.8325
0.0684	7.0	504	0.9713	0.8112	0.8027	0.8143	0.7967
0.0684	8.0	576	0.9850	0.8252	0.8137	0.8236	0.8089
0.0684	9.0	648	0.9955	0.8392	0.8258	0.8347	0.8203
0.0684	10.0	720	0.9964	0.8392	0.8258	0.8347	0.8203

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0