IndoBERT-Sentiment-Analysis3

This model is a fine-tuned version of indobenchmark/indobert-base-p1 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.3766
• Accuracy: 0.9513
• F1 Score: 0.9512

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: 6
• eval_batch_size: 6
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score
0.2097	0.0960	50	0.7501	0.8744	0.8728
0.175	0.1919	100	0.3987	0.9179	0.9177
0.1202	0.2879	150	0.4578	0.9103	0.9099
0.1208	0.3839	200	0.8259	0.8538	0.8511
0.1877	0.4798	250	0.5322	0.9051	0.9047
0.1831	0.5758	300	0.5864	0.8897	0.8888
0.0811	0.6718	350	0.4034	0.9385	0.9384
0.1689	0.7678	400	0.4355	0.9256	0.9254
0.097	0.8637	450	0.5130	0.9205	0.9202
0.1433	0.9597	500	0.3372	0.9385	0.9384
0.1522	1.0557	550	0.3154	0.9462	0.9461
0.0114	1.1516	600	0.3916	0.9385	0.9383
0.0362	1.2476	650	0.5182	0.9256	0.9254
0.0224	1.3436	700	0.3558	0.9487	0.9487
0.0157	1.4395	750	0.4156	0.9436	0.9435
0.013	1.5355	800	0.6271	0.9179	0.9176
0.0285	1.6315	850	0.3944	0.9462	0.9461
0.0315	1.7274	900	0.5559	0.9256	0.9254
0.0542	1.8234	950	0.3738	0.9462	0.9461
0.1277	1.9194	1000	0.5316	0.9282	0.9280
0.1	2.0154	1050	0.4853	0.9282	0.9280
0.0207	2.1113	1100	0.3835	0.9462	0.9461
0.0227	2.2073	1150	0.3942	0.9487	0.9487
0.0004	2.3033	1200	0.3754	0.9538	0.9538
0.0293	2.3992	1250	0.3260	0.9538	0.9538
0.0001	2.4952	1300	0.3674	0.9538	0.9538
0.0001	2.5912	1350	0.3815	0.9513	0.9512
0.0001	2.6871	1400	0.3905	0.9513	0.9512
0.0213	2.7831	1450	0.3840	0.9513	0.9512
0.0002	2.8791	1500	0.3711	0.9513	0.9512
0.0246	2.9750	1550	0.3765	0.9513	0.9512

Framework versions

• Transformers 4.53.2
• Pytorch 2.6.0+cu124
• Datasets 2.14.4
• Tokenizers 0.21.2